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TRL Craveworthy
by Larry Carroll
As everybody knows, those boots are made for walking — and Jessica Simpson has made some big strides in them over the last few years. Now, as she steps into the shoes (and shorts) of Daisy Duke for next month's big-screen version of "The Dukes of Hazzard," the blonde with the penchant for headline-making malapropisms is ratcheting up a cross-media career that has already made her one of the most recognizable faces in entertainment.
Jessica's patriotic spread in
Jessica in
Us Weekly
She may have made her name playing dumb, but there's reason to suspect significant smarts behind Simpson's every move.
"If I'm going to have a movie career," the Texas-born actress has said, "I don't want it to be the little 'Here today, gone tomorrow' movie career. I want to be like a Reese Witherspoon, Meg Ryan or Julia Roberts — I want to make it last."
Could Simpson really have a genuine
movie star
future ahead of her, perhaps even along the lines of the "Erin Brockovich" Oscar winner? Don't laugh yourself silly quite yet. If one commonality has marked her fame thus far, it's that Simpson has been constantly underestimated.
After years of honing her pipes in the local church choir, the 12-year-old future starlet auditioned for "The Mickey Mouse Club" — only to be rejected. Soon after, her grandmother helped fund a small pressing of Simpson's debut album, which grabbed the attention of the Christian Youth Conference tour circuit. The young singer built up a loyal fanbase, but few expected her to expand it beyond the religious market.
"These Boots Are Made for Walkin' "
The Dukes of Hazzard [Original Soundtrack]
After signing with Sony at the end of the '90s, Simpson released two albums: 1999's
Sweet Kisses
and 2001's
While the records placed her on the public's radar, the skyrocketing careers of Simpson's contemporaries left her struggling to find an identity in a cluttered landscape that included the (for a time) barely distinguishable Britney Spears, Christina Aguilera and Mandy Moore.
It was around this time that Simpson married Nick Lachey, the charismatic lady-killer from 98 Degrees. Suddenly, the poor man's (or woman's) Britney had found her own unique persona: a sexy housewife lovingly standing behind her man while simultaneously asserting her own independence via the mic.
Then, in the summer of 2003, the reality show "Newlyweds" brought Nick and Jessica into living rooms across America, and Simpson's star rose even further. Men enjoyed watching the domestic goddess perform fascinating and mysterious rituals, like cleaning the house; women, meanwhile, seemed to enjoy a kinship with an unguarded beauty unafraid to put her shortcomings on display.
"I foresee big movies in the future," Simpson remarked at the time. Sure enough, Hollywood was listening.
Set Visit: "The Dukes of Hazzard"
Simpson, Knoxville, Seann William Scott on the set of the movie
As Simpson publicly pondered the existence of seaborne chickens and crafted her own Homeric inquiry into the flight patterns of the American buffalo, her popularity grew exponentially. Web sites and tabloids alike had her rumored to be up for film roles ranging from a Bond girl to Bruce Willis' love interest in "Die Hard 4."
Appropriately enough, the former "Mickey Mouse Club" reject finally landed her dream movie role of Daisy Duke by beating out many of her contemporaries — including none other than Spears herself.
"We'd always play 'Dukes of Hazzard,' " Simpson recalled of her childhood, discussing the role of Bo and Luke's sexy cousin. "I think it's just being from the South. It's a big deal. I was Daisy; my cousins played Bo and Luke."
Simpson believes that film will be an easier transition for her than most other first-time actresses, because her reality show has "given me so much more than any audition, script, time on camera, whatever it may be," she once said. "I want to be a legitimate movie star, and I think it's all about making the right decision about the roles [early] in your career."
Photos: "The Dukes of Hazzard"
Watch the trailer
If the attention currently being paid to Simpson's sexy video for "These Boots Are Made for Walkin' " is any indication, the budding actress has once again chosen wisely. As she writhes and shakes her way around the General Lee, Simpson is once again reminding the world that she's not going to fade away — that she can sing, that she can dance, and that she now plans on showing folks that she can act.
That she's also easy on the eyes makes her bid for big-screen stardom perhaps a bit less quixotic than it might otherwise seem. But mere good looks, of course, do not guarantee movie stardom. Does Jessica Simpson have a Hollywood A-list future ahead of her?
For a possible answer, we might want to look back to the 1960s, when a befuddled, airheaded blonde captured the nation's attention by dancing in a bikini and asking impossibly stupid questions on that icon of television grooviness, "Rowan & Martin's Laugh-In." A short time after the show went off the air, the young, seemingly ditzy Goldie Hawn snagged herself an Oscar in 1969's "Cactus Flower," and has since gone on to a long and enormously well-regarded career as one of the shrewdest and most consistently successful actresses in Hollywood.
After "Dukes," Simpson will move on to "Room Service," a comedy in which she'll play a disinherited heiress working as a hotel maid to pay off her debts. Sure enough, she recently described the character as a "fun, Goldie Hawn type."
If only we were all so dumb.
Check out everything we've got on
"The Dukes of Hazzard."
Movies on MTV.com
for more from Hollywood, including news, interviews, trailers and more.
E-Mail this story to a friend
What do you think of this feature? You Tell Us...
Photo: MTV News
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text:
The Iron Man-themed Galaxy smartphone is dressed in red and gold, has 64 GB of internal memory, and will be offered in a box containing a matching wireless charging pad. A clear cover that enhances its "exclusive design" is also included. You can check out the handset in the photos and video embedded below.
According to Samsung, the Galaxy S6 edge Iron Man Limited Edition will be launched on May 27 in South Korea, and next month in China and Hong Kong. The company didn't mention other markets, which might leave many Iron Man fans disappointed. The price of the S6 edge Iron Man Limited Edition wasn't unveiled, but it's probably higher than the price of a regular S6 edge 64 GB.
Samsung said nothing about an Iron Man edition of the Galaxy S6, which might mean that the curved S6 edge is the only one that's getting the superhero treatment.
source: Samsung

subset: commoncrawl


text:
Download Nomad7 apk free for Android smartphone
18 Jul
Posted by Spinning World Apps in Business | July 18, 2013 | 0 Comments
Apk file size: 0 bytes
Nomad7 is the daily discipleship app from Nomad Community Church in Melbourne, FL. We believe in short daily reminders of why we choose to follow Christ. Each day features different content that helps enhance the message given on Sundays. Walk with us...not all who wander are lost!
Spinning World Apps part of our Business and have average installs from 10 to 50. Last Update July 18, 2013. Google play rating is 40.0. Current verison is None. Actual size 0 bytes.


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Four charged amid grand final party melee
Four people have been arrested after an alcohol-fuelled AFL grand final party in Karratha at the weekend.
Police say on Saturday evening they attended a house on Hutton Court in Nickol to find more than 20 people fighting on the street.
A 17-year-old boy and a 43-year-old man from Nickol, a 22-year-old woman from Baynton and another 43-year-old man from Mullewa were all charged with disorderly conduct.
They are all due to appear in court later this month.
Police say despite the arrests, residents were generally well behaved over the long weekend.
Meanwhile, police are investigating an incident in South Hedland that left a teenager with serious injuries.
It is believed in the early hours of Saturday morning an 18-year-old man was joy riding on the bumper of his friend's Jeep when he fell off and was run over.
After being admitted to the local hospital, he was flown to Perth with serious leg injuries.
Topics: antisocial-behaviour, crime, road, karratha-6714


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Gayesha Perera is a Sri Lankan actress and model who works in Sri Lankan and International films. She became a controversial character after acted in nude scenes of the film Flying Fish. Then she tried to enter politics too. Gayesha Perera's got married last day. Groom is famous singer Hasanjith Chathuranga Kuruppuarachchi.
Gayesha's witness to the marriage is the Hambantota district parliamentarian Namal Rajapkasa, while the President Mahinda Rajapaksa will be Hasanjith's witness.
Last Poson full moon day they held homecomming party. participated lot of artist and Gayesha's forging friends


text:
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 Anakin Skywalker, Jedi Knight and hero of the Republic, is challenged by a fledgling hero of the Confederacy...

 The Confederacy is being provided with newer and more advanced ships from Valahari - a world whose royal family has claimed neutrality in the Clone Wars. Anakin Skywalker and Obi-Wan Kenobi are dispatched to negotiate for the Republic; but as events unfold and
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Home Living : March 8th 2011
HLN - HOME LIVING: NORTH, Tuesday, March 8, 2011 3 80 exhibits at Homeshow » ADVERTISING FEATUREHOMELIVING In light of the wonderful success of the first two Hills Homeshow and Lifestyle Expos, visitors can expect this year's event to be even bigger and better. Held from Saturday, March 26 to Sunday, March 27, from 10am to 4pm at the Bernie Mullane Sports Complex at Kellyville, the Hills Home Show and Lifestyle Expo is expected to draw large crowds. With more than 80 exhibitors, the show has grown substantially since its inception in March 2009. Complete Exhibitions director and organiser of the show Norm Lembke said there would be prizes offered by many exhibitors, including a beautiful original painting by local artist Victoria Lane as a door prize. ''With ongoing building and renovating activity continuing at a fast pace in the Hills area, visitors will want to view the latest in products, services and technologies for building, renovating, maintenance, decorating, design and lifestyle for the home,'' Mr Lembke said. Seminars will run during the show with popular topics such as decorating and design, colour co-ordination, raw food cooking, 3D home design, investing, and travel tips. On Saturday from 1pm to 1.30pm, a special guest spot by Andrew Morello, from financial advisers Yellow Brick Road, will help answer homeshow enthusiasts' questions. Industry experts from the Master Painters Association, Building Designers Association of NSW and Hills Shire Council will be on hand throughout the weekend to answer any questions. Sponsored by the Hills Shire Times and Rouse Hill Times, homeshow entry and parking is free. For details,phone 0416 147 477, email email@example.com, or visit hillshomeshow.com.au SHUTTER SPECIALISTS PH: 9868 5373 ONLY hln BEAT ANY GENUINE QUOTE ALL SHUTTER REPAIRS SLIDING • BI-FOLD • HINGED • FIXED PLANTATION SHUTTERS Plantation Shutters www.sarmax.com.au email: firstname.lastname@example.com Just a few reasons to call Sarmax NOW for your free quote Privacy Noise reduction Ventilation Light control Insulation Low maintenance Cedar or aluminium Basswood Custom made High quality Local family business Save $ - buy direct from the importer Your team that thinks outside the square Call us today 0428 178 997 2039454im 17/1 Wk34 &ACTORY DIRECT TO YOU .O MIDDLEMEN INVOLVED COMPARE OUR PRICES! PLANTATION SHUTTERS GLOBAL SHUTTERS & BLINDS PTY LTD TEL: 1300 128 230 www.gloshutters.com "!337//$ M *plus installation charge, price quoted for a minimum order of 2m2 *Discounts off R.R.P. on selected ranges only. Offer supercedes previous offers. X33630 SYDNEY BLINDS & SCREENS 9636 1555 Showroom: 26 Stoddart Road, Prospect. (Mon--Fri & Sun) www.sydneyblinds.com.au prices ever seen! FREE quotes ALL suburbs •VERTICALS •VENETIANS •ROMANS •ROLLERS •TIMBERS •PLEATEDS •SUNSCREENS •PANELS •SHEER ELEGANCE 60% off* Buy 6 blinds or more & get THE MORE YOU BUY, THE MORE YOU SAVE! BUY 2 BLINDS GET 20% OFF * BUY 3 BLINDS GET 30% OFF* BUY 4 BLINDS GET 40% OFF* BUY 5 BLINDS GET 50% OFF* SUMMER STOCK SELLOUT NEW SHOWROOM NOW OPEN opposite Greengate Hotel Killara Phone 9880 2885 Display 684 Pacific Hwy, Killara 2071 | www.totalbathrooms.com.au 2272289k wk36 Offer ends 08/04/11 www.uniquedaylighting.com.au 210/354 Eastern Valley Way, Chatswood (near Fitness First) Unique Daylighting Solatube® Premier Dealer 9417 7066 S1957AW
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We present to your attention this pretty two-storey house built in 1965, in a small village, close to the Greek border, and 5 km away from the municipality town of Ivailovgrad. The property provides 90 sq.m. of living area and a yard of 300 sq. m. The ground floor consists of 3 rooms, based on concrete flooring; the walls are made of stone and the window frames are wooden. The first floor provides 3 rooms based on wooden flooring. The walls are brick-built, and the window frames are wooden. Telephone land line can be easilly connected. There is no bathroom/wc, which have to be arranged inside.
There are main drains in the village to which the house can be linked; the house is connected to running water and electricity
The road to the property is asphalt, as the house is in the midle of the village, supplied with all necessary amenities like shop, cafe, bus stop, etc. The property is structurally sound and in good condition; it needs only slight refreshment to become your perfect holiday house. The area where this lovely house is located is very peaceful and quiet. The nature is very well preserved as there is no industrialization nearby. The climate is mild, the village is surrounded by beautiful hills. The region is popular with the nearby historic sights; the remains of the Medieval stronghold Lutica, and the excellently preserved villa from Roman times (II c. AD), called Armira. Do not miss your chance to experience rural life in sunny Bulgaria!
Contact us for more details. Reference No: 10333.


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Industrial Metal Supply has a wide variety of perforated sheet patterns. There are several uses for the patterns whether at home or on a job. Round and square shapes are the most requested, but the company also has square slot and rectangle shapes to choose from. The shapes come in various sizes depending on the need for the metal. International Metal Supply can also create ornamental patterns for special projects.
Perforated metal sheets are often used in automobiles and on some pieces of furniture. The metal can be cut after it is purchased in order to fit the area where you are working. It can be nailed if in small amounts, but it is normally welded on the area. The metal can also be clamped on items for easy removal later on if you choose to remove the metal. One of the common uses is in automobiles to make fuel filters and speakers. Medical and food trays in schools and other cafeterias are often made of perforated sheet patterns. When using the metal in construction purposes, it is used in stucco walls to provide structure and stability. This is a product that is used in situations where you need extra support that you can't get from plastic or wood.


text:
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The SPRING PROGRAM was a great success! The keynote address by Steven Bell provided context and an overview of the meaningof success for today's students and how the library can be effectual in that effort. Excellent breakout sessions by TCLC members brought new ideas for fostering success. Thank you, everyone!
Watch for information on the SPRING TOUR to Baltimore, soon!
Ann Upton
TCLC Coordinator
Announcing the TCLC Unconference 2014, focusing on Marketing and Outreach.
Come and share your experiences, ask questions, learn from your TCLC colleagues.
Keep October 23, 2014 open for this event, to be held at Saint Charles Borromeo Seminary in Wynnewood, PA (click here for directions ).
It's a bargain, too! One person from a TCLC library is $15, two from the same library is only $25.
See the attached flyer for the details.
TCLC is offering a grants writing workshop in August – just before the new semester begins. Learn the proper way to write successful grants, and get funding for a project for your library. Learn to write clear and concise plans, purposes, goals, and outcomes, and winning phrases for a successful grant.
Grants: The Write Stuff
Wednesday, August 13, 2014
9:30 AM Registration and Continental Breakfast
10:00 AM – Noon – Program
Rosemont College Library
Cost: $30 TCLC members; $50 non-members
See the attached flyer for more details and registration information. Still have questions? Contact Ellen Gasiewski at the TCLC office : Phone: 610-525-0796 or Email: email@example.com
Click on our Facebook link below to view photos of our recent bus trip to Mount Vernon on June 11, 2014.
Here is a link to the PowerPoint slides from Ben Levin's presentation on April 25th at the Spring Program.
by Ben Levin
Founder, Principal
web: www.chamjari.com
twitter: ChamjariInc, BenJLevi
For anyone who missed our 2014 Spring program on April 25th, here is a link to Craig M. MacDonald's presentation on the topic of user experience.


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text:
The Sunday lunchtime crowd at the Moaning Cow public house were staggered at the power of Mr. Angry's rhetoric. The applause rang out as he attacked the incompetence of the Coalition Government's failure to help SMEs.
"They called it 'The Red Tape Challenge'" he cried "but have they reduced the mountain of regulations?" He sank another pint of strong lager. "No, my friends, they have not."
He was now warming to his theme.
"They pledged to cut or improve 85% of health and safety rules. 300 organisations have made representations. What has changed? Nothing, my faithful admirers. Nothing!"
No-one could now stop the great orator.
"They promised to 'light a bonfire' under poor regulations. Yet my friends at the CBI.."
"Who Mr. Angry?" asked the man at the fruit machine.
"The..er…Conference for…er…Budding….er…Investors."
"The Confederation of British Industry, you berk" shouted Mrs. Angry's best friend.
"Them as well." He ordered another pint of lager. "The CBI says new directives will cost £178m a year. It's a scandal my friends." He drank half his pint. "What is more they said they'd sort out the increasing number of personal injury claims which is costing business millions."
"What about the Enterprise and Regulatory Reform Bill due in April Mr. Angry? It'll help reduce these claims" asked a newcomer to these events. "And what about the reduced number of Health and Safety visits?"
"I haven't time for nip picking" shouted Mr. Angry.
"Mr. Angry" shouted Rita the bar maid. "The Benefits Office has just phoned. If you can sign form 3427C/X where indicated, have your passport certified, supply two recent utility bills, countersign the QE/398/2013 declaration, confirm your Mother's maiden name and be there at 9.00am tomorrow, they'll let you have your holiday travel vouchers."
"Tell them I'll be there" he cried.


text:
|Publication number
|Publication type
|Application number
|Publication date
|Apr 17, 2012
|Filing date
|May 30, 2008
|Priority date
|Dec 21, 2007
|Fee status
|Also published as
|Publication number
|Meir Tzur, Victor Pinto
|Original Assignee
|Csr Technology Inc.
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (56), Non-Patent Citations (26), Referenced by (12), Classifications (6), Legal Events (7)
|External Links: USPTO, USPTO Assignment, Espacenet
This application claims the benefit of U.S. Provisional Application No. 61/016,199 filed Dec. 21, 2007, herein incorporated by reference in its entirety.
The present invention is directed to methods, apparatuses, and systems for object recognition. The present invention is also directed to methods, apparatuses, and systems for object recognition by comparing features of an object, such as a face, with identified subsets of distinctive features for the objects in a recognition database.
Digital cameras have become ubiquitous and can be used to take both still images and video images. Object recognition, and, in particular, face recognition, is a desirable feature for digital cameras. It can also be desirable to provide object recognition, and, in particular, face recognition, with other devices, such as computers, personal data assistants (PDAs), mobile telephones, and the like. In some instances, these devices may include a digital camera. In other instances, the devices may receive a still or video image and perform object recognition on the image.
Face recognition is also closely associated with face detection, in which a generic face is sought in an image. Face recognition goes a step further, seeking to classify a face detected in an image as a particular face from a database of previously identified faces desired to be found.
In many conventional methods, faces are recognized by determining the Euclidean distance between the features of an input face image and the features of a set of reference face images (e.g., a gallery or library of face images.) The identity of the input face image is determined to be the reference face which is least distant and is below a minimum threshold of detection. Other mathematical methods, such as principal component analysis (PCA) or linear discriminant analysis (LCA) have been used to reduce the computational load of the analysis and to improve the robustness of recognition.
Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified.
For a better understanding of the present invention, reference will be made to the following Detailed Description, which is to be read in association with the accompanying drawings, wherein:
The present invention now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific exemplary embodiments by which the invention may be practiced. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Among other things, the present invention may be embodied as methods or devices. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense.
Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase "in one embodiment" as used herein does not necessarily refer to the same embodiment, though it may. Furthermore, the phrase "in another embodiment" as used herein does not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention.
In addition, as used herein, the term "or" is an inclusive "or" operator, and is equivalent to the term "and/or," unless the context clearly dictates otherwise. The term "based on" is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on."
Briefly stated, the present invention is directed toward object recognition including, for example, face recognition. A recognition system is trained to recognize a gallery of one or more identified objects (e.g., faces) which form a recognition database. To train the system, several images of an identified object are provided and the system determines one or more characteristic values (e.g., the mean value and variance) for each of multiple features of the object. Based on these characteristic values, a subset of distinctive features is selected for the object from a set of available features and this subset is stored in a recognition database. Different objects may have different subsets of distinctive features. The distinctive features for each object can be, for example, features that are substantially distinctive (e.g., are the greatest distance) from a set of generic features determined using a set of reference objects. For example, a reference library of faces can be used to determine a set of generic features and, in at least some instances, statistical values, such as a mean value and a variance, for the generic features. Distinctive features for a particular face could include those features that exceed a threshold level of distinction over the generic features determined from the reference library. Accordingly, each object or face in the recognition database is represented by a subset of distinctive features that are particularly distinguishing for that object.
To recognize an input object, the features of the input object are compared to the subset of distinctive features for each (or at least for some) of one or more objects in the recognition database. A comparison metric is calculated and if the comparison metric is exceeds a threshold value then it is determined that the input object corresponds to that object in the recognition database. An advantage of these recognition systems and methods over conventional systems and methods is that particularly distinguishing features are identified for each object in the recognition database and these features are compared to the input object. This reduces the calculation time and complexity with respect to conventional systems and methods which consider all of the available features.
Object Recognition
For purposes of illustration, the discussion below will use face recognition as an illustrative example. The terms "face" and "object" may be used interchangeably in the discussion. It will be recognized, however, that the methods, systems, and devices discussed below can be used to recognize objects other than faces and that the application of these methods, systems, and devices to those other objects will be readily understood. Examples of other objects include, but are not limited to, bodies, patterns, pets, cars, eye iris, finger prints, and the like.
The processor 102 may be located in the same device as one, or either, of the reference database 104 and the recognition database 108. Alternatively, one, or both, of the reference database 104 and recognition database 108 may be remote to the processor 102.
The reference database 104 can be, for example, a database of faces. Alternatively or additionally, the reference database can be a database of characteristic values (e.g., mean and variance) for a set of features, where such characteristic values are generated from a reference set of faces. The reference database 104 can, in at least some embodiments, include both the faces and the characteristic values of the features.
The faces in the reference database, or the reference set of faces used to generate the values in the reference database, can be a selection of faces representing variation in human facial features. Alternatively, the faces may be selected based on one or more characteristics such as, for example, gender, age (e.g., child or adult; child, teen, adult, or elderly), race, demographics (for example, a selection of faces for a reference database used in Japan might be different then the selection of faces for the United States), and the like. Other selections of faces for the reference database can also be made; for example, the selection of faces may be made by, for example, a device user or a device manufacturer or third party software supplier.
Characteristic values can be determined for a set of features from the faces in the reference database or from the reference set of faces used to generate the reference database. Examples of suitable features for face recognition include, but are not limited to, the known Haar or Haar-like features. (See, for example, P. Viola and M. J. Jones, "Robust Real-time Face Detection", International Journal of Computer Vision 57(2), 137-154 (2004) and C. Papageorgiou, M. Oren and T. Poggio, "A General Framework for Object Detection", Proceedings of the 6th International Conference on Computer Vision, 555-562 (1998), both of which are incorporated herein by reference.) Such features may include, but are not limited to, the shape, size, relative dimensions (e.g., length versus width of a rectangle or major axis versus minor axis of an ellipse), or area of facial features; or comparisons of shape, size, relative dimensions, or area between facial features and the like. Any set of features can be used.
The set of features can include any number of features. Preferably, the set of features includes many different features. For example, the set of features may include at least 10, 20, 50, 100, 200, 500, 1000, 10000, or more features. The set of features may be selected from among a larger group of possible features for consideration.
The set of features can be identified by one or more characteristic values that can used to determine the subset of distinctive features for each object in the recognition database. Such characteristic values can be, for example, statistical values determined from the faces in the reference database or the reference set of faces used to generate the reference database. Such statistical values can include the mean, variance, median, higher order statistics, and the like of the faces. In at least one embodiment, the mean μj and variance σ2 j of each feature (j=1, . . . , n; where n is the number of features in the set of features) can be determined according to the following equations:
where N is the number of faces in the reference database or the reference set of faces from which the reference database is generated. In other embodiments, higher order statistics may be used in addition to, or instead of, the mean and variance.
The system 100 can be trained to recognize a face. For each face to be added to the recognition database, multiple training images 106 of the identified face are provided to the processor 102 which then determines a subset of distinctive features, selected from the set of features, that will be recorded in the recognition database 108 for that particular face.
For each of multiple features of the object, one or more values, representative of that feature, are calculated using the multiple images of the object (step 204). As an example, the value(s) can be statistical values, for example, the mean value and variance for that feature of the object as determined from the training images. The features for which value(s) are calculated can be any, or all, of the set of features described above. For example, value(s) may be calculated for any, or all, of the set of features catalogued in a reference database and may even include other features. For purposes of illustration of the method, the discussion below uses the mean and variance values for each of the features, but it will be understood that other characteristic values (or the mean or variance alone) could be used in addition to the mean and variance or as an alternative.
Using the value(s) determined for the features from the multiple images, a subset of distinctive features is determined for the object (e.g., face) (step 206) and this subset of distinctive features is provided to the recognition database (step 208). Any method and any criteria can be used to select the distinctive features which are a subset of the features for which the values were calculated in step 204 above. Preferably, the distinctive features include features for which there is substantial statistical variation of that feature of the individual object from the aggregate generic features of the objects in the reference database. In at least some embodiments, the features with the most substantial statistical variation from the features determined for the objects in the reference database are selected. In other embodiments, the distinctive features are not necessarily those with the most variation from the features in the reference database, but still exhibit substantial variation.
Any number of distinctive features can be selected for a particular object. For example, at least 1, 2, 3, 4, 5, 8, 10, 12, 16, 20, 24, 32, 64, 100 or more distinctive features can be selected for the subset of distinctive features for an object. The number of distinctive features for each object in the recognition database can be the same or that number can vary for different objects. Typically, the number of distinctive feature selected is no more than one half, one third, one quarter, one tenth, one twentieth, one fiftieth, or one hundredth, of the features available for selection. Often, different subsets of distinctive features will be selected for objects in the recognition database (although there may be instances where the same subset is selected for two or more objects).
Any criteria for selecting distinctive features that utilizes the calculated value(s) from the training images and corresponding values from the reference database (or determined from the faces in the reference database) can be used. As an example, a distinctive feature for a particular object may have a mean value determined from the training images that is statistically different from the mean value of that feature for the objects in the reference database. As another example, a set of distinctive features might include features of the individual object in the training images that differ by at least one standard deviation (or 1.5 or two or 2.5 standard deviations) from the mean value of that feature from the reference database.
As another example, in at least some embodiments, the Kullback-Leibler divergence is used to select at least some (or all) of the distinctive features. This analysis is designed to measure the distance between two given probability distributions. For two continuous probability distributions p and q, the divergence D(p|q) (a comparison metric) can be defined as:
If D(p|q) is not symmetric, (i.e., D(p|q)≠D(q|p)), a symmetric version may be used instead:
In at least some embodiments, the features in, or calculated from faces in, the reference database are assumed to have a normal (Gaussian) distribution. Therefore, given the mean and variance of the feature, the probability distribution is exactly defined. In other embodiments, a non-Gaussian distribution can be used for one or more (or even all) of the features. The following discussion assumes a Gaussian distribution, but application to a non-Gaussian distribution will be readily understood.
The distribution for the each feature can also be determined for the specific face in the training images. Again, if a normal distribution is assumed, the mean and variance of the feature describe the distribution.
The individual divergence D can be calculated for each feature of interest, assuming p(x) is the distribution of that feature for a general face and q(x) is the distribution of that feature for the particular face in the training images. For the case of normal distribution of p(x) and q(x), D(p|q) depends only on the mean values and variances of the two distributions.
The features can be sorted according to their D value. From a large set of N features, a smaller subset of M distinctive features can be selected by selecting some or all of the features for which D has minimum or relatively low values.
Another embodiment of a method for selecting the distinctive features employs the probability of detection and the probability of false positives for selecting the distinctive features. Again, in at least some embodiments normal distributions are assumed (although in other embodiments other distributions can be used) where p(x) the distribution of the particular feature for a general face and q(x) is the distribution of the particular feature for the face in the training images. In such a case, using a ML (maximum likelihood) criterion, the probability of detection of a particular feature for the particular face in the training images can be calculated as:
where x1 and x2 are determined from the ML equation:
and where μ1, σ1 2 correspond to the general face statistics for that particular feature and μ2, σ2 2 correspond to the face statistics for the face in the training images and for the particular feature.
The probability of false positives (i.e., the probability to decide that a face matches the face in the training images when in fact it does not) can be calculated as:
A comparison metric D for each of the features of interest can be calculated using the following equation:
D=α(1−P d)+(1−α)P fp
where α is a constant between zero and one and can be selected and represents a trade-off between less stringent requirements for recognition of the face and the chance of obtaining a false positive. In at least some embodiments, α is in the range of 0.2 to 0.8 or in the range of 0.3 to 0.7 or in the range of 0.4 to 0.6.
The features can be sorted according to their D value. From a large set of N features, a smaller subset of M distinctive features can be selected by selecting some or all of the features for which D has minimum or relatively low values.
It will be recognized that other criteria and comparison metrics D can be used. For example, cumulative distribution functions calculated for the reference set of faces and the face in the training images can be used to select the distinctive features. Other methods for selecting statistically distinctive values also known and can be used to assist in selecting the distinctive features.
As another example, another comparison metric D for use in selecting distinctive features can be written as:
where L is the number of examples for face in the training image and pi(x) is the value of the probability distribution for a particular feature x and for the generic features from the reference database. Features can be selected as distinctive features if they attain relatively low values of D.
In at least some embodiments, parameters may be determined for each distinctive feature. These parameters can be used to compare the previously identified object from the training images with an unknown object. These parameters may be stored in the recognition database or may be calculated based on information, such as means and variances, stored in the recognition database. As an example, two parameters ai and bi of a parabolic sensitivity function for each distinctive feature i can be determined.
In one embodiment, these parameters are based on the maximum likelihood criterion. The parameters for a distinctive feature i can then be given by the equations:
a i=σ2 2−σ1 2
b i=2μ2σ1 2−2μ1σ2 2
where μ1, σ1 2 correspond to the general face statistics for the distinctive feature and μ2, σ2 2 correspond to the particular face statistics for the distinctive feature.
The recognition database can be used to detect and recognize faces in input images by employing the recognition database.
Any suitable processor 302 can be used and the processor can be the same as the processor 102 in
The processor 302 may be located in the same device as the recognition database 304. Alternatively, the recognition database 304 may be remote to the processor 302.
The recognition database is used for recognition of an object in an input image.
The unknown object is then individually compared to one or more previously identified objects from the recognition database (step 404). One of the objects of the recognition database is selected and, for one or more (and, preferably, each) of the distinctive features of the object of the recognition database, one or more characteristic values representative of that features of the unidentified object can then be determined (step 406). Alternatively, the characteristic values for all, or part, of the possible features may be determined prior to selecting the object from the recognition database.
A comparison metric is calculated comparing all of the distinctive features (or at least a subset of the distinctive features) of the object from the recognition database with those same features of the unidentified object (step 408). Any known method or metric for comparing two subsets of value to determine if they are statistically (or otherwise) similar can be used. After calculating the comparison metric, it can then be determined if the comparison metric exceeds a threshold value indicating that the distinctive features of the object from the recognition database and the corresponding features of the unidentified object are sufficiently similar (step 410). In some instances, "exceeding a threshold value" means a value less than a threshold value and, in other instances, it means a value greater than a threshold value—whichever indicates that the object from the recognition database and the unidentified object are the same. If the comparison metric does exceed the threshold value, then the process may halt. Alternatively, the process can continue to consider other objects in the recognition database even if the comparison metric exceeds the threshold value.
If the comparison metric does not exceed the threshold value, then a new object from the recognition database is selected (steps 412 and 404). The process can be used to compare the unidentified object to all of the objects in the recognition database, or to only a subset of the objects in the recognition database (e.g., a subset of objects selected by a user).
One embodiment of a comparison metric is a distance metric (such as a Euclidian distance) between features of the one or more objects in the recognition database and the same features of the input image. For example, the individual differences in values (or normalized differences in values) for an object in the recognition database and the input image for each of the features can be added to provide a distance metric. If the distance is less than a threshold value, the input image can be recognized as the corresponding object in the recognition database.
Another embodiment of a comparison metric uses the parameters ai and bi described above. A comparison metric Fj can be calculated for the object j in the recognition database using the following equation:
where aj,i and bj,i are the parameters described above for distinctive feature i of object j in the recognition database, Mj is number of distinctive features of object j (or at least the number of distinctive features that will be considered in this calculation), and fi is the value of distinctive feature i of the unidentified object being compared to object j. If Fj exceeds a threshold value T then the unidentified object is correlated to, or identified as, object j. If Fj does not exceed the threshold value T then the unidentified object is not correlated to, or identified as, object j. In at least some embodiments, the threshold value T may be selected to represent an acceptable level of false positives and may be selected manually or calculated algorithmically. One example of an algorithm for calculating T is the following:
where μ1, σ1 2 correspond to the general face statistics for the distinctive feature i of object j and μ2, σ2 2 correspond to the particular face statistics for the distinctive feature i of object j.
In some embodiments, however, if Fj for all objects in the recognition database fails to exceed the threshold value T then the object with the highest value of Fj may be correlated to the unidentified object. In other embodiments, the output 308 from the processor 302 may indicate that the object was not recognized.
Illustrative Camera Device
The camera 500 may be a camera for taking still pictures or motion pictures (e.g., a video camera). The camera 500 may be a stand-alone camera or may be part of a device such as, for example, a mobile phone, PDA, computer, and the like.
As shown in the figure, the camera 500 includes a processing unit (CPU) 522 in communication with a memory 530 via a bus 524. Camera 500 also includes a power supply 526, one or more external interfaces 550, a user interface 552, a display 554, an image receiver 556, optics (e.g., lens(es) and shutter) 558, a flash 560, a light sensor 562, and a motion sensor 564. Power supply 526 provides power to camera 500. A rechargeable or non-rechargeable battery may be used to provide power. The power may also be provided by an external power source, such as an AC adapter.
The optics 558 includes the lens(s), shutter, focusing apparatus, and other optical components that allow the camera to generate an image on the image receiver 556. Any suitable optical arrangement can be used. The image receiver 556 can be any suitable device for receiving the image and generating an electrical representation of the image including, but not limited to, an array of charge-coupled devices (CCD array) or array of CMOS detectors. Preferably, the image receiver generates a digital representation of the image, although it will be understood that analog image receivers can be used and the analog image representation can then be digitized or scanned to generate a digital representation.
The camera 500 may also include a flash 560 to provide additional light when automatically or manually activated. The camera 500 may also include a light sensor 562 to measure the amount of ambient light. The camera 500 may also include a motion sensor 564, such as one or more gyroscopes. The motion sensor 564 may be useful for stabilizing the camera as a picture or video is being taken. The motion sensor may also be useful for later processing, including reducing blurring, of the image.
The camera 500 typically includes a user interface 552 that allows the user to operate the camera and to change various settings of the camera. The user interface 552 may also allow the user to view images (still or video) on the display 554 of the camera, set parameters related to the capture or processing or images, or send or receive images from the camera.
Camera 500 may optionally communicate with another device, such as a computer or mobile telephone, or with a wired or wireless network. External interface 550 can include a port for coupling the camera 500 to another device or may include circuitry for coupling camera 500 to one or more networks. This circuitry, if provided, may be constructed for use with one or more wired or wireless communication protocols.
Display 554 may be a liquid crystal display (LCD), light emitting diode (LED), or any other type of display. Display 554 may also include a touch sensitive screen arranged to receive input from an object such as a stylus or a digit from a human hand.
Memory 530 typically includes a RAM 532 and a ROM 534 and may include other storage devices. Memory 530 may store processor readable instructions, data structures, program modules or other data. Memory 530 further includes one or more permanent data storage 544, which can be utilized by camera 500 to store, among other things, applications 542, images (still or video), and/or other data. It will be understood that a portion of the memory 536 may be removable from the camera. For example, removable storage memory includes diskettes, memory sticks, memory cards, and the like.
Applications 542 may include computer executable instructions which, when executed by camera 500, control operation of the camera, process images, store images, or transmit or receive images. Applications 542 may further include an object recognition application 545 as discussed above. In other embodiments, as discussed above, the object recognition application may be resident on another device.
It will be understood that if the camera is part of another device, such as a mobile phone, PDA, or computer, components of the camera may be shared with other portions of the device. Shared components can include, but are not limited to, for example, the CPU, memory (including RAM, ROM, data storage, other storage, and applications), power supply, external interface(s), user interface, and display.
It will also be recognized that any portion of the methods described herein, including all portions relating to recognition of an object in an image, can be performed by a device other than a camera or a camera-containing device. For example, the recognition of an object in an image can be performed entirely, or partially, by a separate device such as a computer or the like.
The above specification, examples and data provide a description of the manufacture and use of the composition of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention also resides in the claims hereinafter appended.
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|Pilpre, Arnaud, "Self- Properties of Multi Sensing Entities in Smart Environments," Thesis Submitted May 6, 2005 for the degree of Master of Science, Massachusetts Institute of Technology, Jun. 2005.
|Primot, J. et al., "Deconvolution from Wave-front Sensing: A New Technique for Compensating Turbulence-degraded Images," The Journal of the Optical Society of America, Sep. 1990, 7:1598-1608.
|Rabie, T. et al., "Adaptive-neighborhood Image Deblurring," Journal of Electronic Imaging, Oct. 1994, 3 (4):368-378.
|Raskar, R. et al., "Coded Exposure Photography: Motion Deblurring Using Fluttered Shutter," ACM Transactions on Graphics (TOG), Jul. 2006, 25(3):795-804.
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|Citing Patent
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|Oct 12, 2010
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|Inferencing and learning based on sensorimotor input data
|U.S. Classification
|382/118, 382/190
|International Classification
|Cooperative Classification
|European Classification
|Aug 27, 2008
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Big Loan for Deutsche Bank Towers
BERLIN—In one of the largest single-lender loans since the property crash, the property arm of German insurer Allianz AG has agreed to provide €315 million ($458.8 million) toward a real-estate purchase.
The loan is going to a closed-end real-estate fund managed by a Deutsche Bank AG subsidiary, and it will finance the purchase of the Deutsche Bank towers, the bank's Frankfurt headquarters.
The sale of the office complex to...


text:
Guest Editorial
Recently a good client of mine called me to discuss a new standardized fee schedule they were putting together for the Texas area. The following is a letter I wrote in response to help explain how we got to the current price structure in the industry today. I thought it might be interesting to those newer to the industry.
"I want to thank you for reaching out to me and other repossession agents regarding setting up a standardized pricing structure in Texas. I wanted to give a brief explanation and history of repossession fees to give you a better understanding of how current pricing structures came about and how this has affected the repossession industry. I've been in this industry since 1975 so I do have some experience in this.
"Before the auto auctions decided to get into the repossession business (by forming forwarding companies), repossession rates were established by the repossession companies and the market place. At this time repossession companies charged for repossession, storage, keys, skip tracing, delivery, mileage for out of town accounts and close fees. When the auto auctions got into the business, in order to land the work from the large lenders, they promised a flat repossession fee and no close fees. To do this, they demanded much lower repossession fees from the repossession companies and no close fees. Many of these companies either lost a good percentage of their business or acquiesced to work for cut rates. And as the auctions set up their pricing to include the cost of storing vehicles they auctioned off, they also demanded free storage from repossession companies (which did not have the cost of the storage included in their repossession fees).
"Other fees disappeared as well, making keys (as the auctions wanted to do that themselves), skip tracing (because they had promised flat rates to the lender) as well as mileage fees. This had some positive effects on the industry in that repossession companies had to learn to tighten their belts and eliminate waste. But it also resulted in companies having to cut corners, reduce quality of service or many companies simply went out of business.
"In order to still make a profit with all these reduced fees a new fee (at least one I had never heard of before this time) came into being called a "redemption fee." This ranged from $75 – $150 and is charged to the debtor for redeeming their vehicle. Companies were also allowed to continue to charge storage to the debtors if they redeemed because this didn't affect the flat rate promised by the forwarding companies for the repossession. Being able to charge these fees was an effort to try to make a profit from a repossession fee that, in itself, often resulted in a net loss.
"Now some lenders are saying, 'we don't want our customers to have to pay storage when they redeem their vehicles, after all we don't pay storage and it's hard for them to come up with that additional money.' And some lenders are also saying companies can't charge a redemption fee. In the meantime the cost of equipment, fuel, insurance, compliance, property taxes, and on and on, all continue to rise.
"Repossession rates have been artificially pushed down by forwarders and large lenders demanding fees which can not sustain a company that provides quality service, maintains proper insurance, bonding, training, equipment, compliance standards and so on. To make up for all the lost revenue, repossessors have had to either take short cuts, charge additional fees (like redemption fees, personal property fees, and higher storage rates for debtors) or find other ways to add to their revenue on each car such as providing on-line or local auction services themselves."
Repossession fees have been cut and cut to the point it is getting dangerous. It's been shown time and time again, when you read these stories about repossessions gone bad, they happened when you have untrained people out there picking up cars and/or they are under undue stress to repossess that car because if they don't, they don't get paid—their family doesn't eat. If you cut back fees too far, it becomes impossible for companies to do it right. Perhaps instead of trying to find more ways to save the consumer money, maybe it's time to start to find ways to make the consumer and general public more safe by paying a higher repossession fee that allows for companies to run their businesses right and to make their repossessions safe.
Russ DeWitt
President,Capital Adjusters, Inc.
Past President, National Finance Adjusters
American Recovery Association
California Assn of Licensed Repossessors
Certified Asset Recovery Specialist
Certified Recovery Agent
Send any comments to: email@example.com
Phone: 512-5-836-8030 Fax: 512-836-5379 Website: www.repoaustin.com
PO Box 140111, Austin, TX 78714


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text:
Meet Nate! {Featured Social Worker of the Month-September}
I am so excited to share today's Social Worker of the Month with you! It's Nate from over at Social Worker Success! I reached out to Nate since we have some things in common (ummmm, a love of social work AND blogging!) and was very pleased when he agreed to be featured. So without further ado, here's Nate!
Why did you choose the field of social work? What is your current position?
As a child and adolescent, I was often described with these adjectives:
- Good listener
- Patient
- Friendly
I knew I didn't want to sell widgets for a living. When I was an undergraduate student at the University of Alabama, I wasn't sure exactly what kind of work I would do after graduating. Fortunately I stumbled into a class at the school of social work. When I arrived there, I knew I had found my people and my calling. I also found a mentor who gave me a long term vision of getting my LCSW.
I've worked in the mental and behavioral health fields for most of my career. I currently have two postions: 1) a clinical social worker for a large medical center serving Veterans and their families, and 2) founder and blogger at SocialWorkerSuccess.com where I write and coach on the habits, tools, and skills social workers use to be successful.
Share a moment/case story that makes you proud to be a social worker.
On the micro-level I love it any time a client or patient finds ( or rediscovers) meaning and purpose in their lives. For example, a client I worked with began volunteering at a horse stable recently. He had never worked with horses before, so this was a very new experience for him. To watch his new found sense of purpose and joy was something to behold.
On the macro-level, I'm proud to have co-chaired a committee last year that helped to update social work licensure laws in Alabama. The reforms had been attempted multiple times in the past decade, so it was satisfying to be a part of making positive changes that affect all social workers in my state.
What do you do to take care of yourself?
Ah…the good ole question: What do you do for self care? I'm a big believer in what you do everyday is way, way, more important than what you do occasionally. Here are a few things I do daily:
- Incorporate formal and informal mindful awareness practices throughout the day.
- Stay physically active for at least 30 minutes daily. Right now my wife and I are experimenting doing "21 Day Yoga Retreat" at home. It has been awesome 😎.
- Eat a healthy breakfast low in sugar and high in protein.
What keeps you going when the going gets tough?
Knowing that I'm going to die. . . eventually. And no matter how tough the going is, this too shall pass. My wife and daughter are the absolute joys of my life.
What advice would you have for someone new to the social work field?
Watch less TV (a lot less) and invest in yourself.
What was your most challenging experience as a social worker? How did you overcome it?
Starting a new job and my mother's cancer returning in the same week. She died less than five months later. I was a hot mess for quite a while after that. How did I overcome it: faith in God, unwavering support from my wife, and heaping doses of individual and group psychotherapy.
Are there any resources, websites, books that you reference regularly or that have inspired you along your social work journey?
Books: (non-affiliate links)
- Seven Habits of Highly Effective People by Stephen Covey (seriously, we can't be friends if you haven't read this)
- Emotional Intelligence:Why It Can Matter More Than IQ by Daniel Goleman
- The End of Poverty: Economic Possibilities for Our Time by Jeffrey Sachs
- Anything by Brené Brown
Three social work centric mainstays:
A couple of others not necessarily social work related:
TED Talks ( I'm a shameless TED fanboy)
Wait But Why (Arguably the funniest blog on the inter-webs right now)
Do you have any "ah-ha" moments that you would like to share?
Two ahas in my life, both from read quotes from people much wiser than me:
"For a long time it seemed to me that life was about to begin – real life. but there was always some obstacle in the way, something to be gotten through first, some unfinished business, time still to be served, a debt to be paid. at last it dawned on me that these obstacles were my life. this perspective has helped me to see there is no way to happiness. happiness is the way. so treasure every moment you have and remember that time waits for no one."
― Father Alfred Souza*
"Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth and our freedom."
― Viktor E. Frankl
*This is a loose citation, at best. The quote is often credited to Souza, but I don't have any resource other than vague Google references. Would love any verified information on who Souza is/was.
There you have it–some amazing and heartfelt words of wisdom from Nate. If you haven't checked out Nate's website before, I'd highly recommend it–you'll find goodies, such as my favorite, 9 Life Lessons From Prince, as well as other great advice and ideas for social work success. Thank you Nate for taking the time to share some of yourself with the rest of us–I very much appreciate the information you shared!
Are you interested in being a featured social worker? Check out the Featured Social Worker page and/or send me an email at firstname.lastname@example.com.