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Computer vision geo-location, awareness & detail

Computer vision (i.e., image understanding) involves understanding the 3D scene creating the image. Computer vision is challenging because it is the computer that decides how to act based on an understanding of the image. Key image understanding tasks include depth computation, as well as object detection, localization, recognition and tracking. Techniques up to now have not been able to perform any of these tasks robustly with the precision and accuracy demanded by many real-world applications. Additional complications include operational and environmental factors. For humans, visual recognition is fast and accurate, yet robust against occlusion, clutter, viewpoint variations, and changes in lighting conditions. Moreover, learning new categories requires minimal supervision and a very small set of exemplars. Achieving this level of performance in a wearable portable system would enable a great number of useful applications especially for enhancing mobile cell phone and camera operation. We demonstrate some of the computer vision techniques that we have developed and tested in real environments for applications in the field of automotive navigation, personal navigation, assistive devices and augmented reality. Some of the techniques include object detection and recognition, depth from motion, context recognition and the general task of mapping and localization. Our object detection techniques have shown to have performance close to 100%. We have actually shown that we can triangulate based on objects in the environment using only a camera; which can aid when GPS drops out such as in urban canyons and indoor environments. We argue that all of this potential can be packaged within a smart phone like an iphone.
 A category with the (minimum) three required fields