{'fraction_non_alphanumeric': 0.017027863777089782, 'fraction_numerical': 0.0, 'mean_word_length': 5.596938775510204, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Efficient Target Detection Technique Using Image Matching Via Hybrid Feature Descriptors

Image matching is one of the most famous applications in computer vision and robotics. It is used for real time target detection and recognition systems. An ideal image matching technique should be robust to different image transformations such as scaling, illumination, noise and rotation. Different feature descriptors and detectors such as SIFT, SURF, BRISK, AKAZE and ORB have been introduced previously. However, each one of them has its own weak points in their matching performance. In this paper, we firstly perform a comprehensive comparison between such image matching techniques and their performance on different datasets of images. Then, we introduce a hybrid technique that combine between different feature descriptors. The experimental results showed that the proposed hybrid technique has improved the robustness of the image matching process. The conducted comparative analysis based on the execution time, number of keypoints detected and number of inliers (good matches after outliers’ rejection) has revealed the power of combined ORB and BRISK feature descriptors, outperforming the other feature descriptors combinations in enhancing the accuracy of matching and detection tasks.