Abstract: A system and method for for grouping of similar image classes for image recognition is provided. The invention comprises extracting one or more features from multiple classes of images for determining a correlation value between each of the multiple classes of images based on assessment of the extracted features of each one of the classes of images with respect to other classes of images in the multiple classes of images. Further, the one class of image is grouped with the other class of image in the multiple classes of images to form one or more groups of super-classes of similar class of images based on analysis of the determined correlation values with respect to a pre-determined threshold value. An input image is recognized based on the formed groups of super-classes followed by sub-class classification of the images.

Abstract: The present disclosure is directed to a traffic light recognition system and method for advanced driver assistance systems (ADAS) and robust to variations in illumination, partial occlusion, climate, shape and angle at which traffic light is viewed. The solution performs a real time recognition of traffic light by detecting the region of interest, where extracting the region of interest is achieved by projecting the sequence of frames into a kernel space, binarizing the linearly separated sequence of frames, identifying and classifying the region of interest as a candidate representative of traffic light. With the aforesaid combination of techniques used, traffic light can be conveniently recognized from amidst closely similar appearing objects such as vehicle headlights, tail or rear lights, lamp posts, reflections, street lights etc. with enhanced accuracy in real time.

Abstract: A system and method for for grouping of similar image classes for image recognition is provided. The invention comprises extracting one or more features from multiple classes of images for determining a correlation value between each of the multiple classes of images based on assessment of the extracted features of each one of the classes of images with respect to other classes of images in the multiple classes of images. Further, the one class of image is grouped with the other class of image in the multiple classes of images to form one or more groups of super-classes of similar class of images based on analysis of the determined correlation values with respect to a pre-determined threshold value. An input image is recognized based on the formed groups of super-classes followed by sub-class classification of the images.

Abstract: The present disclosure is directed to a traffic light recognition system and method for advanced driver assistance systems (ADAS) and robust to variations in illumination, partial occlusion, climate, shape and angle at which traffic light is viewed. The solution performs a real time recognition of traffic light by detecting the region of interest, where extracting the region of interest is achieved by projecting the sequence of frames into a kernel space, binarizing the linearly separated sequence of frames, identifying and classifying the region of interest as a candidate representative of traffic light. With the aforesaid combination of techniques used, traffic light can be conveniently recognized from amidst closely similar appearing objects such as vehicle headlights, tail or rear lights, lamp posts, reflections, street lights etc. with enhanced accuracy in real time.

Abstract: A method of automatically focusing on a region of interest (ROI) by an electronic device is provided. The method includes extracting at least one feature from at least one candidate ROI in a field of view (FOV) in the electronic device, displaying at least one indicia for the at least one candidate ROI based on the at least one feature, receiving a selection of at least one ROI from among the at least one candidate ROI for which the at least one indicia is displayed; and focusing on the at least one ROI according to the selection.