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 retrieval of similar images related to query images is provided. The query images are pre-processed for noise removal by selecting filtering technique based on noise variance estimation in each query image with respect to pre-set noise variance threshold value. The pre-processed query images are pre-classified for determining class one image identifier. Image types are generated from pre-processed query images for determining class two image identifier. Features are extracted from pre-classified query images based on class one image identifier and from generated images based on class two image identifier. The images similar to query images are retrieved which have features similar to extracted features of pre-classified query images and generated images. The retrieved similar images are ranked for determining most similar images with respect to query images. Similarity between query images and retrieved similar images is analyzed for re-ranking retrieved similar images.

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: A system and method for retrieval of similar images related to query images is provided. The query images are pre-processed for noise removal by selecting filtering technique based on noise variance estimation in each query image with respect to pre-set noise variance threshold value. The pre-processed query images are pre-classified for determining class one image identifier. Image types are generated from pre-processed query images for determining class two image identifier. Features are extracted from pre-classified query images based on class one image identifier and from generated images based on class two image identifier. The images similar to query images are retrieved which have features similar to extracted features of pre-classified query images and generated images. The retrieved similar images are ranked for determining most similar images with respect to query images. Similarity between query images and retrieved similar images is analyzed for re-ranking retrieved similar images.

Abstract: A system and computer-implemented method for detecting retinopathy is provided. The system comprises an image input module configured to receive one or more fundus images. Further, the system comprises a pre-processing module configured to apply one or more transformations to the one or more received fundus images. Furthermore, the system comprises a feature extraction module configured to extract one or more features from the one or more transformed images using one or more Convolutional Neural Networks (CNNs). Also, the system comprises a prediction module configured to determine stage of retinopathy by classifying the one or more extracted features using pre-stored features, wherein the pre-stored features are extracted from one or more training fundus images by the one or more CNNs and further wherein each pre-stored feature corresponds to a class which is associated with a predetermined stage of retinopathy.

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 computer-implemented method for detecting retinopathy is provided. The system comprises an image input module configured to receive one or more fundus images. Further, the system comprises a pre-processing module configured to apply one or more transformations to the one or more received fundus images. Furthermore, the system comprises a feature extraction module configured to extract one or more features from the one or more transformed images using one or more Convolutional Neural Networks (CNNs). Also, the system comprises a prediction module configured to determine stage of retinopathy by classifying the one or more extracted features using pre-stored features, wherein the pre-stored features are extracted from one or more training fundus images by the one or more CNNs and further wherein each pre-stored feature corresponds to a class which is associated with a predetermined stage of retinopathy.

Abstract: A system and computer-implemented method for automatically extracting and analyzing data from one or more data sources is provided. The system comprises a platform manager configured to provide options for configuring rules for data extraction. The system further comprises a web scraping and crawling module configured to extract data from one or more data sources by executing one or more data extraction jobs using the configured rules. Furthermore, the system comprises an information extraction engine configured to analyze the extracted data by performing one or more analytical operations, decipher the analyzed data using pre-stored vocabularies and classify the deciphered data. The information extraction engine further configured to convert at least one of: the analyzed data, the deciphered data and the classified data to one or more formats for use by at least one of: one or more enterprise applications, enterprise portals and one or more communication channels.