Abstract: A system, a method, and a computer program product for recognizing a logo from an image is provided. The method comprises receiving and preprocessing an image that contains the logo to be recognized. The method further comprises detecting text of the logo to recognize the logo. The method furthermore comprises extracting local and global features of a region of interest associated with the logo to recognize the logo if the text is not detected in the logo. The method further comprises detecting one or more signs in proximity of the logo to recognize the logo if the logo is not recognized by extracting the local and global features of the logo. The method finally comprises identifying a region where the logo is present in the pre-processed image to recognize the logo if the logo is not recognized by detecting the one or more signs in proximity of the logo.

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, a method, and a computer program product for recognizing a logo from an image is provided. The method comprises receiving and preprocessing an image that contains the logo to be recognized. The method further comprises detecting text of the logo to recognize the logo. The method furthermore comprises extracting local and global features of a region of interest associated with the logo to recognize the logo if the text is not detected in the logo. The method further comprises detecting one or more signs in proximity of the logo to recognize the logo if the logo is not recognized by extracting the local and global features of the logo. The method finally comprises identifying a region where the logo is present in the pre-processed image to recognize the logo if the logo is not recognized by detecting the one or more signs in proximity of the logo.

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.