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, 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 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: 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 framework for implementing multitenant architecture is provided. The framework comprises a framework services module which is configured to provide framework services that facilitate abstraction of Software-as-a-Service (SaaS) services and crosscutting services for a Greenfield application and a non SaaS based web application. Further the abstraction results in a SaaS based multitenant web application. The framework further comprises a runtime module configured to automatically integrate and consume the framework services and APIs to facilitate monitoring and controlling of features associated with the SaaS based multitenant web application. The framework further comprises a metadata services module configured to provide a plurality of metadata services to facilitate abstraction of storage structure of metadata associated with the framework and act as APIs for managing the metadata.

Abstract: A framework for implementing multitenant architecture is provided. The framework comprises a framework services module which is configured to provide framework services that facilitate abstraction of Software-as-a-Service (SaaS) services and crosscutting services for a Greenfield application and a non SaaS based web application. Further the abstraction results in a SaaS based multitenant web application. The framework further comprises a runtime module configured to automatically integrate and consume the framework services and APIs to facilitate monitoring and controlling of features associated with the SaaS based multitenant web application. The framework further comprises a metadata services module configured to provide a plurality of metadata services to facilitate abstraction of storage structure of metadata associated with the framework and act as APIs for managing the metadata.