Abstract: The present invention relates to an artificial intelligence based system and method for moderating interaction between interacting users. The attempt is to improve emotional intelligence of users so that a seasoned response and reaction is observed during interaction, even if situations of conflict arise. The disclosure, thus, provides for a mixed reality glass powered assistant that displays the moderated expressions of a customer to the service provider. For the same, the analytical engine upon determining the negative emotions of customer, transforms the image of customer and adds smile to his face, which is presented to the service provider via a mixed reality glass so that he responds back to the customer in a positive manner.

Abstract: The present invention relates to an artificial intelligence based system and method for moderating interaction between interacting users. The attempt is to improve emotional intelligence of users so that a seasoned response and reaction is observed during interaction, even if situations of conflict arise. The disclosure, thus, provides for a mixed reality glass powered assistant that displays the moderated expressions of a customer to the service provider. For the same, the analytical engine upon determining the negative emotions of customer, transforms the image of customer and adds smile to his face, which is presented to the service provider via a mixed reality glass so that he responds back to the customer in a positive manner.

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: The present subject matter is related in general to image processing that discloses a method for detecting nationality and layout of an input image. A nationality detection system retrieves predefined Financial Document (FD) images based on extracted features of an input image from a database and performs template matching of each predefined FD image with the input image to compute a first layout relevance score for each predefined FD image. Therefore, complexity of performing template matching with every predefined image in the database is eliminated, thereby increasing the processing speed. The nationality detection system detects the nationality and layout of the input image based on highest first layout relevance score if it is greater than or equal to predefined threshold value. Else, the nationality and layout of the input image are detected based on a nationality-based relevance score computed using the first layout relevance score of each predefined FD image.

Abstract: Embodiment of the present disclosure discloses method and device for determining one or more characteristics of currency note. The currency evaluating device is configured to receive image of currency note and detect text from one or more predefined first regions of image. Further, language associated with detected text is identifies and one of single nationality and plurality of nationalities associated with language of currency note is obtained from data source associated with currency evaluating device. When single nationality is obtained, nationality of currency note is identified to be said single nationality. When plurality of nationalities is obtained, nationality of currency note is identified from plurality of nationalities using Optical Character Recognition (OCR). Upon identifying nationality, one or more characteristics of currency note is identified by extracting object features on image based on identified nationality.

Abstract: The present subject matter is related in general to image processing that discloses a method for detecting nationality and layout of an input image. A nationality detection system retrieves predefined Financial Document (FD) images based on extracted features of an input image from a database and performs template matching of each predefined FD image with the input image to compute a first layout relevance score for each predefined FD image. Therefore, complexity of performing template matching with every predefined image in the database is eliminated, thereby increasing the processing speed. The nationality detection system detects the nationality and layout of the input image based on highest first layout relevance score if it is greater than or equal to predefined threshold value. Else, the nationality and layout of the input image are detected based on a nationality-based relevance score computed using the first layout relevance score of each predefined FD image.

Abstract: Embodiment of the present disclosure discloses method and device for determining one or more characteristics of currency note. The currency evaluating device is configured to receive image of currency note and detect text from one or more predefined first regions of image. Further, language associated with detected text is identifies and one of single nationality and plurality of nationalities associated with language of currency note is obtained from data source associated with currency evaluating device. When single nationality is obtained, nationality of currency note is identified to be said single nationality. When plurality of nationalities is obtained, nationality of currency note is identified from plurality of nationalities using Optical Character Recognition (OCR). Upon identifying nationality, one or more characteristics of currency note is identified by extracting object features on image based on identified nationality.

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: An electrical device having a projection device for projecting image information onto a projection surface disposed externally to the telecommunication device; an inertial sensor to detect a movement of the electrical device; the electrical device being operable in a first operating mode so that the projection of the image information onto the projection surface follows a movement of the electrical device with a movement component in a plane disposed in parallel to the plane of the projection surface; the electrical device being operable in a second operating mode so that, in spite of the electrical device's movement, the projection of the image information onto the projection surface with a movement component in a plane parallel to the plane of the projection surface is at least partially substantially fixed; the electrical device being configured so that its movement in the second operating mode is interpreted as a user input.