Abstract: Disclosed are systems and methods for improving interactions with and between computers in content hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically creating a caption comprising a sequence of words in connection with digital content.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically creating a caption comprising a sequence of words in connection with digital content.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically creating a caption comprising a sequence of words in connection with digital content.

Abstract: Systems and methods for automated detection and classification of objects in a fluid of a receptacle such as, for example, a soft-sided receptacle such as a flexible container. The automated detection may include initiating movement of the receptacle to move objects in the fluid contained by the receptacle. Sequential frames of image data may be recorded and processed to identify moving objects in the image data. In turn, at least one motion parameter of the objects may be determined and utilized to classify the object into at least one of a predetermined plurality of object classes. For example, the object classes may at least include a predetermined class corresponding to bubbles and a predetermined class corresponding to particles.

Abstract: Systems and methods for automated detection and classification of objects in a fluid of a receptacle such as, for example, a soft-sided receptacle such as a flexible container. The automated detection may include initiating movement of the receptacle to move objects in the fluid contained by the receptacle. Sequential frames of image data may be recorded and processed to identify moving objects in the image data. In turn, at least one motion parameter of the objects may be determined and utilized to classify the object into at least one of a predetermined plurality of object classes. For example, the object classes may at least include a predetermined class corresponding to bubbles and a predetermined class corresponding to particles.

Abstract: The present invention provides a face recognition method. The method includes obtaining a plurality of training face images which belongs to a plurality of face classes and obtaining a plurality of training dictionaries corresponding to the training face images. A face class includes one or more training face images. The training dictionaries include a plurality of deep feature matrices. The method further includes obtaining an input face image. The input face image is partitioned into a plurality of blocks, whose corresponding deep feature vectors are extracted using a deep learning network. A collaborative representation model is applied to represent the deep feature vectors with the training dictionaries and representation vectors. A summation of errors for all blocks corresponding to a face class is computed as a residual error for the face class. The input face image is classified by selecting the face class that yields a minimum residual error.

Abstract: A method for video sequential alignment is provided. The method includes inputting a first video sequence and a second video sequence, and extracting features of the first video sequence and the second video sequence. The method also includes generating an expanded path using a hierarchical sequential alignment algorithm and generating masks containing search space using keyframe matching. Further, the method includes calculating the lowest path cost within search space to find a minimal alignment path using a block A* algorithm and outputting a final alignment result after applying the block A* algorithm to search space.

Abstract: Systems and methods for automated detection and classification of objects in a fluid of a receptacle such as, for example, a soft-sided receptacle such as a flexible container. The automated detection may include initiating movement of the receptacle to move objects in the fluid contained by the receptacle. Sequential frames of image data may be recorded and processed to identify moving objects in the image data. In turn, at least one motion parameter of the objects may be determined and utilized to classify the object into at least one of a predetermined plurality of object classes. For example, the object classes may at least include a predetermined class corresponding to bubbles and a predetermined class corresponding to particles.

Abstract: A method for video sequential alignment is provided. The method includes inputting a first video sequence and a second video sequence, and extracting features of the first video sequence and the second video sequence. The method also includes generating an expanded path using a hierarchical sequential alignment algorithm and generating masks containing search space using keyframe matching. Further, the method includes calculating the lowest path cost within search space to find a minimal alignment path using a block A* algorithm and outputting a final alignment result after applying the block A* algorithm to search space.