Provided are devices, computer-program products, and methods related to removing redundant data associated with frames. For example, a method can include storing a plurality of reference data sets in a reference database, where a reference data set is associated with a media segment. The method can further include receiving, by a server, cue data for a frame. The cue data includes a plurality of pixel data samples from the frame, and the frame is associated with an unidentified media segment. The method can further include identifying an absence of a pixel data sample from the cue data for the frame, and matching the cue data for the frame to a reference data set. The matching can include using a previous pixel data sample from a previous frame. The previous pixel data sample corresponds to the pixel data sample absent from the frame, and the reference data set is associated with a media segment. The method can further include determining the unidentified media segment is the media segment.

It is an object to provide a transmitting apparatus, a receiving apparatus, and a transmission system that are capable of performing an image quality adjustment process on a partial region of interest (ROI) segmented from a captured image. The transmitting apparatus includes a controlling section that controls acquisition of image quality adjusting information including information for use in adjusting image quality of each of a plurality of ROIs, and a transmitting section that sends out image data of the plurality of ROIs as payload data and sends out ROI information of each of the plurality of ROIs as embedded data.

A method and an apparatus for grounding a target video clip in a video are provided. The method includes: determining a current video clip in the video based on a current position; acquiring descriptive information indicative of a pre-generated target video clip descriptive feature, and executing a target video clip determining step which includes: determining current state information of the current video clip, wherein the current state information includes information indicative of a feature of the current video clip; generating a current action policy based on the descriptive information and the current state information, the current action policy being indicative of a position change of the current video clip in the video; the method further comprises: in response to reaching a preset condition, using a video clip resulting from executing the current action policy on the current video clip as the target video clip.

Methods, systems, and computer programs are presented for detecting near duplicates and partial matches of videos. One method includes an operation for receiving a video containing frames. For each frame, keypoints are determined within the frame. For each keypoint, a horizontal gradient vector is calculated based on a horizontal gradient at the keypoint and a vertical gradient vector is calculated based on a vertical gradient at the keypoint. The horizontal and vertical gradients are binary vectors. Further, a keypoint description is generated for each keypoint based on the horizontal gradient vector and the vertical gradient vector. Further, the frames are matched to frames of videos in a video library based on the keypoint descriptions of the keypoints in the frame in the videos in the video library. Further, a determination is made if the video has near duplicates in the video library based on the matching.

The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.

A multi-dimensional database and indexes and operations on the multi-dimensional database are described which include video search applications or other similar sequence or structure searches. Traversal indexes utilize highly discriminative information about images and video sequences or about object shapes. Global and local signatures around keypoints are used for compact and robust retrieval and discriminative information content of images or video sequences of interest. For other objects or structures relevant signature of pattern or structure are used for traversal indexes. Traversal indexes are stored in leaf nodes along with distance measures and occurrence of similar images in the database. During a sequence query, correlation scores are calculated for single frame, for frame sequence, and video clips, or for other objects or structures.

A multi-dimensional database and indexes and operations on the multi-dimensional database are described which include video search applications or other similar sequence or structure searches. Traversal indexes utilize highly discriminative information about images and video sequences or about object shapes. Global and local signatures around keypoints are used for compact and robust retrieval and discriminative information content of images or video sequences of interest. For other objects or structures relevant signature of pattern or structure are used for traversal indexes. Traversal indexes are stored in leaf nodes along with distance measures and occurrence of similar images in the database. During a sequence query, correlation scores are calculated for single frame, for frame sequence, and video clips, or for other objects or structures.

A multi-dimensional database and indexes and operations on the multi-dimensional database are described which include video search applications or other similar sequence or structure searches. Traversal indexes utilize highly discriminative information about images and video sequences or about object shapes. Global and local signatures around keypoints are used for compact and robust retrieval and discriminative information content of images or video sequences of interest. For other objects or structures relevant signature of pattern or structure are used for traversal indexes. Traversal indexes are stored in leaf nodes along with distance measures and occurrence of similar images in the database. During a sequence query, correlation scores are calculated for single frame, for frame sequence, and video clips, or for other objects or structures.

A multi-dimensional database and indexes and operations on the multi-dimensional database are described which include video search applications or other similar sequence or structure searches. Traversal indexes utilize highly discriminative information about images and video sequences or about object shapes. Global and local signatures around keypoints are used for compact and robust retrieval and discriminative information content of images or video sequences of interest. For other objects or structures relevant signature of pattern or structure are used for traversal indexes. Traversal indexes are stored in leaf nodes along with distance measures and occurrence of similar images in the database. During a sequence query, correlation scores are calculated for single frame, for frame sequence, and video clips, or for other objects or structures.

The present invention discloses a method and a system for monitoring manufacturing operation workflow using Structural Similarity (SSIM) index based activity detection. The method comprising receiving video data corresponding to a manufacturing operation activity, extracting a plurality of video frames from the video data, measuring SSIM index for each video frame of the plurality of video frames with respect to next consecutive video frame of the plurality of video frames, comparing the SSIM index of the each video frame with the SSIM index of next consecutive video frame of the plurality of video frames to identify one or more local maxima, and determining at least one manufacturing operation activity based on the one or more local maxima using machine learning technique.