Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: Techniques related to video processing with adaptive sharpness enhancement are discussed. Such techniques may include determining a detail versus noise score for a video frame based on a noise level, a motion level, an average luma value, and spatial frequency information of the video frame and enhancing sharpness of the video frame based on sharpness enhancement control parameters generated based on the detail versus noise score for the video frame.

Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: An apparatus for visual search and retrieval using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, an extractor, and a comparator. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each segment, wherein the score is based, at least partially, on a classification probability of each frame. The extractor is to extract deep features from a highest ranked segment, and the comparator is to determine the top-K neighbors based on the deep features.

Abstract: An apparatus for video summarization using semantic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: Techniques related to video processing with adaptive sharpness enhancement are discussed. Such techniques may include determining a detail versus noise score for a video frame based on a noise level, a motion level, an average luma value, and spatial frequency information of the video frame and enhancing sharpness of the video frame based on sharpness enhancement control parameters generated based on the detail versus noise score for the video frame.

Abstract: An apparatus for visual search and retrieval using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, an extractor, and a comparator. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each segment, wherein the score is based, at least partially, on a classification probability of each frame. The extractor is to extract deep features from a highest ranked segment, and the comparator is to determine the top-K neighbors based on the deep features.

Abstract: Techniques for a system, article, and method of low-complexity histogram of gradients generation for image processing may include histogram of gradients generation for image processing including the following operations: obtaining image data including horizontal and vertical gradient components of individual pixels of an image; associating the horizontal and vertical gradient components of the same pixel with one of a plurality of angular channels depending on the values of the horizontal and vertical gradient components; determining a gradient magnitude and a gradient orientation of individual angular channels after the horizontal and vertical gradient components are assigned to the channels; and generating a histogram of gradients by using the gradient direction and gradient magnitude of the angular channels.

Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: Systems, apparatus, articles, and methods are described below including operations for adaptive control for denoise filtering.

Abstract: Techniques for a system, article, and method of low-complexity histogram of gradients generation for image processing may include histogram of gradients generation for image processing including the following operations: obtaining image data including horizontal and vertical gradient components of individual pixels of an image; associating the horizontal and vertical gradient components of the same pixel with one of a plurality of angular channels depending on the values of the horizontal and vertical gradient components; determining a gradient magnitude and a gradient orientation of individual angular channels after the horizontal and vertical gradient components are assigned to the channels; and generating a histogram of gradients by using the gradient direction and gradient magnitude of the angular channels.