Methods and apparatus to improve driver-assistance vision systems using object detection based on motion vectors are disclosed. An example apparatus includes a motion vector object detection analyzer to generate a motion vector boundary box around an object represented in a first image, the motion vector boundary box generated based on a comparison of the first image relative to a second image. The example apparatus also includes a boundary box analyzer to: determine whether the motion vector boundary box corresponds to any artificial intelligence (AI)-based boundary box generated based on an analysis of the first image using an object detection machine learning model; and, in response to the motion vector boundary box not corresponding to any AI-based boundary box associated with the first image, associate a label with the motion vector boundary, the label to indicate the object detection machine learning model did not recognize the object in the first image.

A method includes receiving a reference image and a non-reference image; dividing the reference image into a plurality of tiles; determining, using an electronic device, a motion vector map using coarse-to-fine based motion vector estimation; and generating an output frame using the motion vector map with the reference image and the non-reference image.

A method includes receiving a reference image and a non-reference image; dividing the reference image into a plurality of tiles; determining, using an electronic device, a motion vector map using coarse-to-fine based motion vector estimation; and generating an output frame using the motion vector map with the reference image and the non-reference image.

A visual search method, a computer device, and a non-transitory computer readable storage medium are provided. An i.sup.th image frame is received. The location and the classification of the subject in the i.sup.th image frame are extracted. A detection block corresponding to the subject is generated. In subsequent image frames of the i.sup.th image frame, the subject is tracked on the basis of the location of the subject in the i.sup.th image frame. The detection block is adjusted on the basis of the tracking result.

Techniques related to motion estimation with neighbor block pattern for video coding.

Techniques related to motion estimation with neighbor block pattern for video coding.

An image processing apparatus including a pixel shifting super-resolution image generation circuit configured to generate a high-resolution composite image from a plurality of images, an evaluation area setting circuit configured to set a plurality of evaluation areas within an area extraction range of the composite image, a synthesis accuracy evaluation circuit configured to evaluate, for each of the plurality of evaluation areas, a pixel filling rate for the evaluation area and calculate a plurality of pixel filling rate evaluation values, and a determination circuit configured to determine a determination area from among the plurality of evaluation areas based on the plurality of pixel filling rate evaluation values.

Regions for texture-based coding are identified using a spatial segmentation and a motion flow segmentation. For frames of a group of frames in a video sequence, a frame is segmented using a first classifier into at least one of a texture region or a non-texture region of an image in the frame. Then, the texture regions of the group of frames are segmented using a second classifier into a texture coding region or a non-texture coding region. The second classifier uses motion across the group of frames as input. Each of the classifiers is generated using a machine-learning process. Blocks of the non-texture region and the non-texture coding region of the current frame are coded using a block-based coding technique, while blocks of the texture coding region are coded using a coding technique that is other than the block-based coding technique.

Regions for texture-based coding are identified using a spatial segmentation and a motion flow segmentation. For frames of a group of frames in a video sequence, a frame is segmented using a first classifier into at least one of a texture region or a non-texture region of an image in the frame. Then, the texture regions of the group of frames are segmented using a second classifier into a texture coding region or a non-texture coding region. The second classifier uses motion across the group of frames as input. Each of the classifiers is generated using a machine-learning process. Blocks of the non-texture region and the non-texture coding region of the current frame are coded using a block-based coding technique, while blocks of the texture coding region are coded using a coding technique that is other than the block-based coding technique.

An electronic home plate system includes a home plate enclosure and at least one image sensor system disposed within the enclosure, each image sensor system including an image sensor, a lens and a first processor. The first processor is adapted to detect a motion of a ball by continuously capturing frames from the image sensor. The detected motion includes the ball passing over the home plate enclosure or the ball passing near and not over the home plate enclosure.