A processing system having at least one processor may obtain at least a first source video from a first endpoint device and a second source video from a second endpoint device, where each of the first source video and the second source video is a two-dimensional video, determine that the first source video and the second source video share at least one feature that is the same for both the first source video and the second source video, and generate a volumetric video from the first source video and the second source video, where the volumetric video comprises a photogrammetric combination of the first source video and the second source video.

A method is disclosed that includes receiving, by a processing device, a plurality of images of a test assembly. The processing device selects a component in the test assembly and an image of the plurality of images of the test assembly as received. For the component as selected and the image as selected, the processing device compares a plurality of portions of the image as selected to a corresponding plurality of portions of a corresponding profile image and computing a matching score for each of the plurality of portions. The processing device selects a largest matching score from the matching score for each of the plurality of portions as a first matching score for the component as selected and the image as selected. The first matching score is stored for the component as selected and the image as selected.

In medical examination of breast cancer, a lesion computer-aided detection is performed in real time and with high accuracy, and a burden on a medical worker is reduced. A medical image processing apparatus that processes a medical image includes: a detection unit configured to detect a lesion candidate region; a validity evaluation unit configured to evaluate validity of the lesion candidate region by using a normal tissue region corresponding to the detected lesion candidate region; and a display unit configured to determine display content to a user by using an evaluation result.

An image processing apparatus includes a memory storing one or more instructions, and a processor configured to execute the one or more instructions stored in the memory. The processor is configured to, by using one or more convolution neural networks, extract target features by performing a convolution operation between features of target regions having same locations in a plurality of input images and a first kernel set, extract peripheral features by performing a convolution operation of features of peripheral regions located around the target regions in the plurality of input images and a second kernel set, and determine a feature of a region corresponding to the target regions in an output image, based on the target features and the peripheral features.

A computer-implemented method of training an object detector, the method comprising: training an embedding neural network using, as an input, cropped images from an image dataset, wherein training the embedding neural network is performed using a self-supervised learning approach and the trained embedding neural network translates input images into a lower dimensional representation; and training an object detector neural network by, for images of the image dataset, repeatedly: passing an image through the object detector neural network to obtain proposed coordinates of an object within the image, cropping the image to the proposed coordinates to obtain a cropped image, passing the cropped image through the trained embedding neural network to obtain a cropped image representation, passing an exemplar through the trained embedding neural network to obtain an exemplar representation, wherein the exemplar is a cropped manually labelled image bounding a known object, computing a distance in embedding space between the cropped image representation and the exemplar representation, computing a gradient of the cropped image representation and the exemplar representation with respect to the distance, and passing the gradient into the object detector neural network for use in backpropagation to optimise the object detector neural network.

An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.

The present disclosure relates to systems and methods for object recognition. The system may obtain an image and a model. The image may include a search region in which the object recognition process is performed. In the objection recognition process, for each of one or more sub-regions of the search region, the system may determine a match metric indicating a similarity between the model and the sub-region of the search region. Further, the system may determine an instance of the model among the one or more sub-regions of the search region based on the match metrics.

A method includes receiving a first image that is captured at a first time. The method also includes detecting a location of a first object in the first image. The method also includes determining a region of interest based at least partially upon the location of the first object in the first image. The method also includes receiving a second image that is captured at a second time. The method also includes identifying the region of interest in the second image. The method also includes detecting a location of a second object in a portion of the second image that is outside of the region of interest.

A processor-implemented method of tracking a target object includes: extracting a feature from frames of an input image; selecting one a neural network model from among a plurality of neural network models that is provided in advance based on a feature value range, based on a feature value of a target object that is included in the feature of a previous frame among the frames; and generating a bounding box of the target object included in a current frame among the frames, based on the selected neural network model.

An apparatus detects a plurality of areas from an overall image, determines on each of the plurality of areas whether the area is a candidate for an area including an object as a processing target based on a determination reference value (first determination processing), acquires a zoom-in image of the area determined to be the candidate for the area including the object in the first determination processing, and determines whether the acquired zoom-in image is an image of the object (second determination processing). The apparatus identifies an area, in the overall image, corresponding to the zoom-in image determined not to be the image of the object in the second determination processing, and performs control to update the determination reference value used in the first determination processing based on image information of the identified area in the overall image.