Computer controlled systems and methods of an automated homecage monitoring system predict a state of an animal and of its homecage. The prediction of the state of the animal may be based on a pose estimate of the animal, and at least one sensor input from an at least one sensor. The pose estimate may include a graph of connected nodes, the nodes representing coordinates of the sensor input corresponding to predicted indicia on the animal's body. The animal state may include a behavioral state of an animal, a social state of an animal, a position state of an animal, a sleep state of an animal, and a biological state of an animal. The automated homecage monitoring system may allow for animal state data to be reported for an animal or animals in situ.

Provided is an image processing device including: a memory; and a processor comprising hardware, the processor configured to: calculate a feature value; detect, as peak positions, pixel positions the feature value of which are greater than a prescribed feature value threshold value; record the detected peak positions; form, one at a time for the detected peak positions, a cell region; identify a center position of the formed cell region; determine, by using at least one of the peak position, a morphology of the cell region, and the center position of the cell region, a proximity state between the currently formed cell region and a previously formed cell region; and correct, when it is determined that the proximity state is satisfied, at least one of the currently formed cell region and the previously formed cell region.

An image processing device and an image processing method are disclosed. The image processing method comprises: acquiring an image; segmenting the acquired image by using a deep learning-based segmentation process to obtain a binarized image labeled with a region of interest; processing a pixel matrix of the acquired image by using a pixel matrix of the binarized image to obtain a segmented image; and performing super-resolution reconstruction on the segmented image by using a deep learning-based super-resolution neural network to obtain a reconstructed image.

An information processing method and apparatus are provided that performs operations including identifying, from an image obtained via an image capture device, at least one character string that is relevant in identifying information to be extracted from the image; defining an area, within the image, that includes information as an information extraction area, the information including a plurality of information elements; selecting a region within the defined area where the information to be extracted is expected to be present using a feature within the defined area; removing the feature from the selected region and correcting one or more errors associated with the information caused by the removal of the feature and extracting one or more alphanumeric characters from the corrected information, wherein the extracted one or more alphanumeric characters correspond to the elements of the information and are associated with a respective one of the at least one character strings.

A method of indexing and searching for video content is provided. For each frame of a first plurality of frames, a first global feature and a first plurality of local features may be identified. The first plurality of local features may be clustered around a first plurality of cluster centers. The first plurality of local features may be converted into a first plurality of binary signatures. An index that maps the first plurality of cluster centers and the first plurality of binary signatures to the first plurality of frames may be generated. A search request associated with a second video may be received and its direct and indirect features may be identified. The identified features of the second video may be compared against the index and a candidate video may be selected as a result of the search request.

A method of indexing and searching for video content is provided. For each frame of a first plurality of frames, a first global feature and a first plurality of local features may be identified. The first plurality of local features may be clustered around a first plurality of cluster centers. The first plurality of local features may be converted into a first plurality of binary signatures. An index that maps the first plurality of cluster centers and the first plurality of binary signatures to the first plurality of frames may be generated. A search request associated with a second video may be received and its direct and indirect features may be identified. The identified features of the second video may be compared against the index and a candidate video may be selected as a result of the search request.

According to the exemplary embodiments of the present disclosure, a method and apparatus for detecting a lane line, and a medium are provided. A method for generating a lane line detection model includes: detecting a lane line in an original image to generate a first image associated with the detected lane line; acquiring a second image generated based on the original image and associated with a marked lane line; generating at least one tag indicating whether the detected lane line is accurate, based on the first image and the second image; and training a classifier model for automatically identifying the lane line, based on the first image and the at least one tag. In such case, the lane line detection may be achieved in a simple and effective way.

There is provided an image processing apparatus in which a part used in predetermined processing is specified with use of a relatively small binarization threshold, from parts that have been converted into black pixels through binarization processing using a relatively large binarization threshold.

Disclosed are embodiments for facilitating automatic-guided image capturing and presentation. In some embodiments, the method includes capturing an image of an item, removing a background of the image frame, performing manual mask editing, generating an item listing based on the manually edited mask, inferring item information from the image frame and applying the inferred item information to an item listing.

A system and method for determining a dimension of a target. The method includes: determining a camera parameter, the camera parameter including at least one of a focal length, a yaw angle, a roll angle, a pitch angle, or a height of one or more cameras; acquiring a first image and a second image of an target captured by the one or more cameras; generating a first corrected image and a second corrected image by correcting the first image and the second image; determining a parallax between a pixel in the first corrected image and a corresponding pixel in the second corrected image; determining an outline of the target; and determining a dimension of the target based at least in part on the camera parameter, the parallax, and the outline of the target.