According to an exemplary embodiment, a method for pre-cropping digital image data includes: dividing the digital image into segments; computing a color value distance between corresponding pixels of neighboring segments of the digital image; comparing the color value distance(s) against a minimum color distance threshold; clustering neighboring segments having a color value distance less than or equal to the minimum color distance threshold; computing a connected structure based on the clustered segments; computing a polygon bounding the connected structure; comparing a fraction of segments included in the connected structure and the polygon, relative to a total number of segments in the digital image, to a minimum included segment threshold; and in response to determining the fraction of segments in the connected structure and the polygon, relative to the total number of segments meets or exceeds a minimum included segment threshold, cropping the digital image based on edges of the polygon.

A system for validating installation correctness of a component in a test assembly includes a housing having a platform including a tiered surface. The tiered surface forms an abutment surface configured as a stop against which a test assembly is abutted. A plurality of cameras is positioned to capture different views of the test assembly. A processing device is configured to execute instructions to capture an image from each of the plurality of cameras of the test assembly that includes a plurality of components. Each of the plurality of components is analyzed within each image of the plurality of images. A matching score is determined and an indication of whether the plurality of components was correctly installed in the test assembly is generated.

Computer-implemented methods for detecting objects within digital image data based on color transitions include: receiving or capturing a digital image depicting an object; sampling color information from a first plurality of pixels of the digital image, wherein each of the first plurality of pixels is located in a background region of the digital image; assigning each pixel a label of either foreground or background using an adaptive label learning process; binarizing the digital image based on the labels assigned to each pixel; detecting contour(s) within the binarized digital image; and defining edge(s) of the object based on the detected contour(s). Corresponding systems and computer program products configured to perform the inventive methods are also described.

Disclosed herein are systems, methods, and devices for using adaptive learning to identify objects. An object-identifying device performs a first object identification based on one or more features of a first modality of an object retrieved from an image frame including the object and a first database including first modality identification features. A second object identification is performed based on one or more features of a second modality of the object retrieved from the image frame and a second database including second modality identification features. The second database is updated by adaptively learning a new second modality identification feature according to a first identification result of the first object identification. The second object identification is trained with the updated second database and determines a final identification result by integrating a first identification result of the first object identification and a second identification result of the second object identification.

An information processing apparatus acquires video data that includes target objects including a person and an object, and identifies a relationship between the target objects in the acquired video data, by using graph data that indicates a relationship between target objects and that is stored in a storage. The information processing apparatus identifies a behavior of the person in the video data by using a feature value of the person included in the acquired video data. The information processing apparatus predicts one of a future behavior and a future state of the person by inputting the identified behavior of the person and the identified relationship to a machine learning model.

An information processing apparatus acquires video image data that includes target objects including a person and an object, and specifies, by inputting the acquired video image data to a first machine learning model, a relationship between each of the target objects included in the acquired video image data. The information processing apparatus specifies, by using a feature value of the person included in the acquired video image data, a behavior of the person included in the video image data. The information processing apparatus predicts, by inputting the specified behavior of the person and the specified relationship to a probability model, a future behavior or a future state of the person.

An image recognition apparatus is provided which comprises a first extracting means for extracting, from every registration image previously registered, a set of registration partial images of a predetermined size, and a second extracting means for extracting, from an input new image, a set of new partial images of a predetermined size. The apparatus further comprises a discriminating means for discriminating an attribute of the new partial image based on a rule formed by dividing the set of the registration partial images extracted by the first extracting means, and a collecting means for deriving a final recognition result of the new image by collecting discrimination results by the discriminating means at the time when the new partial images as elements of the set of the new partial images are input.

Aspects of the present disclosure involve systems, methods, devices, and the like for user identification using Artificial Intelligence, Machine Learning, and data analytics. In one embodiment, a verification system and method is introduced that can provide user authentication using parallel modeling for face identification. The verification system used includes a face identification module for use in the identification and verification using parallel processing of a received image with a claimed identity. The parallel processing includes an ensemble of machine learning models processed in parallel for optimal performance.

A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

This application discloses a method and apparatus for improving video target detection performance in surveillance edge computing. This application relates to the technical field of digital image processing. The method includes: determine the size of multiple rectangular sliding windows for scanning according to the input size of the object detection neural network algorithm and the size of the original input image; when each frame is detected, the original input image and the sub-images in each rectangular sliding window are scaled in different proportions; the resolution of the scaled original input image is lower than that of the scaled sliding window sub-images; stitching the scaled images into a rectangular image and using it as a detection input image; the detection is performed by an object detection neural network algorithm corresponding to the size of the detection input image.