A workpiece image search apparatus includes: a workpiece image deformation unit that generates a third workpiece image by deforming a second workpiece image so that a difference in workpiece shape between a first workpiece image and the second workpiece image becomes smaller, wherein the first workpiece image is obtained by projecting a first workpiece shape of a first workpiece on a two-dimensional plane, and the second workpiece image is obtained by projecting a second workpiece shape of a second workpiece on a two-dimensional plane; and a similarity calculation unit that calculates a similarity between the first workpiece shape and the second workpiece shape by comparing the third workpiece image with the first workpiece image.

In implementations of the subject matter as described herein, there is provided a method for forgery detection of a face image. Subsequent to inputting a face image, it is detected whether a blending boundary due to the blend of different images exists in the face image, and then a corresponding grayscale image is generated based on a result of the detection, where the generated grayscale image can reveal whether the input face image is formed by blending different images. If a visible boundary corresponding to the blending boundary exists in the generated grayscale image, it indicates that the face image is a forged image; on the contrary, if the visible boundary does not exist in the generated grayscale image, it indicates that the face image is a real image.

A method and system for automated deformable registration of an organ from medical images includes generating segmentations of the organ by processing a first and second series of images corresponding to different organ states using a first trained CNN. A second trained CNN processes the first and second series of images and the segmentations to deformably register the second series of images to the first series of images. The second trained CNN predicts a displacement field by minimizing a registration loss function, where the displacement field maximizes colocalization of the organ between the different states.

A computer device extracts a plurality of target windows from a target video. Each of the target windows comprises a respective plurality of consecutive video frames. For each of the target windows, the device performs action recognition on the respective plurality of consecutive video frames corresponding to the target window to obtain respective first action feature information of the target window. The device obtains a similarity between the first action feature information of the target window and preset feature information. The device determines, from the respective obtained similarities corresponding to the plurality of target windows, a highest first similarity and a first target window corresponding to the highest first similarity. The device also determines a dynamic action corresponding to the highest first similarity as the preset dynamic action in accordance with threshold settings.

Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

A prediction system for simulating effects of a real-world event can be used for autonomous driving. In operation, the system receives input data regarding a complex system (e.g., roadways) and various real-world events. A full-scale network is constructed of the complex system, such that nodes represent road intersections and edges between nodes represent road segments linking the road intersections. The network is reduced is scaled down to generate a multi-layer model of the complex system. Each layer in the model is simulated to identify equilibrium flows, with the model thereafter destabilized by applying stimuli to reflect the real-world event. An autonomous vehicle can then be caused to chart and traverse a road path based on road segments and intersections that are least affected by the real-world event.

This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

A method for identifying authenticity of an object, the method includes maintaining, in an identification server system, a reference image of an original object, the reference image and provided to represent all equivalent original objects, receiving, in the identification server system, one or more input images of the object to be identified, and generating, by the identification server system, a target image from the one or more input images. The method further includes aligning, by the identification server system, the target image with the reference image and analysing, by the identification server system, the target image in relation to the aligned reference image for identifying authenticity of the object.

A computer system that computes second tractography results is described. This computer may include: a computation device (such as a processor, a graphics processing unit or GPU, etc.) that executes program instructions; and memory that stores the program instructions. During operation, the computer system receives information specifying tractography results that specify a set of neurological fibers. Then, the computer system computes, using a predetermined (e.g., pretrained) autoencoder neural network, the second tractography results that specify a second set of neurological fibers based at least in part on the tractography results and information associated with a neurological anatomical region. For example, a subset of the set of neurological fibers may be anatomically implausible and the second set of fibers may exclude the subset. Note that the predetermined autoencoder neural network may be trained using an unsupervised-learning technique.

Disclosed are a vehicle and a control method thereof configured for performing a vehicle control to detect lighting irradiated from a lamp at the time of vehicle parking and park the vehicle safely and accurately. The vehicle includes a lamp configured to irradiate lighting to a ground, a camera configured to photograph the lighting irradiated to the ground to obtain information on a form of the lighting of the lamp, and a controller configured to determine at least one of the form of the ground to which the lighting is irradiated and whether or not an obstacle exists on the ground to which the lighting is irradiated, based on the information on the obtained form of the lighting of the lamp.