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.

The present disclosure relates to an image processing method and apparatus. The method comprises: performing face and body feature extraction on an image to be processed to obtain image features including face features and/or body features, and the image to be processed comprises a first image and a second image; performing a face clustering operation, according to the face features extracted from the first image, to obtain a face clustering result; performing a body clustering operation on the second image to obtain a body clustering result, according to the face clustering result and the body features extracted from the first and second images, no face feature has been extracted from the second image; and obtaining, according to the face clustering result and the body clustering result, a clustering result for the image to be processed. The method can improve the recall rate while ensuring the accuracy of the clustering result.

A computer-implemented method for detecting fraudulent behavior in a facial recognition process includes: receiving, by a computing device, a facial recognition request from a user; collecting bypass information of the user, in which the bypass information includes user device information and user behavior information; inputting the bypass information into at least one decision model to obtain a bypass decision result; and determining, based on the bypass decision result, whether fraudulent behavior is present in the facial recognition process.

The present invention relates to a method by which a computing means monitors a harbor, and a harbor monitoring method, according to one aspect of the present invention, comprises the steps of: acquiring a harbor image; generating a segmentation image corresponding to the harbor image; generating a display image corresponding to the harbor image and having a first view attribute; generating a conversion segmentation image, which corresponds to the segmentation image and has a second view attribute different from the first view attribute; matching the display image so as to generate a panoramic image; matching the conversion segmentation image so as to generate a matching segmentation image; calculating ship mooring guide information on the basis of the matching segmentation image; and outputting the mooring guide information together with the panoramic image.

A system and method are provided for classifying objects in spatial data using a machine learned model, as well as a system and method for training the machine learned model. The machine learned model may comprise a content sensitive classifier, a location sensitive classifier and at least one outlier detector. Both classifiers may jointly distinguish between objects in spatial data being in-distribution or marginal-out-of-distribution. The outlier detection part may be trained on inlier examples from the training data, while the presence of actual outliers in the input data of the machine learnable model may be mimicked in the feature space of the machine learnable model during training. The combination of these parts may provide a more robust classification of objects in spatial data with respect to outliers, without having to increase the size of the training data.

An information processing device on a server side includes: a predetermined number of recognition units, for which a model updated by performing image recognition in a predetermined number of vehicles and executing unsupervised learning is each set, configured to perform image recognition on an image, on which image recognition has been performed in a predetermined number of the vehicles; and an evaluation value calculation unit configured to evaluate recognition results obtained in a predetermined number of the recognition units and calculate an evaluation value for each of the recognition units. The information processing device on the vehicle side includes an execution unit that executes unsupervised learning, and a determination unit that determines whether learning has been performed correctly or not for a model updated in the execution unit on the basis of an evaluation value found on the server side.

A face liveness recognition method includes: obtaining a target image containing a facial image; extracting facial feature data of the facial image in the target image; performing face liveness recognition according to the facial feature data to obtain a first confidence level using a first recognition model, the first confidence level denoting a first probability of recognizing a live face; extracting background feature data from an extended facial image, the extended facial image being obtained by extending a region that covers the facial image; performing face liveness recognition according to the background feature data to obtain a second confidence level using a second recognition model, the second confidence level denoting a second probability of recognizing a live face; and according to the first confidence level and the second confidence level, obtaining a recognition result indicating that the target image is a live facial image.

An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

The method includes receiving digital images of tissue samples of patients, the images having assigned a label indicating a patient-related attribute value; splitting each received image into a set of image tiles; computing a feature vector for each tile; training a Multiple-Instance-Learning program on all the tiles and respective feature vectors for computing for each of the tiles a numerical value being indicative of the predictive power of the feature vector associated with the tile in respect to the label of the tile's respective image; and outputting a report gallery including tiles sorted in accordance with their respectively computed numerical value and/or including a graphical representation of the numerical value.

An object detection system includes at least one memory storing instructions, and at least one processor that, upon executing instructions stored in the memory, controls the object detection system to perform functions including inputting a first video frame and a second video frame from a camera; generating a first set of predicted object locations using only one of the first video frame and the second video frame; generating a second set of predicted object locations based on pixel differences between the first video frame and the second video frame; and determining a final set of object locations based on the first set of predicted object locations and the second set of predicted object locations.