Abstract: A pair of input images acquired using a first modality and a second modality is processed using a multi-classifier trained to determine classification data indicative of whether the pair is normal or abnormal. A pair may be deemed abnormal if one or both input images are obscured or inconsistent with one another. Training data comprising normal and abnormal images are used to train the multi-classifier. During training, the multi-classifier uses an objective function that includes cross entropy loss, distance loss, and discrepancy loss to process the training data. During use, the trained multi-classifier processes a pair of input images. If the resulting classification data indicates the pair of input images are normal, the pair of input images may be processed to assert an identity.

Abstract: A system comprises a picture and metadata captured by a content capture system; a recognizable characteristic datastore configured to store recognizable characteristics of different users; a module configured to identify a time and a location associated with the picture based on the metadata, and to identify one or more potential target systems within a predetermined range of the location at the time; a characteristic recognition module configured to retrieve the recognizable characteristics of one or more potential users associated with the potential target systems, and evaluate whether the picture includes one or more representations of at least one actual target user from the potential users based on the recognizable characteristics of the potential users; a distortion module configured to distort a feature of the representations of the least one actual target user in response to the determination; a communication module configured to communicate the distorted picture to a computer network.

Abstract: A method and system are used for managing and classifying electronic document images. Each of the electronic document images is divided into an array of image segments. The method extracts image features from each of the image segments to obtain numerical coefficients for each of the image segments. The numerical coefficients are compared with each other to generate sub-codes. A classification code is determined as a combination of the sub-codes. The classification codes of a plurality of electronic document images can be stored in a database for further analysis. Based on the classification codes, similarity rates between at two document images can be determined.

Abstract: In order to perform a discrimination calculation using a small-capacity storage unit, a discrimination calculation apparatus comprises a feature calculation unit configured to sequentially calculate a feature of discrimination target data for each hierarchical layer, a discrimination calculation unit configured to sequentially perform a partial discrimination calculation on the discrimination target data using the feature sequentially calculated by the feature calculation unit and store a result of the partial discrimination calculation in a discrimination result storage unit, and a control unit configured to control the discrimination calculation unit to perform a next partial discrimination calculation using the feature sequentially calculated by the feature calculation unit and the result of the partial discrimination calculation stored in the discrimination result storage unit and to store a result of the next partial discrimination calculation in the discrimination result storage unit.

Abstract: Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines.

Abstract: The present invention belongs to the field of computer visual detection, and relates to a binocular automatic gear pitting detection device based on deep learning, comprising a gearbox system, a data acquisition system, an image processing system, a tooth surface positioning system, a control system and a motor, wherein the gearbox is used for installing paired meshing gears; the data acquisition system is arranged on the side wall of the gearbox, and a CCD industrial camera is arranged on the data acquisition system; the image processing system completes quantitative evaluation of gear pitting and target detection based on a deep learning technology; both ends of the tooth surface positioning system are respectively connected with the motor and the gearbox, and the torque of the motor is transmitted to an input shaft of the gearbox.

Abstract: The invention relates to a method for image integration (100), said method comprising the steps of acquiring at least two overlapping images (110) having different properties, and forming at least one integrated image (170) based on the overlapping images (110), wherein the step of forming at least one integrated image (170) comprises generating at least one score map (130) for each acquired overlapping image (110) relating to locally salient features of said overlapping image (110), forming an integration map (150) based on the at least one score map (130) of the at least two overlapping images (110), and integrating the at least two overlapping images (110) based on the integration map (150) to form an integrated image (170).

Abstract: A method for processing a three-dimensional (3D) image includes acquiring a frame of a color image and a frame of a depth image, and generating a frame by combining the acquired frame of the color image with the acquired frame of the depth image. The generating of the frame includes combining a line of the color image with a corresponding line of the depth image.

Abstract: The invention relates to a computer implemented method for associating objects in a video comprising subsequent frames, the method comprising obtaining first object proposal region information of a previous frame, determining second object proposal region information of a current frame, wherein the first and second object proposal region information are at least indicative of an appearance measure, a spatial location and a detection probability of each object proposal region of the respective frame, associating objects in the video by at least associating a first set of object proposal regions of the previous frame to a second set of object proposal regions of the current frame, wherein the object proposal regions are associated using distance measures calculated based on the appearance measures, the spatial locations and the detection probabilities.

Abstract: Systems and methods for optical character recognition using specialized confidence functions. An example method comprises: receiving a grapheme image; computing a feature vector representing the grapheme image in a space of image features; and computing a confidence vector associated with the grapheme image, wherein each element of the confidence vector reflects a distance, in the space of image features, between the feature vector and a center of a class of a set of classes.

Abstract: The present disclosure describes human body attribute recognition methods and apparatus, electronic devices, and a storage medium. The method includes acquiring a sample image containing a plurality of to-be-detected areas being labeled with true values of human body attributes; generating, through a recognition model, a heat map of the sample image and heat maps of the to-be-detected areas to obtain a global heat map and local heat maps; fusing the global and the local heat maps to obtain a fused image, and performing human body attribute recognition on the fused image to obtain predicted values; determining a focus area of each type of human body attribute according to the global and the local heat maps; correcting the recognition model by using the focus area, the true values, and the predicted values; and performing, based on the corrected recognition model, human body attribute recognition on a to-be-recognized image.

Abstract: An automatic generation system of a training image and a method thereof are provided. The disclosure generates a training image and records the target category and the target position. The disclosure adds the target image to the container image as a candidate image, calculates a reliability of the candidate image, and repeatedly executes the process until the reliability of the candidate image meets a threshold condition for generating the training image. The disclosure is able to generate the training images automatically, and the recognition difficulty of the training image is adjustable by the user, so as to be suitable for customized recognition training.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a generative adversarial network (GAN) to reconstruct a missing portion of an image by determining a reconstructed portion of the image based on data from portions of the image surrounding the missing portion and compare an acquired image with the reconstructed portion of the image to determine a damaged portion. The instructions further include instructions to determine estimated damage based on the damaged portion.

Abstract: Multilayered product structures are formed on substrates by a combination of patterning steps, physical processing steps and chemical processing steps. An inspection apparatus illuminates a plurality of target structures and captures pupil images representing the angular distribution of radiation scattered by each target structure. The target structures have the same design but are formed at different locations on a substrate and/or on different substrates. Based on a comparison of the images the inspection apparatus infers the presence of process-induced stack variations between the different locations. In one application, the inspection apparatus separately measures overlay performance of the manufacturing process based on dark-field images, combined with previously determined calibration information. The calibration is adjusted for each target, depending on the stack variations inferred from the pupil images.

Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

Abstract: An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.

Abstract: A multimodal system is provided for managing safety in an industrial environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) generate label data using the location data and identity about one or more objects generated by the real-time locating component, and (ii) update one or more predictive models utilized by the component vision component or the multimodal system using the label data.

Abstract: An information processing apparatus includes a processor configured to infer an object represented in image data to be processed, the object being inferred by using a learning model for inferring the object represented in the image data, and perform image processing on the image data with a correction level of the image processing for correcting the image data being varied on a basis of a probability of correctness in inference of the object.

Abstract: An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.