Abstract: A picture search method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product, relating to the field of artificial intelligence that can obtain an OCR result of pictures in a preset picture library in response to a picture search request; traverse pictures which are not subjected to low-dimensional OCR processing and high-dimensional OCR processing in the preset picture library, and perform the low-dimensional OCR processing based on an OCR threshold on each of the traversed pictures to obtain a low-dimensional OCR result of each corresponding picture; determining a target picture matching a key character string in the preset picture library according to at least one of the low-dimensional OCR result and the high-dimensional OCR result of each picture; and determining the target picture as a search result of the picture search request, and displaying the search result.

Abstract: A method is described for processing data retrieved from two image capturing sensors, by filling missing disparity values of pixels included in holes comprised in the retrieved data.

Abstract: A lane detection method integratedly using image enhancement and a deep convolutional neural network. On the assumption that lanes have similar widths in a local region of an image and a lane can be segmented into several image blocks, each of which contains lane marking in the center, a method based on a deep convolutional neural network is provided to detect lane marking blocks in the image. Input to the model includes road images captured by a camera as well as a set of enhanced images generated by the contrast limited adaptive histogram equalization (CLAHE) algorithm. The method according to the present disclosure can effectively overcome difficulties of lane detection under complex imaging conditions, such as poor image quality, and small lane marking targets, so as to achieve better robustness.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Composite sensor data can be generated based on information from multiple exposures captured at different exposure times. In examples, a first depth determination technique is applied to a first subset of pixels and a second depth determination technique is applied to a second subset of the pixels. The first subset of pixels may correspond to pixels that are associated with a highly-reflective surface.

Abstract: The present disclosure discloses a person re-identification method of integrating global features and ladder-shaped local features and a device thereof, comprising: extracting the image to be identified and the image-library image by using a pre-trained person re-identification network model, respectively, to obtain the person feature of the image to be identified and the person feature of the image-library image; matching the similarity between the person feature of the image to be identified and the person feature of the image-library image, and outputting the person images with the top N similarity as the person re-identification result; wherein a person re-identification network comprises a backbone network, an improved global feature branch and a ladder-shaped local feature extraction branch guided by a block weight; using a public data set to train the person re-identification network, and obtaining the trained person re-identification network model.

Abstract: An image processing apparatus, including a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to obtain similarity information indicating a similarity between each pixel of a plurality of pixels included in a first image and an adjacent pixel of the each pixel; generate a weight map including weight information corresponding to the each pixel, based on the similarity information; generate a spatially variant kernel including a plurality of kernels corresponding to the plurality of pixels, based on the weight map and a spatial kernel including weight information based on a location relationship between the each pixel and the adjacent pixel; and generate a second image by applying the spatially variant kernel to the first image.

Abstract: The present disclosure provides an image compression apparatus, an image compression method. The apparatus comprises: an image division module configured to divide a target image into one or more macro blocks; a macro block partitioning module configured to partition each of the macro blocks; a component determination module configured to determine, for each partition, a first component and a second component in each color channel; a color index determination module configured to determine, for each pixel, a color index of the pixel in a corresponding partition according to the first component and the second component; and a compressed code stream obtaining module configured to obtain a preset image compression ratio, and compress the macro block according to the number of partitions of the macro block, the first component and the second component and color indexes of all pixels to obtain a compressed code stream.

Abstract: Disclosed is a method for self-supervised reinforcement learning (RL) by analogy executed by a computer device, the method including configuring a self-supervised RL with analogical reasoning (SRAR) model; and learning a policy for problem solving in a situation in which a task domain changes using the configured SRAR model.

Abstract: A computer-implemented method for analysing the relevance of visual parameters for training a computer vision model. Upon adjusting the set of visual parameters to increase their relevance, a new set of visual data and corresponding groundtruth results that can be used in (re)training and/or testing the computer vision model.

Abstract: This application relates to systems and methods for identifying anomalous interactions. Interaction data representative of an interaction is received and the interaction is classified as one of an anomalous interaction or a benign interaction using an anomaly detection model. The anomaly detection model is configured to identify a similarity between the interaction data and known benign interactions. An indication of authorization is generated based on the classification of the interaction. The indication of authorization authorizes the interaction when the anomaly detection model classifies the interaction as benign and the indication of authorization denies the interaction when the anomaly detection model classifies the interaction as anomalous.

Abstract: Embodiments of the present application provide a method for denoising videos. The method comprises: acquiring, by the CPU, video frame images by acquiring video data and decoding the video data; loading, by the GPU, the video frame images from the CPU; acquiring, by the GPU, first images by denoising the video frame images using a predetermined non-local means (NLM) denoising algorithm; acquiring, by the GPU, second images by denoising the first images using a predetermined non-local Bayes (NLB) denoising algorithm; and acquiring, by the CPU, denoised video data by acquiring the second images from the GPU and encoding the second images.

Abstract: Embodiments of the present invention disclose a binocular image matching method, apparatus, device, and storage medium. The method comprises: performing target detection on a first image to obtain a first bounding box of a target in the first image; determining a second bounding box corresponding to the first bounding box in a second image; and obtaining a third bounding box of the target in the second image by regressing the second bounding box. The technical solutions realize accurate matching of targets in a binocular image without requiring performing target detection on both images in the binocular image, and then use a matching algorithm to match the targets detected in the two images, thereby greatly reducing the calculation overhead of target matching in the binocular image.

Abstract: A pole classing system may include an array of three-dimensional (3D) scanners, programmable logic controller equipment, and a computing device. The computing device may control the array of the 3D scanners to generate images of a pole from different directions and positions, generate a first pole dataset comprising dimensions and features of the pole, determine a class for the pole based on the dimensions of the pole and a first set of pole standard parameters, generate a second pole dataset by selecting a partial first pole dataset, and transmit the second pole dataset to the programmable logic controller equipment. The programmable logic controller equipment may process, based on a second set of pole standard parameters, the second pole dataset, the images and the features of the pole to thereby optimize the determined class of the pole and generate an updated class of the pole.

Abstract: Aspects of the disclosure are directed to the field of artificial intelligence technologies and provides a method and an apparatus for obtaining a feature of duct tissue based on computer vision, an intelligent microscope, a storage medium, and a computer device. The method can include the steps of obtaining an image including duct tissue, determining, in an image region corresponding to the duct tissue in the image, at least two feature obtaining regions adapted to duct morphology of the duct tissue, obtaining cell features of cells of the duct tissue in the feature obtaining regions respectively, and obtaining a feature of the duct tissue based on the cell features of the cells of the duct tissue in the feature obtaining regions respectively.

Abstract: A 6D (six degree of freedom) pose measurement method for mechanical parts based on virtual contour feature points, wherein multiple lights are used for lighting in different times for photographing, so that the success rate of recognition of geometric features in images is increased.

Abstract: An apparatus for labeling an object includes a processor and a memory. The processor creates a synthetic three-dimensional (3D) modeling environment scene, generates image data synthetically generated by an in-flight camera simulation, the image data being within the 3D modeling environment scene based on an orientation of a camera and including one or more objects; uses a mask to identify the one or more objects in the 3D modeling environment scene, labels the identified one or more objects using a cursor on target (COT) lookup table, and stores the labeled identified one or more objects and flight metadata in a database as part of a training dataset to thereby train an artificial intelligence (AI) system. The AI system identifies a real object corresponding to the label of the one or more identified one or more objects in the COT lookup table. The real object is a real-world, target object.

Abstract: A pointer positioning method, an apparatus, and an instrument, relates to the technical field of instrument. The method comprises: on the condition that a virtual pointer rotates in a first dial image at a first preset angle, detecting quantities of first pixel points corresponding to a plurality of first rotation positions; according to the quantities of the first pixel points corresponding to the plurality of first rotation positions, determining a first target position where the virtual pointer is located; according to the first target position, determining a target angle value between a target pointer in an instrument dial corresponding to the first dial image and a reference position; and according to the target angle value, searching a target scale value corresponding to the target pointer from a corresponding relationship between preset angle values and scale values.

Abstract: A method for lane boundary detection, the method may include obtaining an image of an environment of a vehicle, the environment comprises at least one lane boundary portions; wherein the image comprises a first plurality of two dimensional (2D) image segments; converting, by a first machine learning process, each of the 2D segments to a segment vector to provide a first plurality of segment vectors; wherein each segment vector represents a 2D segment; finding an associated cluster for each segment vector to provide a second plurality of associated clusters; searching for at least one LBR cluster of the second plurality of associated clusters; and determining, for each LBR segment vector and by a second machine learning process, a location of a lane boundary portion within a 2D image segment that is represented by the LBR segment vector; wherein a LBR segment vector has an associated cluster that is a LBR cluster.

Abstract: Provided is an image and an inspection system that make it possible to effectively utilize a result of past inspection. An audit position indicating a position where an audit is planned or a position where an audit is completed and a past audit position indicating a position where an audit was performed in the past within a predetermined range including the audit position are displayed over a map on a user terminal, the map being a drawing representing a configuration of a manufacturing site. The inspector references the map on the user terminal and effectively utilizes a past audit result to perform an audit.

Abstract: A model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.