Abstract: The present application discloses a method for inspecting a battery tab, the method including: obtaining a sectional view of a plurality of layers of tabs of a battery; identifying and analyzing the sectional view to obtain a plurality of connected domains, where each connected domain includes one tab or a plurality of tabs that are bonded with each other; determining, based on positions and a number of intersection points of tab bonding in each connected domain, a number of layers of tabs corresponding to the connected domain; calculating a total number of layers of the plurality of layers of tabs in the sectional view based on the number of layers of tabs corresponding to the connected domain; and determining, based on the total number of layers of tabs and a preset real number of layers of tabs, whether the plurality of layers of tabs are folded.

Abstract: A method for training an image classification model according to an embodiment includes training a feature extractor and a rotation angle classifier to predict a rotation angle of each of unlabeled first training images, training the image classification model to predict a label and rotation angle of each of labeled second training images, but predict a uniform label even though an actual rotation angle of each of the second training images is changed, generating a pseudo label based on a training image that satisfy a preset condition among unlabeled candidate images, and training the image classification model to predict a rotation angle of each of the third training images, and predict a label of each of the third training images based on the pseudo label, but predict a uniform label even though an actual rotation angle of each of the third training images is changed.

Abstract: Knowledge distillation method for fracture detection includes obtaining medical images including region-level labeled images, image-level diagnostic positive images, and image-level diagnostic negative images, in chest X-rays; performing a supervised pre-training process on the region-level labeled images and the image-level diagnostic negative images to train a neural network to generate pre-trained weights; and performing a semi-supervised training process on the image-level diagnostic positive images using the pre-trained weights. A teacher model is employed to produce pseudo ground-truths (GTs) on the image-level diagnostic positive images for supervising training of a student model, and the pseudo GTs are processed by an adaptive asymmetric label sharpening (AALS) operator to produce sharpened pseudo GTs to provide positive detection responses on the image-level diagnostic positive images.

Abstract: Disclosed are an image processing method, an image processing device, a neutral network and a training method thereof, and a storage medium. The image processing method includes: obtaining an input image; performing a segmentation process on the input image via a first encoding-decoding network, to obtain a first output feature map and the first segmented image; concatenating the first output feature map with at least one selected from the group consisting of the input image and the first segmented image, to obtain an input of the second encoding-decoding network; and performing a segmentation process on the input of the second encoding-decoding network via a second encoding-decoding network, to obtain the second segmented image. And the first encoding-decoding network and the second encoding-decoding network forms a neural network.

Abstract: Object recognition scanning systems and methods for implementing artificial intelligence based item determination are disclosed herein. Example object recognition scanning systems and methods include imaging, by an imager having a field of view (FOV) extending over a scanning area, one or more items within the FOV, and receiving image data of an item imaged by the imager during a scanning session. A trained object recognition model, taking the image data as input, determines a product identification probability for the item. The object recognition scanning systems and methods include executing one of: (a) a first decoding sequence including, (b) a second decoding sequence, or (c) a mismatch detection sequence.

Abstract: The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

Abstract: An information processing apparatus comprises a control unit configured to set a shift amount based on a bit width of data, for each layer of a network including a plurality of layers, a plurality of MAC (multiply-accumulate) units configured to execute MAC operations on a plurality of data and a plurality of filter coefficients of the layer, a plurality of shift operation units configured to shift a plurality of MAC operation results obtained by the plurality of MAC units based on the shift amount, and an adding unit configured to calculate a total sum of the plurality of MAC operation results shifted by the plurality of shift operation units.

Abstract: A method for an image processing circuit includes steps of: receiving a fingerprint image; performing a low-pass filtering on the fingerprint image to remove a moiré signal on the fingerprint image, to generate a filtered image; and performing a data binning on the filtered image to generate an output image.

Abstract: A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Abstract: Methods, systems and apparatuses, including computer programs encoded on computer storage media, are provided for generating prediction models related to targeting and acquiring customers. Thousands of variables of historical data, including data for prospects and external data, are used to train the prediction models. The variables are pre-processed, then sensitivity analysis is performed on the input variables with respect to the target. The variables with the most influence on the target are selected and added to the feature set used for training a prediction model.

Abstract: Provided herein are methods and systems for improved core sample analysis. At least one image of a core sample may be analyzed to determine structural data associated with the core sample (e.g., attributes of the core sample). A machine learning model may analyze the at least one image and determine one or more attributes associated with the core sample. The machine learning model may generate a segmentation mask. An output image may be generated. A user may interact with the output image and provide one or more user edits. The one or more user edits may be provided to the machine learning model for optimization thereof.

Abstract: A device and a method for classification using a pre-trained classification model and a computer readable storage medium are provided. The device is configured to extract, for each of multiple images in a target image group to be classified, a feature of the image using a feature extraction layer of the pre-trained classification model; calculate, for each of the multiple images, a contribution of the image to a classification result of the target image group using a contribution calculation layer of the pre-trained classification model; aggregate extracted features of the multiple images based on calculated contributions of the multiple images, to obtain an aggregated feature as a feature of the target image group; and classify the target image group based on the feature of the target image group.

Abstract: The invention relates to techniques for material testing of optical test pieces, for example of lenses. Angle-variable illumination, using a suitable illumination module, and/or angle-variable detection are carried out in order to create a digital contrast. The digital contrast can be, for example, a digital phase contrast. A defect detection algorithm for automated material testing based on a result image with digital contrast can be used. For example, an artificial neural network can be used.

Abstract: Systems and methods for target inspection are provided. The system includes a camera, at least one sensor, and a controller. The camera acquires images of a target over time and the sensor(s) acquire motion data characterizing camera and target movement. The controller generates, using a first computer vision (CV) algorithm, an initial prediction and confidence level regarding an object of interest for a first image acquired at a first time. The controller also determines, using the motion data, a motion parameter characterizing relative motion between the camera and the target at the first time. The controller additionally receives a weighting based upon a second image acquired at a second time prior to the first time. The controller generates, using a second CV algorithm, a final prediction and confidence level for the first image based upon the first image, the initial prediction and confidence level, the motion parameter, and the weighting.

Abstract: A computerized apparatus for creating a highlight video, including an interface for charting an event by recording information in regard to the event, where the event information is associated with time codes. A camera captures video of the event, where the video is associated with the time codes. The interface also creates triggers including lists of at least one action. An input/output uploads the event information, video, and triggers to a server. A processor produces entries in an action log from the event information, compares the entries in the action log to the triggers to determine if any of the entries in the action log match any of the triggers, collects the time codes of those entries that match the triggers, creates video clips from the video of the event, based on the collected time codes, and compiles the video clips into the highlight video.

Abstract: An in-stream disparity processing system comprises a delay block having an input for receiving an input stream of disparity cost vectors, and configured to provide a delayed stream of disparity cost vectors at an output of the delay block, by delaying the input stream by a predetermined amount; and a processing block having a cost input connected to receive the delayed stream of disparity cost vectors and a smoothing input connected to receive the input stream of disparity cost vectors, and configured to apply cost smoothing to the delayed stream based on the input stream, so as to generate, at an output of the processing block, a stream of reverse pass disparity cost vectors.

Abstract: An apparatus includes a storage control unit that divides, into two-dimensional blocks, a feature image of a layer and stores the respective blocks in any one of a predetermined number of memories, a unit that determines a pattern for reading blocks from the memories based on information relating to an operation on the feature image, and a unit that reads blocks from the memories in accordance with the pattern. The storage control unit assigns repeatedly, for the two-dimensional blocks, the memories in a predetermined order from a head row/column along a row/column. In a second or a subsequent row/column, a memory, which the assignment is started, is shifted by a constant number from a previous row/column in the predetermined order.

Abstract: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container includes capturing a plurality of images of the specimen container from multiple viewpoints, stacking the multiple viewpoint images along a channel dimension into a single stacked input, and processing the stacked input with a single deep convolutional neural network (SDNN). The SDNN includes a segmentation convolutional neural network that receives the stacked input and outputs multiple label maps simultaneously. The SDNN also includes a classification convolutional neural network that processes the multiple label maps and outputs an HILN determination (Hemolysis, Icterus, and/or Lipemia, or Normal) of the serum or plasma portion of the specimen. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: It is provided a recognition device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging, including a bracket, a camera mounted on a top of the bracket, a plurality of light sources for illumination, and a processor connected with the camera. The processor receives a shrimp image collected by the camera, extracts an edge image of the shrimp after the collected shrimp image is preprocessed, and calculates a movement speed of the shrimp and a quantity of baits after a head and a tail are recognized, to recognize starvation extent of the shrimp. It is also provided a recognition method for analyzing starvation extent of the whiteleg shrimp based on underwater imaging. The present disclosure may guide feeders to feed and realize reasonable culture for the shrimp, by capturing images of the shrimp in time and determining starvation the extent of shrimp.