Abstract: In order to perform quantitative analysis on an object in an image, it is important to accurately identify the object, but when plural objects are in contact with each other, it is potential that a target portion cannot be accurately identified. An image is segmented into a foreground region and a background region, the foreground region being a region in which an object for which quantitative information is to be calculated is shown, and the background region being a region other than the foreground region. With respect to a first object and a second object in contact with each other in the image, a contact point between the first object and the second object is detected based on a region segmentation result output by a segmentation unit.

Abstract: This application discloses a point cloud encoding method and apparatus, a point cloud decoding method and apparatus, and a storage medium for point cloud encoding and/or decoding, and belongs to a data processing field. The method includes: first obtaining auxiliary information of a to-be-encoded patch, and then encoding the auxiliary information and a first index of the to-be-encoded patch into a bitstream. Values of the first index may be a first value, a second value, and a third value. Different values indicate different types of patches. Therefore, different types of patches can be distinguished by using the first index. For different types of patches, their corresponding auxiliary information encoded into a bitstream may comprise different contents. This can simplify a format of information encoded into the bitstream, reduce bit overheads of the bitstream, and improve encoding efficiency.

Abstract: Systems, apparatuses and methods may provide for technology that processes an inference workload in a first subset of layers of a neural network that prevents or inhibits data dependent branch operations, conducts an exit determination as to whether an output of the first subset of layers satisfies one or more exit criteria, and selectively bypasses processing of the output in a second subset of layers of the neural network based on the exit determination. The technology may also speculatively initiate the processing of the output in the second subset of layers while the exit determination is pending. Additionally, when the inference workloads include a plurality of batches, the technology may mask one or more of the plurality of batches from processing in the second subset of layers.

Abstract: A method for counting suckling piglets based on self-attention spatiotemporal feature fusion is disclosed, which includes: detecting a side-lying sow in a video frame by using CenterNet to acquire a key frame of suckling piglets and a region of interest of the video frame, and overcome the interference of the movement of non-suckling piglets on the spatiotemporal feature extraction for the region of interest; transforming spatiotemporal features extracted by a spatiotemporal two-stream convolutional network from a key frame video clip into a spatiotemporal feature vector, inputting the obtained spatiotemporal feature vector into a temporal, a spatial and a fusion transformer to obtain a self-attention matrix; performing element-wise product for the self-attention matrix and the fused spatiotemporal features to obtain a self-attention spatiotemporal feature map; and inputting the self-attention spatiotemporal feature map into a regression branch of the number of suckling piglets to complete the counting of the

Abstract: A system and method, in particular computer implemented method for determining a perturbation for attacking and/or validating an association tracker. The method includes providing digital image data that includes an object, determining with the digital image data a first feature that characterizes the object, providing in particular from a storage a second feature that characterizes a tracked object, determining the perturbation depending on a measure of a similarity between the first feature and the second feature.

Abstract: The present invention relates to a neural network-based high-resolution image restoration method and system, including: performing feature extraction on a target frame in a network input to obtain a first feature, performing feature extraction on a first frame and an adjacent frame and an optical flow between the first frame and the adjacent frame to obtain a second feature, and concatenating the first feature and the second feature to obtain a shallow layer feature; performing feature extraction and refinement on the shallow layer feature to obtain a plurality of output first features and a plurality of output second features; performing feature decoding on the plurality of output second features, and concatenating decoded features along channel dimensionality to obtain features; and performing weight distribution on the features to obtain final features, and restoring an image. The present invention can effectively help to improve image quality.

Abstract: The present disclosure relates to a method of matching a text with a design performed by an apparatus for matching a text with a design. According to an embodiment of the present disclosure, the method may comprise acquiring an image from information including images and texts; learning features of the acquired image; extracting texts from the information and performing learning about a pair of an extracted text and the acquired image; extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; performing learning about a pair of the trend word and the acquired image; and identifying a design feature matched with the trend word among learned features of the image.

Abstract: An information processing device is provided that includes an operation control unit which controls the operations of an autonomous mobile object that performs an action according to a recognition operation. Based on the detection of the start of teaching related to pattern recognition learning, the operation control unit instructs the autonomous mobile object to obtain information regarding the learning target that is to be learnt in a corresponding manner to a taught label. Moreover, an information processing method is provided that is implemented in a processor and that includes controlling the operations of an autonomous mobile object which performs an action according to a recognition operation. Based on the detection of the start of teaching related to pattern recognition learning, the controlling of the operations includes instructing the autonomous mobile object to obtain information regarding the learning target that is learnt in a corresponding manner to a taught label.

Abstract: A method for the clustering and identification of animals in acquired images based on physical traits is provided, where a trait feature is a scalar or vector quantity that is a property of a trait and trait distance is a measure of discrepancy between the same trait features of two animals, and also introduced are several different ways of implementing clustering using trait features.

Abstract: The travelling route recognition unit recognizes, based on an image captured by an imaging apparatus capturing an area in the vicinity of the vehicle, a travelling route where the vehicle travels. The classifying unit classifies the travelling route into at least one of a predetermined plurality of road models. The edge point extracting unit extracts edge points necessary for expressing the road model classified by the classifying unit among edge points indicating a boundary of the travelling route recognized by the travelling route recognition unit. The parameter generation unit correlates road model information indicating the road model classified by the classifying unit with the edge points extracted by the edge point extracting unit and generates a travelling route parameter indicating the travelling route recognized by the travelling route recognition unit. The transmission unit transmits the travelling route parameter generated by the parameter generation unit to the server.

Abstract: An information processing device according to the present invention includes: a storage means that stores reference attribute information representing an attribute of a person corresponding to a target place; an extraction means that extracts person attribute information representing an attribute of a person in a captured image obtained by capturing an image of the target place; and a detection means that detects a predetermined person in the captured image based on the reference attribute information and the person attribute information.

Abstract: Disclosed are an unsupervised learning-based image registration method using a neural network with cycle consistency and an apparatus therefor. An image registration method includes receiving a first image and a second image for image registration, outputting a deformation field for the first image and the second image using an unsupervised learning-based neural network with cycle consistency for the deformation field, and generating a registration image for the first image and the second image based on a spatial deformation function using the output deformation field. The outputting of the deformation field includes outputting the deformation field for the first image for registering the first image to the second image may be output, when the first image is a moving image and the second image is a fixed image, and the generating of the registration image includes generating the registration image by applying the deformation field for the first image to the first image using the spatial deformation function.

Abstract: Systems and methods for controlling traffic signaling includes a wireless sensor operable to detect and receive wireless signals emitted from a vehicle, an image sensor operable to capture a stream of images of a field of view. A traffic control system is operable to extract geographic positioning information for the vehicle from the wireless signals, track the vehicle's movement using the extracted geographic positioning information, detect and track an object in the stream of images corresponding to the vehicle. The vehicle's geographic movement is further tracked using a pixel location of the object in steam of images and a traffic control action is executed based on the geographic movement to facilitate passage of the at least one vehicle through a monitored traffic control location.

Abstract: The invention provides a multi-slice magnetic resonance imaging method and device based on long-distance attention model reconstruction. The method includes that: a deep learning reconstruction model is constructed; data preprocessing is performed on multiple slices of simultaneously acquired signals, and multiple slices of magnetic resonance images or K-space data is used as data input; learnable positional embedding and imaging parameter embedding are acquired; the preprocessed input data, the positional embedding and the imaging parameter embedding are input into the deep learning reconstruction model; and the deep learning reconstruction model outputs a result of the magnetic resonance reconstruction image. The invention further provides a device for implementing the method. The invention may improve the quality of the magnetic resonance image, improve the diagnosis accuracy of a doctor, increase the imaging speed, and improve the utilization rate of a magnetic resonance machine.

Abstract: An information processing device according to the present invention includes: a storage means that stores reference attribute information representing an attribute of a person corresponding to a target place; an extraction means that extracts person attribute information representing an attribute of a person in a captured image obtained by capturing an image of the target place; and a detection means that detects a predetermined person in the captured image based on the reference attribute information and the person attribute information.

Abstract: Digital synthesis techniques are described to synthesize a digital image at a target time between a first digital image and a second digital image. To begin, an optical flow generation module is employed to generate optical flows. The digital images and optical flows are then received as an input by a motion refinement system. The motion refinement system is configured to generate data describing many-to-many relationships mapped for pixels in the plurality of digital images and reliability scores of the many-to-many relationships. The reliability scores are then used to resolve overlaps of pixels that are mapped to a same location by a synthesis module to generate a synthesized digital image.

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 exemplary surgical instrument tracking system includes at least one physical computing device that determines, based on endoscopic imagery of a surgical area and using a trained neural network, an observation for an object of interest depicted in the endoscopic imagery, associates, based on a probabilistic framework and kinematics of a robotically-manipulated surgical instrument located at the surgical area, the observation for the object of interest to the robotically-manipulated surgical instrument, and determines a physical position of the robotically-manipulated surgical instrument at the surgical area based on the kinematics of the robotically-manipulated surgical instrument and the observation associated with the robotically-manipulated surgical instrument.

Abstract: A harvester including an inlet configured to receive a crop including a stalk, a blade configured to cut the crop into a billet, a sensor configured to detect a three-dimensional appearance of at least a portion of the billet and generate a signal associated with the three-dimensional appearance of the at least a portion of the billet, and a control system having a processor, a memory, and a human-machine interface. The control system is configured to receive the signal from the sensor and programmed to 1) analyze the three-dimensional appearance of the at least a portion of the billet, 2) classify the three-dimensional appearance using an indicator of cut quality and 3) index the indicator of cut quality into the memory.

Abstract: A method for training a generator network that is configured to generate images with multiple objects. The method includes providing a set of training images, a generator network, and a discriminator network; drawing noise samples and target counts of objects; mapping, by the generator network, the noise samples and target counts of objects to generated images; randomly drawing images from a pool comprising generated images and training images; supplying the randomly drawn images to the discriminator network, thereby mapping them to a combination of: a decision whether the respective image is a training image or a generated image; optimizing discriminator parameters, optimizing generator parameters, and optimizing both the generator parameters and the discriminator parameters with the goal of improving the match between the predicted count of objects on the one hand, and the actual or target count of objects on the other hand.