Abstract: An information processing device includes: an acquisition unit configured to acquire setting information which is set based on a template image and associates an image region with analysis process content; and an image processing unit configured to perform an analysis process corresponding to the analysis process content associated with the image region on an image of a target region corresponding to the image region in an input image.

Abstract: Provided are a method and apparatus for detecting a status of a lens, an electronic device and a medium. A specific implementation includes: determining current feature points included in a current image according to the current image captured at a current moment by a to-be-detected lens of a target driving device; determining historical feature points included in a historical image according to the historical image captured at a historical moment by the to-be-detected lens; and matching the current feature points with the historical feature points and determining a status detection result of the to-be-detected lens according to a matching result. Therefore, an effect of determining a status of the to-be-detected lens of the target driving device is achieved and the problem of a potential safety hazard of the target driving device due to an abnormal status of the to-be-detected lens can be avoided.

Abstract: A diagnosis assisting system, a diagnosis assisting method, and a diagnosis assisting program provide information that contributes to an objective diagnosis, with a server of a diagnosis assisting system including a learned model acquisition unit that acquires a plurality of learned models, each of which is generated by machine learning, inputs information based on an image obtained by imaging a diagnostic target, and outputs information that indicates a diagnosis result of the diagnostic target; an image acquisition unit that acquires an image to be analyzed; a calculation unit that performs calculation based on the plurality of learned models acquired on the image acquired, and calculates one piece of information about a diagnosis using the plurality of learned models; and an output unit that outputs the information calculated by the calculation unit.

Abstract: A single image deraining method is proposed. A wavelet transforming step processes an initial rain image to generate an i-th stage low-frequency rain image and a plurality of i-th stage high-frequency rain images. An image deraining step inputs the i-th stage low-frequency rain image to a low-frequency deraining model to output an i-th stage low-frequency derain image. A first inverse wavelet transforming step recombines the n-th stage low-frequency derain image with the n-th stage high-frequency derain images to form an n-th stage derain image. A weighted blending step blends a (n?1)-th stage low-frequency derain image with the n-th stage derain image to generate a (n?1)-th stage blended derain image. A second inverse wavelet transforming step recombines the (n?1)-th stage high-frequency derain images with the (n?1)-th stage blended derain image to form a (n?1)-th stage derain image, and sets n to n?1 and repeats the last two steps.

Abstract: An identity authentication method is provided, including: acquiring a raw feature of a user; calling an identity authentication model to extract a primary attribute feature vector in the raw feature, the primary attribute feature vector being an unbiased feature representation for selectively decoupling m?1 domain discrepancy features in the raw feature, and m being an integer greater than 2; and performing unbiased identity authentication based on the primary attribute feature vector to obtain an identity authentication result.

Abstract: Laser mapping imaging with reduced fixed pattern noise is disclosed. A method includes actuating an emitter to emit a plurality of pulses of electromagnetic radiation and sensing reflected electromagnetic radiation resulting from the plurality of pulses of electromagnetic radiation with a pixel array of an image sensor to generate a plurality of exposure frames. The method includes applying edge enhancement to edges within an exposure frame of the plurality of exposure frames. The method is such that at least a portion of the plurality of pulses of electromagnetic radiation emitted by the emitter comprises a laser mapping pattern.

Abstract: An expression recognition method and apparatus, a computer device, and a readable storage medium are provided. The method includes: performing face key-point position detection on a face image to obtain face key-point position information; and obtaining expression class information of the face image using four cascaded convolutional modules and a trained neural network classifier according to the face image and the face key-point position information.

Abstract: A method includes receiving from a first detection system, an image of a license plate, applying an optical recognition function to characters of the license plate, and assigning a confidence value to each of the characters. The method further includes, in response to determining that the confidence value for a particular character is below a threshold, sending an image of the character to a gamified human reviewer application, receiving from the gamified human reviewer application, a human response indicative of a characteristic of the character, and updating the optical recognition function based on the characteristic of the character.

Abstract: An apparatus comprising an interface, a light projector and a processor. The interface may be configured to receive pixel data. The light projector may be configured to generate a structured light pattern. The processor may be configured to process the pixel data arranged as video frames and generate disparity and depth maps. The processor may comprise convolutional neural network hardware that may arrange reference images into a tensor, perform logical operations on one of the video frames in a depth direction of the tensor to generate a tensor of feature maps of the video frames, use a convolution to reduce an amount of calculations performed in the depth direction of the tensor of feature maps, perform convolution filtering on the tensor of the feature maps, determine an index map location, and search lookup data based on the index map location to determine the disparity and depth maps.

Abstract: A computer-implemented method of training an image generation model. The image generation model comprises an argmax transformation configured to compute a discrete index feature indicating an index of a feature of the continuous feature vector with an extreme value. The image generation model is trained using a log-likelihood optimization. This involves obtaining a value of the index feature for the training image, sampling values of the continuous feature vector given the value of the index feature according to a stochastic inverse transformation of the argmax transformation, and determining a likelihood contribution of the argmax transformation for the log-likelihood based on a probability that the stochastic inverse transformation generates the values of the continuous feature vector given the value of the index feature.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining video data; dividing the video data into a plurality of video fragments based, at least in part, on page detection; extracting one or more elements from each of the plurality of video fragments; determining element type data for each of the one or more extracted elements; generating element style data for the one or more extracted elements; determining page flow for the plurality of video fragments; and generating one or more pages based, at least in part, on the one or more elements extracted from the plurality of video fragments, the element type data, the element style data, and the page flow.

Abstract: One or more machine learning techniques can be used to identify locations of potential malignancies within images (e.g., video images) captured during a medical procedure, such as an endoscopic procedure. The images can be displayed, in real-time, on a display unit. The images can be displayed with a graphical overlay that isolates the identified locations of potential malignancies.

Abstract: A method and system for multi-object tracking is set forth. Object data for boundaries of a plurality of objects are received. Poisson multi-Bernoulli mixture filtering is performed on the object data to form a filtered set of object data. Ultimately, the filtered set of object data is used to control the operation of the vehicle. Identifiers and probabilities are associated with the objects to reduce the set of object data.

Abstract: A method and apparatus for improving sensitivity of wafer detection, and a storage medium are provided. The method including: acquiring a gray-scale image of a peripheral circuit region of a wafer; dividing the gray-scale image into at least one pixel region; determining a repeated image region in each pixel region, in which the repeated image region is a region with N*M pixels where M pixels on a same column in the repeated image region have a same gray-scale value, and N and M are integers greater than 0; and determining position information where interference superposition occurs in the peripheral circuit region of the wafer according to all repeated image regions in the gray-scale image.

Abstract: Certain aspects of the present disclosure provide a method, including: processing input data with a feature extraction stage of a machine learning model to generate a feature map; applying an attention map to the feature map to generate an augmented feature map; processing the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map; processing the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data; and processing the refined feature data with an attention stage of the machine learning model to generate an updated attention map.

Abstract: Disclosed is a reconfigurable parallel 3-Dimensional (3-D) convolution engine for performing 3-D Convolution and parallel feature map extraction on an image. The reconfigurable parallel 3-D convolution engine further comprises a plurality of CNN reconfigurable engines configured to perform 3-D convolution, in parallel, to process a plurality of feature maps, a kernel memory space, present in each instance of CNN reconfigurable engine, capable for holding a set of parameters associated to a network layer having each operational instance of CNN reconfigurable engine, and at least one memory controller, an Input Feature Map Memory (FMM) cluster and an Output FMM cluster.

Abstract: A violation detection platform may obtain image data, location data, and sensor data associated with a vehicle. The violation detection platform may determine a probability that a frame of the image data includes an image of a stop sign. The violation detection platform may determine that the probability satisfies a probability threshold. The violation detection platform may identify location data and sensor data associated with the frame of the image data based on the probability satisfying the probability threshold. The violation detection platform may determine an occurrence of a type of a stop sign violation based on the probability, the location data, and the sensor data. The violation detection platform may perform one or more actions based on determining the occurrence of the type of the stop sign violation.

Abstract: Adaptive phase unwrapping for a time-of-flight camera. A scene is illuminated with modulation light having two or more frequencies that do not have a common integer denominator. The modulation light that is reflected off objects within the scene is received at a sensor array. The received modulation light is then processed and weighted in the complex domain to determine unwrapped phases for each of the two or more frequencies of modulation light.

Abstract: This disclosure relates generally to estimating age of a leaf using morphological features extracted from segmented leaves. Traditionally, leaf age estimation requires a single leaf to be plucked from the plant and its image to be captured in a controlled environment. The method and system of the present disclosure obviates these needs and enables obtaining one or more full leaves from images captured in an uncontrolled environment. The method comprises segmenting the image to identify veins of the leaves that further enable obtaining the full leaves. The obtained leaves further enable identifying an associated plant species. The method also discloses some morphological features which are fed to a pre-trained multivariable linear regression model to estimate age of every leaf. The estimated leaf age finds application in estimation of multiple plant characteristics like photosynthetic rate, transpiration, nitrogen content and health of the plants.

Abstract: An image recognition method, a training system for an object recognition model and a training method for an object recognition model are provided. The image recognition method includes the following steps. At least one original sample image of an object in a field and an object range information and an object type information in the original sample image are obtained. At least one physical parameter is adjusted to generate plural simulated sample images of the object. The object range information and the object type information of the object in each of the simulated sample images are automatically marked. A machine learning procedure is performed to train an object recognition model. An image recognition procedure is performed on an input image.