A computer-vision method includes recognizing a feature representation of a query image depicting an unknown subject. A similarity score is computed between the representation of the query image and feature representations of a plurality of gallery images collectively depicting two or more different subjects with at least two or more gallery images for each subject, and each gallery image having a label identifying which of the subjects is depicted. One or more updated feature representations of the query image are sequentially iterated based on one or more of the computed similarity scores. For each of the one or more updated feature representations, an updated similarity score is computed between the updated feature representation and the feature representations of each of the gallery images. The unknown subject is identified based on a gallery image having a highest updated similarity score.

Apparatus and method to detect anomalies in observations use a first plurality of observations regarding operation of a computing system, which are binned based on features values of the observations. Based on the binning, a weighting score is determined for the observations, which is applied to a loss function of an autoencoder. A second plurality of observations is then applied to the autoencoder as input to determine a reconstruction error value for each observation of the second plurality of observations. The reconstruction error values are used to detect anomalous observations of the second plurality of observations.

There is provided an item detection device that detects an item to be loaded and unloaded and includes an image acquisition unit acquiring a surrounding image obtained by capturing surroundings of the item detection device, an information image creation unit creating an information image, in which information related to a part to be loaded and unloaded in the item has been converted into an easily recognizable state, on the basis of the surrounding image, and a computing unit computing at least one of a position and a posture of the part to be loaded and unloaded on the basis of the information image.

An image processing apparatus includes an image processing unit configured to generate a second image obtained by performing predetermined image processing on a first image representing an input image, a determination unit configured to determine a luminance correction range on the basis of image information in one of a high luminance range and a low luminance range determined in accordance with the image processing, and a luminance correction unit configured to correct a luminance value in the luminance correction range determined by the determination unit for the second image.

An image processing device can use a calculation formula based on an ellipse to approximate a base function of a reference GMM. The burden rate according to a co-occurrence correspondence point can be approximately determined by a calculation in which the Manhattan distance to the ellipse and the co-occurrence correspondence point and the width of the ellipse are input to a calculation formula for the burden rate based on the base function. The width of the ellipse is quantized by the nth power of 2 (where n is an integer of 0 or greater), and the calculation can be carried out by means of a bit shift.

Techniques are described for selective extraction of color attributes from digital images that overcome the challenges experienced in conventional systems for color extraction. In an implementation, a user applies a region selector to a source image to select a portion of the source image for color attribute extraction. A graphics editing system identifies a selected region of the source image as well as visual objects of the source image included as part of the selected region. The graphics editing system iterates through the selected visual objects and extracts color attributes from the visual objects, such as color values, patterns, gradients, gradient stops, opacity, color area, and so forth. The graphics editing system then generates a color palette that includes the extracted color attributes, and the color palette is able to be utilized for various image editing tasks, such as digital image creation and transformation.

A method and system of using multiple image cameras or multiple image and depth cameras to capture a target object. Geometry and texture are reconstructed using captured images and depth images. New images are rendered using geometry based rendering methods or image based rendering methods.

A system comprises a computer including a processor and a memory, and the memory including instructions such that the processor is programmed to calculate a standard deviation of a plurality of predictions, wherein each prediction of the plurality of predictions is generated by a different deep neural network using sensor data; and determine at least one of a measurement corresponding to an object based on the standard deviation.

An image processing method and an image processing device are provided. The image processing method includes: acquiring an input image; extracting first and second pixel groups in the input image, wherein the first pixel group comprises first pixels with different positions, and the second pixel group comprises second pixels with different positions, the positions of the first pixels are different from the positions of the second pixels, the number of the first pixels are equal to the number of the second pixels, and the position of each first pixel in the first pixel group corresponds to the position of a respective second pixel in the second pixel group; when a preset similarity condition is satisfied between the first pixel and the respective second pixel, determining a first processing result of the first pixel as a second processing result of the respective second pixel.

This application relates to the field of artificial intelligence, and specifically, to the field of computer vision, and discloses a perception network based on a plurality of headers. The perception network includes a backbone and the plurality of parallel headers. The plurality of parallel headers are connected to the backbone. The backbone is configured to receive an input image, perform convolution processing on the input image, and output feature maps, corresponding to the image, that have different resolutions. Each of the plurality of parallel headers is configured to detect a task object in a task based on the feature maps output by the backbone, and output a 2D box of a region in which the task object is located and confidence corresponding to each 2D box. Each parallel header detects a different task object.