The object detection device extracts a plurality of predetermined features from an image in which a target object is represented, calculates an entire coincidence degree between the plurality of predetermined features set for an entire model pattern of the target object and the plurality of predetermined features extracted from a corresponding region on the image while changing a relative positional relationship between the image and the model pattern, and calculates, for each partial region including a part of the model pattern, a partial coincidence degree between the predetermined features included in the partial region and the predetermined features extracted from a region corresponding to the partial region on the image. Then, the object detection device determines whether or not the target object is represented in the region on the image corresponding to the model pattern based on the entire coincidence degree and the partial coincidence degree.

A magnetic particle inspection device includes an image acquisition unit configured to acquire an image obtained by attaching magnetic particles to a magnetized inspection object and capturing the image of the inspection object, and a binarized image obtained by binarizing a whole or a part of the image; a region specifying unit configured to specify a magnetic particle group region based on at least one of the image and the binarized image, the magnetic particle group region containing a magnetic particle group; and a detection unit configured to detect a flaw by processing luminance information and form information with artificial intelligence obtained by supervised learning, the luminance information being obtained by performing statistical processing on luminances of a plurality of minute regions in the specified magnetic particle group region, and the form information being information regarding a form of the magnetic particle group that is obtained from the binarized image.

A method and system, the method including receiving semantic descriptions of features of an asset extracted from a first set of images; receiving a model of the asset, the model constructed based on a second set of a plurality images of the asset; receiving, based on an optical flow-based motion estimation, an indication of a motion for the features in the first set of images; determining a set of candidate regions of interest for the asset; determining a region of interest in the first set of images; iteratively determining a matching of features in the set of candidate regions of interest and the determined region of interest in the first set of images to generate a record of matches in features between two images in the first set of images; and displaying a visualization of the matches in features between two images in the first set of images.

This image processing apparatus is provided with an image acquisition unit for generating a concentration change image and a control unit for performing control for displaying a blood vessel image and a concentration change image, and the control unit is configured to perform control for accepting a selection of a target region on the blood vessel image displayed on the display unit and for displaying the concentration change image corresponding to the selected target region.

A method includes receiving, from a sensing system of an autonomous vehicle (AV), image data including first image data and second image data. The method further includes determining, for a frame, whether an amount of image data matching between the first image data and the second image data satisfies a first threshold condition, in response to determining that the amount of image data matching satisfies a first threshold condition, identifying the frame as invalid, determining whether a number of consecutive frames determined to be invalid satisfies a second threshold condition, and in response to determining that the number of consecutive frames determined to be invalid satisfies the second threshold condition, generating a notification that the sensing system is outputting invalid data.

An identifier of query content is accurately determined. A content determining device 100 includes an input unit 2 that inputs query content, a storage unit 1 that stores a plurality of pieces of master content, and a content determining unit 4 that determines a region where feature values of two pieces of master content out of the plurality of pieces of master content do not match each other, calculates a matching feature count which is a count of feature values of the region that match feature values in a corresponding region of the query content, for each of the two pieces of master content, and determines an identifier of the query content on the basis of the matching feature count of each of the pieces of master content.

A similar picture identification method is described. According to the method, processing circuitry of a device obtains n first local regions of a first picture, the n first local regions being a first set of regions that is affine invariant, and obtains m second local regions of a second picture, the m second local regions being a second set of regions that is affine invariant. The processing circuitry obtains n first characteristic values respectively corresponding to the n first local regions, and obtains m second characteristic values respectively corresponding to the m second local regions. The processing circuitry further determines, according to a comparison result of the n first characteristic values and the m second characteristic values, whether the first picture is similar to the second picture, where n and m are positive integers.

A method, apparatus and system for zero shot object detection includes, in a semantic embedding space having embedded object class labels, training the space by embedding extracted features of bounding boxes and object class labels of labeled bounding boxes of known object classes into the space, determining regions in an image having unknown object classes on which to perform object detection as proposed bounding boxes, extracting features of the proposed bounding boxes, projecting the extracted features of the proposed bounding boxes into the space, computing a similarity measure between the projected features of the proposed bounding boxes and the embedded, extracted features of the bounding boxes of the known object classes in the space, and predicting an object class label for proposed bounding boxes by determining a nearest embedded object class label to the projected features of the proposed bounding boxes in the space based on the similarity measures.

Disclosed herein is a washing machine capable of identifying whether laundry of an inner tub includes waterproof clothing. The washing machine includes a cabinet provided with an opening at an upper portion thereof, an outer tub provided in the cabinet, an inner tub provided in the outer tub, a motor configured to rotate the inner tub, a camera configured to capture an image of an inside of the inner tub, and a controller configured to control the motor to increase a rotational speed of the inner tub to a first rotational speed during spinning. The controller is configured to control the motor to set the rotational speed of the inner tub to a second rotational speed, which is less than the first rotational speed, based on the image of the inside of the inner tub captured by the camera during the spinning.

A system includes a memory and at least one processor to acquire a hyperspectral image of a food object by an imaging device, the hyperspectral image of the food object comprising a three-dimensional set of images of the food object, each image in the set of images representing the food object in a wavelength range of the electromagnetic spectrum, normalize the hyperspectral image of the food object, select a region of interest in the hyperspectral image, the region of interest comprising a subset of at least one image in the set of images, extract spectral features from the region of interest in the hyperspectral image, and compare the spectral features from the region of interest with a plurality of images in a training set to determine particular characteristics of the food object and determine that the hyperspectral image indicates a foreign object.