Abstract: A radiation tomography system includes a radiation tomography apparatus and an image processing apparatus. The image processing apparatus includes an image reconstruction unit, an attenuation distribution image creation unit, and an attenuation correction unit. The attenuation distribution image creation unit includes a first processing unit and a second processing unit. The first processing unit creates and outputs an intermediate image based on an emission scan image using a trained neural network. The second processing unit creates and outputs an attenuation distribution image based on the intermediate image.

Abstract: The present disclosure provides a system and method for image reconstruction. The method may include obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and generating a trained image processing model by training a preliminary model using the training samples.

Abstract: A photographing control device includes a storage unit that stores identification information of a plurality of persons as a group, an acquiring unit that acquires a photographed image obtained by a predetermined photographing device and capturing multiple persons, a region identifying unit that identifies a plurality of facial regions from the photographed image, an individual authentication controlling unit that causes an authentication device storing a set of facial feature information items of the plurality of persons to perform facial authentication of each of the identified facial regions by use of the set of facial feature information items, and an output unit that outputs the photographed image as an output image if facial authentication of each of the facial feature information items of the respective persons belonging to the group has succeeded with regard to any one of the plurality of facial regions.

Abstract: A computer-executable method relating to weeds, and a computer system. The method comprises: receiving an image (S11); recognizing one or more plants in the image in order to obtain the classification and/or names of the plants, and determining whether the plants are weeds (S12); and in response to determining that at least one plant is a weed, outputting information indicating that the at least one plant is a weed (S13).

Abstract: A thermal image processing device includes: an acquirer that acquires a thermal image from a thermal image sensor; a first processor that performs, on a high temperature pixel indicating a temperature higher than a first temperature among a plurality of pixels included in the thermal image acquired, image processing for decreasing the temperature indicated by the high temperature pixel; and a second processor that performs high frequency enhancement processing for enhancing a high frequency component included in a converted image serving as the thermal image on which the image processing has been performed.

Abstract: The present invention relates to a method of detecting an edge (or a contour line) of a pattern, which is formed on a workpiece (e.g., a wafer or a mask) for use in manufacturing of semiconductor, from an image generated by a scanning electron microscope. The pattern-edge detection method includes: generating an objective image of a target pattern formed on a workpiece; generating a feature vector representing features of each pixel constituting the objective image; inputting the feature vector to a model constructed by machine learning; outputting, from the model, a determination result indicating whether the pixel having the feature vector is an edge pixel or a non-edge pixel; and connecting a plurality of pixels, each having a feature vector that has obtained a determination result indicating an edge pixel, with a line to generate a virtual edge.

Abstract: A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

Abstract: An image processing apparatus comprises an acquisition unit configured to acquire reference information which indicates at least one of a number of objects detected from a frame, a density of an object in a frame, or a frame rate, and a tracking unit configured to track an identical object between frames. The tracking unit determines that each of objects for which a difference in a detection position between the frames is smaller than a threshold value corresponding to the reference information is an identical object, and does not determine that each of objects for which a difference in the detection position between the frames is equal to or larger than the threshold value is an identical object.

Abstract: An inspection apparatus according to one or more embodiments extracts an attention area from a target image using a first estimation model, performs a computational process with a second estimation model using the extracted attention area, and determines whether a target product has a defect based on a computational result from the second estimation model. The first estimation model is generated based on multiple first training images of defect-free products in a target environment. The second estimation model is generated based on multiple second training images of defects. The computational process with the second estimation model includes generating multiple feature maps with different dimensions by projecting the target image into different spaces with lower dimensions. The extracted attention area is integrated into at least one of the multiple feature maps in the computational process with the second estimation model.

Abstract: This application describes systems and methods for detecting depth in deep trench isolation with semiconductor devices using test key transistors. An method comprises: capturing, by an image sensor, an image; generating a plurality of chrominance channels by converting the image into luminance-chrominance space; performing homogeneous region segmentation on the plurality of chrominance channels to generate one or more regions of interest in the plurality of chrominance channels; and projecting the regions of interest onto eigen-illuminant images to determine gray color pixels on the image, wherein the eigen-illuminant images are generated via performing a machine learning algorithm on a training set of images captured by the image sensor.

Abstract: Techniques for utilizing image processing systems to measure damage to vehicles include utilizing an image processing system to determine, based on a part of the damaged vehicle depicted in an image of the damaged vehicle and a measurement of a corresponding part of an associated undamaged vehicle, a depth of field indicator corresponding to the image. In some situations, the image processing system warps at least a part of the image, and the warped image is utilized to determine the depth of field indicator. The techniques additionally include measuring a damaged area of the vehicle based on the depth of field indicator and a heat map that has been generated based on the image and that is indicative of damaged areas of the vehicle. In some cases, measuring the damaged area is further based on a segmentation map generated based on the image.

Abstract: A processor-implemented method with image analysis includes: receiving a test image; generating a plurality of augmented images by augmenting the test image; determining classification prediction values for the augmented images using a classifier; determining a detection score based on the classification prediction values; and determining whether the test image corresponds to anomaly data based on the detection score and a threshold.

Abstract: A video segmentation method and apparatus, an electronic device, and a computer-readable storage medium, related to the technical field of video processing. The method comprises: acquiring a video frame to be processed in a target-video, and information to be segmented in the video frame to be processed; determining a tilt angle corresponding to the video frame to be processed according to the information to be segmented; performing correction processing on the video frame to be processed according to the tilt angle, to obtain a corrected video frame; and performing image segmentation on the corrected video frame according to the information to be segmented, to determine an image to be played.

Abstract: Example embodiments relate to pedestrian countdown signal classification to increase pedestrian behavior legibility. An example embodiment includes a method that includes obtaining, by a computing system of a vehicle, a camera image patch. The method further includes determining, by the computing system, using the camera image patch and a pedestrian countdown signal classifier model, a state of a pedestrian countdown signal. The method also includes determining, by the computing system based on the state of the pedestrian countdown signal, a prediction of whether a pedestrian will enter a crosswalk governed by the pedestrian countdown signal. And the method includes, based on the prediction, causing, by the computing system, the vehicle to perform an invitation action that invites a pedestrian to enter the crosswalk.

Abstract: Heart strain determination includes receiving a series of 2D-slice images as input. A pose estimation module estimates a slicing-pose of the inputted series of 2D-slice images in the heart. A 3D deformation estimation module estimates a 3D deformation field from the series of 2D-slice images and the estimated slicing-pose. A strain measurement module computes a heart strain measure from the 3D deformation field and a predefined definition for strain computation.

Abstract: A three-dimensional data encoding method includes: encoding geometry information of each of three-dimensional points based on one of a first geometry information encoding method of encoding using octree division and a second geometry information encoding method of encoding without using octree division; and generating a bitstream including the geometry information encoded and a geometry information flag indicating whether the encoding was performed based on the first geometry information encoding method or the second geometry information encoding method. In the generating of the bitstream: when the encoding is performed based on the first geometry information encoding method, the bitstream including a parameter set used in octree division is generated; and when the encoding is performed based on the second geometry information encoding method, the bitstream not including the parameter set used for octree division is generated.

Abstract: An anomaly detecting unit detects an anomaly object in a target image. A characteristic amount watching unit watches at least two basic characteristic amounts of the anomaly object, determines whether values of the basic characteristic amounts satisfy a predetermined watching determination condition of any one of predetermined plural anomaly types or not, if it is determined that the values of the basic characteristic amounts satisfy the watching determination condition, determines as an auxiliary characteristic amount for the anomaly object a characteristic amount corresponding to the anomaly type of which the values of the basic characteristic amounts satisfy the watching determination condition, and starts watching a value of the auxiliary characteristic amount. An anomaly type determining unit determines an anomaly type of the anomaly object on the basis of the basic characteristic amounts and the auxiliary characteristic amount currently watched by the characteristic amount watching unit.

Abstract: This image retrieval system comprises: a storage means for storing a plurality of registered images capturing a plurality of objects to be registered; an acquisition means for acquiring a retrieval image including a region of interest; an extraction means for extracting a feature amount from a region, in the retrieval image, other than the region of interest; a retrieval means for retrieving, from the plurality of registered images stored in the storage means, a registered image having a feature amount that matches the feature amount extracted by the extraction means; and an output means for outputting, in a manner that makes it possible to recognize a region corresponding to the region of interest in the retrieval image, all or a portion of the registered image retrieved by the retrieval means.

Abstract: The present disclosure is directed to encoding images. In particular, one or more computing devices can receive data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type. The computing device(s) can receive data representing an image comprising one or more objects of the particular type. The computing device(s) can generate, based at least in part on the data representing the image and the data representing the ML model(s), data representing an encoded version of the image that alters at least a portion of the image comprising the object(s) such that when the encoded version of the image is decoded, the object(s) are unrecognizable as being of the particular type by one or more object-recognition ML models based at least in part upon which the ML model(s) configured to encode the images were trained.

Abstract: An analysis apparatus according to one or more embodiments may identify the classes of features included in object data using a plurality of discriminators that are respectively configured to discriminate the presence of features of classes different to each other; and determines that a first data portion, with respect to which discrimination is established by one of the plurality of discriminators, but discrimination is not established by the remaining discriminators, includes a feature of the particular class that is discriminated by the one discriminator, and determines that a second data portion, with respect to which discrimination is established by all of the discriminators including the one discriminator, does not include a feature of that particular class.