Abstract: A method and an apparatus for recognizing characters using an image are provided. A camera is activated according to a character recognition request and a preview mode is set for displaying an image photographed through the camera in real time. An auto focus of the camera is controlled and an image having a predetermined level of clarity is obtained for character recognition from the images obtained in the preview mode. The image for character recognition is character-recognition-processed so as to extract recognition result data. A final recognition character row is drawn that excludes non-character data from the recognition result data. A first word is combined including at least one character of the final recognition character row and a predetermined maximum number of characters. A dictionary database that stores dictionary information on various languages using the first word is searched, so as to provide the user with the corresponding word.

Abstract: Multiple image verification challenges can be used to identify the location of an object within an initial image. For instance, a first set of tiles is generated using the initial image. This first set is provided to a client computing device for display in a first verification challenge requesting that the user select tiles including the object. In response, a user selection of tiles of the first set is received. These selected tiles are then used to generate a second set of tiles corresponding to a sub-portion of the initial image. The second set of tiles is provided to a client computing device for display in a second verification challenge. In response to the second verification challenge, a user selection of tiles of the second set is received. This user selection of tiles of the second set is then used to determine a location of the object in the image.

Abstract: An image processing apparatus, an image processing method, and a program, provide accurate collation even when an image contains a number of identical or similar subjects. The image processing apparatus generates, with respect to feature points to be detected from a first image, a first local feature amount group including local feature amounts representing feature amounts of local regions containing the respective feature points, and a first coordinate position information group including coordinate position information. The image processing apparatus clusters the feature points of the first image based on the first coordinate position information group. The image processing apparatus collates, in units of clusters, the first local feature amount group with a second local feature amount group formed from local feature amounts of feature points detected from a second image.

Abstract: In a multi-session imaging study, information from a previous imaging session is stored in a Binary Large Object (BLOB). Current emission imaging data are reconstructed into a non-attenuation corrected (NAC) current emission image. A spatial transform is generated aligning a previous NAC emission image from the BLOB to the current NAC emission image. A previous computed tomography (CT) image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed with attenuation correction using the warped CT image. Alternatively, low dose current emission imaging data and a current CT image are acquired, a spatial transform is generated aligning the previous CT image to the current CT image, a previous attenuation corrected (AC) emission image from the BLOB is warped using the spatial transform, and the current emission imaging data are reconstructed using the current CT image with the warped AC emission image as prior.

Abstract: A method performed by an electronic device is described. The method includes receiving a set of images. The method also includes determining a motion region and a static region based on the set of images. The method further includes extracting, at a first rate, first features from the motion region. The method additionally includes extracting, at a second rate that is different from the first rate, second features from the static region. The method also includes caching the second features. The method further includes detecting at least one object based on at least a portion of the first features.

Abstract: In one embodiment, a system of an ADV perceives an object in a perspective view of the ADV using a plurality of sensors mounted on the ADV including capturing an image of the object. The system determines a two-dimensional (2D) bounding box for the object and a perceived distance from the ADV to the object. The system searches, within an image space of the image, a dynamic pitch angle based on the 2D bounding box and the perceived distance from the ADV to the object by modifying at least a previous extrinsic calibration value to calculate a distance. The system determines a distance from the ADV to a point on a ground plane of the perspective view of the ADV based on the determined dynamic pitch angle. The system generates a driving trajectory based on the determined distance for the point on the ground plane to control the ADV.

Abstract: An expanding appliance includes a connect port, an image capturing module, an intelligent control module, an image transmitting module, and a result displaying module. The expanding appliance connects an image input device through the connect port, connects a display device through the result displaying module, and connects one or more image-applied function module through the image transmitting module. The intelligent control module generates a demanding command according to a successfully-connected image-applied function module. The image capturing module controls the image input device to capture image data based on the demanding command, and quantizes samples of the image data as computation data. The image transmitting module provides the computation data to the image-applied function module for image identification and receives an identification result. Finally, the intelligent control module triggers the result display module for displaying the identification result on the display device.

Abstract: Described is a system that can recognize novel objects that the system has never before seen. The system uses a training image set to learn a model that maps visual features from known images to semantic attributes. The learned model is used to map visual features of an unseen input image to semantic attributes. The unseen input image is classified as belonging to an image class with a class label. A device is controlled based on the class label.

Abstract: This information processing device 10 is provided with a product quantity estimating unit 14 (detecting means) and a wait time estimating unit 18: the product quantity estimating unit 14 detects the number of shopping baskets held by people lined up at a product registration device (POS device); the wait time estimating unit 18 calculates a wait time prediction value for each product registration device on the basis of the number of shopping baskets; the product quantity estimating unit 14 detects the shopping baskets, and estimates the quantity of products held by each person lined up at each product registration device; the wait time estimating unit 18 calculates the wait time prediction value for each product registration device on the basis of the quantity of products held by each person lined up at each product registration device.

Abstract: The invention relates to a method, an image processor (26) and a medical observation device (1), such as a microscope or endoscope, for observing an object (4) containing a bolus of at least one fluorophore (12). The object (4) is preferably live tissue comprising several types (16, 18, 20) of tissue. According to the method, a set (34) of component signals (36) is provided. Each component signal (36) represents a fluorescence intensity development of the fluorophore (12) over time in a different type of tissue. A time series (8) of input frames (10) is accessed, one input frame (10) after the other. The input frames (10) represent electronically coded still images of the object (4) at subsequent time. Each input frame (10) contains at least one observation area (22) comprising at least one pixel (23).

Abstract: An apparatus and method for image normalization using a Gaussian residual of fit selection criteria. The method may include acquiring a two-dimensional image of a plurality of particles, where the plurality of particles comprises a plurality of calibration particles, and identifying a calibration particle by correlating a portion of the image corresponding to the calibration particle to a mathematical model (e.g. Gaussian fit). The measured intensity of the calibration particle may then be used to normalize the intensity of the image.

Abstract: A mura detection device includes an XYZ coordinate system conversion unit which receives a photographed image of a display device and converts the photographed image into XYZ image data according to XYZ chromatic coordinates, a background image generation unit which generates background image data obtained by removing a part of the XYZ image data, a color difference calculation unit which generates color difference image data by comparing the photographed image and the background image data, and a mura data generation unit which calculates a color mura index value using the color difference image data.

Abstract: A network is machine trained to estimate flow by spatial location based on input of anatomy information. A medical scan of tissue may be used to generate flow information without the delay or difficulty of performing a medical scan configured for flow imaging or CFD. Anatomy imaging is used to provide flow estimates with the speed provided by the machine-learned network.

Abstract: The present disclosure relates to systems and methods for determining quantitative chemical exchange or exchangeable proton information from a region-of-interest in a subject. The methods and systems use adaptive fitting to quantify magnetic resonance (MR) data, such as chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) images, using initial values based on, for example, Lorentzian fitting. Images are iteratively less downsampled until quantitative maps of desired resolution are obtained. Such an approach allows for reliable fitting at a faster fitting speed, and is less susceptible to suboptimal signal to noise (SNR) than conventional methods.

Abstract: In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Abstract: A system and method of classifying images of pathology. An image is received, normalized, and segmented normalizing the image; into a plurality of regions; A disease vector is automatically determining for the plurality of regions with at least one classifier comprising a neural network. Each of the respective plurality of regions is automatically annotated, based on the determined disease vectors. The received image is automatically graded based on at least the annotations. The neural network is trained based on at least an expert annotation of respective regions of images, according to at least one objective classification criterion. The images may be eye images, vascular images, or funduscopic images. The disease may be a vascular disease, vasculopathy, or diabetic retinopathy, for example.

Abstract: The present disclosure relates to an information processing device, an information processing method, and a program that enable appropriate imaging with the front camera of a smartphone or the like in the dark. A visible light image and an infrared light image are captured. A face image is recognized from the infrared light image, and images of respective organs are extracted from the face image. The images of the respective organs are corrected in accordance with each extracted organ, using the visible light image and the infrared light image. The corrected images of the respective organs are combined. The present disclosure can be applied to smartphones.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.

Abstract: A method of performing automated object inspection includes obtaining a plurality of test images. For each of the plurality of test images, the method includes performing independent object inspection on each of two or more sub-portions of the test image. The method further includes segmenting the test image into at least a first sub-portion of the test image and a second sub-portion of the test image; performing object inspection on the first sub-portion of the test image using a first subset of information channels of the test image and a first model trained on a first set of training images containing the first component; and performing object inspection on the second sub-portion of the test image using a second subset of information channels of the test image, and a second model trained on a second set of training images containing the second component.

Abstract: Type prediction method, apparatus and electronic device for recognizing an object in an image are disclosed. The method may include processing an image to be processed using a full image recognition technique to obtain a first type prediction result of an object in the image to be processed; processing a subject area of the image to be processed using a feature recognition technique to obtain a second type prediction result of an object of the subject area; determining whether the first type prediction result matches the second type prediction result; if the first type prediction result matches the second type prediction result, determining a type of the object of the image to be processed to be the first type prediction result or the second type prediction result.