Abstract: An image processing method is provided. A first image is obtained at a first time point, the first image includes a first object image and a first background image, the first object image corresponds to the position of an object at the first time point. A second image is obtained at a second time point, the second image includes a second object image and a second background image, the second object image corresponds to the position of the object at the second time point, and the first and the second images are shot within substantially the same shooting range. A sum motion vector of the object is obtained according to the first and the second object images. The second object image is kept, and a process is applied to each pixel of the second background image in the second image to generate a third image.

Abstract: A method for fiber tractography processes multi-shell diffusion weighted MRI data to identify fiber tracts by calculating intravoxel diffusion characteristics from the MRI data. A transition probability is calculated for each possible path on the lattice, with the transition probability weighted according the intravoxel characteristics. Entropy is calculated for each path and the paths are ranked according to entropy. A geometrical optics algorithm is applied to the entropy data to define pathways, which are ranked according to their significance to generate a map of the pathways.

Abstract: A method and a device for encoding/decoding images are disclosed. The method for encoding images comprises the steps of: deriving a scan type of a residual signal for a current block according to whether or not the current block is a transform skip block; and applying the scan type to the residual signal for the current block, wherein the transform skip block is a block to which transform for the current block is not applied and is specified on the basis of information indicating whether or not transform for the current block is to be applied.

Abstract: In an example, a method for image processing may include inputting a first pixel value corresponding to a first pixel of an image into a LUT. The LUT may map one or more LUT input values to one or more LUT output values. The first pixel value may correspond to a first LUT input value that maps to a first LUT output value in the LUT. The first pixel may include one or more pixel values. The method may include generating a noise value for the first LUT input value. The method may include generating a first interpolated LUT output value for the first LUT input value based on the noise value. The method may include transforming the image into a transformed image using the first interpolated LUT output value.

Abstract: A system for processing text captured from rendered documents is described. The system receives a sequence of one or more words optically or acoustically captured from a rendered document by a user. The system identifies among words of the sequence a word with which an action has been associated. The system then performs the associated action with respect to the user.

Abstract: A method and a device for encoding/decoding images are disclosed. The method for encoding images comprises the steps of: deriving a scan type of a residual signal for a current block according to whether or not the current block is a transform skip block; and applying the scan type to the residual signal for the current block, wherein the transform skip block is a block to which transform for the current block is not applied and is specified on the basis of information indicating whether or not transform for the current block is to be applied.

Abstract: An adaptive device and an adaptive method for classifying objects with parallel architecture are provided. The method stores a plurality of scene parameters and a plurality of classifier parameters. The method retrieves at least one image datum and then drags frames to enclose a plurality of obstruction images of the image datum and decides a frame range according to a range of each obstruction image. The method uses a plurality of image processing units to respectively calculate a plurality of obstruction characteristic data of the obstruction images in a one-by-one parallel processing way, and chooses and calculates the scene parameters and the classifier parameters corresponding to the obstruction characteristic data to obtain a plurality of classification data. When the method determines that the classification data belongs to the obstruction classification preset, positions of enclosed obstruction images are outputted to achieve real time detection for obstructions.

Abstract: A web client may receive a run-length encoded packet of graphics data from a Remote Desktop Protocol server via a web-socket server. The web client may determine a run-length encoding algorithm for the packet based on a first color space associated with the packet. The web client may decompress the packet based on the determined run-length encoding algorithm, where decompressing the packet involves determining a plurality of commands to be performed on a canvas associated with the web client. The web client may render the graphics data on the canvas using the plurality of commands, wherein the graphics data is associated with a graphical interface for viewing and interacting with a remote desktop.

Abstract: A method and a device for encoding/decoding images are disclosed. The method for encoding images comprises the steps of: deriving a scan type of a residual signal for a current block according to whether or not the current block is a transform skip block; and applying the scan type to the residual signal for the current block, wherein the transform skip block is a block to which transform for the current block is not applied and is specified on the basis of information indicating whether or not transform for the current block is to be applied.

Abstract: Various features described herein may include ways of processing multiple images to determine whether any duplicates are among the multiple images. A hashing algorithm may be used to create a hash key of an image. Multiple hash keys corresponding to multiple images may be compared to determine whether those images are duplicate images. A root mean square algorithm may be used to further identify whether multiple images are duplicate images. An image variation engine, which uses intensity coding, may be used to display differences between images. For example, similar areas in images may be drawn with low intensity or high opacity, while different areas in images may be drawn with high intensity or low opacity.

Abstract: A method of reconstructing an image of an object, the method including: determining, by a plurality of sensors, a waveform based on the object, wherein the plurality of sensors view the object from a plurality of directions; determining, by a pre-processing module, a plurality of measurements of the object using the waveform, wherein the plurality of measurements are arranged in a vector form; determining, by an option module, a sampling matrix, a dictionary, and a noise factor, wherein the sampling matrix represents a geometric arrangement of the plurality of sensors, and the dictionary is pre-selected by the option module; estimating, by an estimation module, a coefficient vector using the measurements, the sampling matrix, and the noise factor; and reconstructing, by a reconstruction module, the image, using the coefficient vector and the dictionary.

Abstract: A system for image processing that matches a model image with an input image. The matching process includes using a feature location index for the model image.

Abstract: Systems and methods are disclosed herein for using one or more computing devices to automatically segment an object in an image by referencing a dataset of already-segmented images. The technique generally involves identifying a patch of an already-segmented image in the dataset based on the patch of the already-segmented image being similar to an area of the image including a patch of the image. The technique further involves identifying a mask of the patch of the already-segmented image, the mask representing a segmentation in the already-segmented image. The technique also involves segmenting the object in the image based on at least a portion of the mask of the patch of the already-segmented image.

Abstract: According to one embodiment, an electronic device includes circuitry. The circuitry is configured to: receive first image data for displaying a first image that comprises a plurality of regions, modify a first region of the first image by adding at least a high-frequency component based on second image data, the first region of the first image including fewer noise components than a first threshold, and output third image data for displaying the first image that comprises the plurality of regions and the modified first region.

Abstract: Disclosed herein are methods and systems for generating a user-hair-color model. One embodiment takes the form of a process that includes obtaining video data depicting a head of a user. The process also includes determining a set of line segments of pixels of the video data, wherein each line segment in the determined set of line segments intersects an upper contour of the depicted head of the user. The process also includes grouping at least some of the pixels of at least one of the line segments in the determined set of line segments into three sets of pixels based at least in part on respective color data of the pixels. The three sets of pixels include a skin-pixel set, a hair-pixel set, and a background-pixel set. The process also includes updating a user hair-color model based at least in part on the skin-pixel set.

Abstract: A method and system determines flows by first acquiring a video of the flows with a camera, wherein the flows are pedestrians in a scene, wherein the video includes a set of frames. Motion vectors are extracted from each frame in the set, and a data matrix is constructed from the motion vectors in the set of frames. A low rank Koopman operator is determined from the data matrix and a spectrum of the low rank Koopman operator is analyzed to determine a set of Koopman modes. Then, the frames are segmented into independent flows according to a clustering of the Koopman modes.

Abstract: The present technique relates to a decoding device, a decoding method, and a program which can decode an image in parallel for each of tiles of the image. A parallel decoding unit decodes the image encoded by for example an encoding method called high efficiency video coding (HEVC) in parallel for each tile used for example the encoding method called HEVC. The present technique can be applied to a decoding device for receiving and decoding an encoded stream encoded by for example the encoding method called HEVC, and generates an image.

Abstract: Methods for coding a block of video data using palette coding in a video coding are disclosed. According to one embodiment, the palette transpose flag is signaled conditionally depending on the maximum index value or the palette size. If the maximum index value or the palette size is equal to or greater than a threshold size, the palette transpose flag is signaled at an encoder side or parsed at a decoder side. Otherwise, the palette transpose flag is not signaled or parsed. In another embodiment, the last palette and the last palette size are updated by the current palette with the current palette size conditionally. For example, updating the last palette and the last palette size by the current palette with the current palette size is skipped if the current coding unit is coded using a pulse-coded modulation (PCM) mode or the current palette size is zero.

Abstract: An offset calculating unit (2) performs an offset process for a luma plane and/or chroma planes where the offset process compresses a pixel value range by a predetermined offset except for a saturation value. A compression processing unit (3) performs a lossy compression process using frequency conversion for the luma plane and the chroma planes after the offset process performed by the offset calculating unit (2). Further, a decompression processing unit (4) decompresses compressed data generated in the compression process performed by the compression processing unit (3) to a luma plane and chroma planes. An inverse offset calculating unit (5) performs an inverse offset process for a plane for which the offset process has been performed among the luma plane and the chroma planes after decompression by the decompression processing unit (4) where the inverse offset process expands a pixel value range by the offset.

Abstract: A image input means 81 inputs a lifeform image which is a captured image of a lifeform sample. A structure standard color storage means 82 stores a standard color of a structure included in the lifeform image. A structure extraction means 83 extracts a target structure from the lifeform image. A structure color computation means 84 computes, from an image of the structure extracted by the structure extraction means 83, a color of the structure. A color distribution conversion means 85 converts a color distribution of the input lifeform image so that a difference between the color of the structure computed by the structure color computation means 84 and the standard color of the corresponding structure stored in the structure standard color storage means 82 is reduced.