Abstract: Methods and apparatus for construction of an object shape from an image using a light-space graphical model are disclosed. A set of normal vectors for a set of pixels in an image is defined. Each normal vector is defined in terms of an azimuth and a zenith measured in a spherical coordinate system centered on a light source illuminating the image. The zenith of each normal vector is constrained based on an observed shading of a respective pixel. A shape is constructed from the image. Constructing the shape includes minimizing an energy function to specify an azimuth value and a zenith value of each normal vector. Minimizing the energy function further includes constraining the azimuth of each normal vector based on an image gradient of the image at each respective pixel to enforce a coplanar assumption between the image gradient expressed in a three-dimensional space and the respective normal vector.

Abstract: An analytical device is disclosed that analyzes whether a first image is similar to (or the same as) as a second image. The analytical device analyzes the first image by combining at least a part (or all) of the first image with at least a part (or all) of the second image, and by analyzing at least a part (or all) of the combined image. Part or all of the combination may be analyzed with respect to the abstraction of the first image and/or the abstraction of the second image. The abstraction may be based on a Bag of Features (BoF) description, based on a histogram of intensity values, or based on other types of abstraction methodologies. The analysis may involve comparing one or more aspects of the combination (such as the entropy or randomness of the combination) with the one or more aspects of the abstracted first image and/or abstracted second image. Based on the comparison, the analytical device may determine whether the first image is similar to or the same as the second image.

Abstract: A method of tracking a target includes classifying a pixel having a pixel address with one or more pixel cases. The pixel is classified based on one or more observed or synthesized values. An example of an observed value for a pixel address includes an observed depth value obtained from a depth camera. Examples of synthesized values for a pixel address include a synthesized depth value calculated by rasterizing a model of the target; one or more body-part indices estimating a body part corresponding to that pixel address; and one or more player indices estimating a target corresponding to that pixel address. One or more force vectors are calculated for the pixel based on the pixel case, and the force vector is mapped to one or more force-receiving locations of the model representing the target to adjust the model representing the target into an adjusted pose.

Abstract: A visual target tracking method includes representing a human target with a machine-readable model configured for adjustment into a plurality of different poses and receiving an observed depth image of the human target from a source. The observed depth image is compared to the model. A refine-z force vector is then applied to one or more force-receiving locations of the model to move a portion of the model towards a corresponding portion of the observed depth image if that portion of the model is Z-shifted from that corresponding portion of the observed depth image.

Abstract: A target tracking method includes modeling the target in a first frame with a first frame iteration of a machine-readable model and receiving an observed depth image of a second frame of a scene including the target. The first frame iteration of the machine-readable model is then adjusted into a second frame iteration of the machine-readable model based on the observed depth image of the second frame.

Abstract: A target tracking method includes representing a human target with a machine-readable model configured for adjustment into a plurality of different poses and receiving an observed depth image of the human target from a source. One or more push force vectors are applied to one or more force-receiving locations of the model to push the model in an XY plane towards a silhouette of the human target in the observed depth image when portions of the model are shifted away from the silhouette of the human target in the observed depth image. One or more pull force vectors are applied to one or more force-receiving locations of the model to pull the model in an XY plane towards the silhouette of the human target in the observed depth image when portions of the observed depth image are shifted away from the silhouette of the model.

Abstract: A target tracking method includes representing a human target with a machine-readable model configured for adjustment into a plurality of different poses. The machine-readable model includes a plurality of joints, including one or more magnetism joints, and each joint has a three-dimensional world space position. The method further includes receiving an observed depth image of the human target from a source. The observed depth image includes a plurality of observed pixels. A magnetism body part is assigned to one or more of the plurality of observed pixels, and a magnetism joint position is estimated based on world space positions of the one or more observed pixels assigned the magnetism body part. A joint of the machine-readable model is then shifted toward the magnetism joint position.

Abstract: A computer-implemented method includes segmenting an input image into a plurality of image cues, each image cue representing a unique set of pixels of the input image. For each image cue, the method includes determining a set of image components, wherein each image component is associated with at least one adjustable factor to represent at least one characteristic of the image cue.

Abstract: Segmenting the prostate boundary is essential in determining the dose plan needed for a successful bracytherapy procedure—an effective and commonly used treatment for prostate cancer. However, manual segmentation is time consuming and can introduce inter and intra-operator variability. This present invention describes an algorithm for segmenting the prostate from two dimensional ultrasound (2D US) images, which can be full-automatic, with some assumptions of image acquisition. Segmentation begins with the user assuming the center of the prostate to be at the center of the image for the fully-automatic version. The image is then filtered to identify prostate edge candidates. The next step removes most of the false edges and keeps as many true edges as possible. Then, domain knowledge is used to remove any prostate boundary candidates that are probably false edge pixels.

Abstract: The disclosure relates generally to receiving original image data, decomposing the original image data into layers, compressing a dynamic range of each of layers, and integrating compressed layers to form a final image.

Abstract: An image processing apparatus for processing and outputting image data includes a image processing content storage unit for storing contents of an image processing; a thumbnail magnification ratio determining unit for determining a magnification ratio of a thumbnail image according to the contents of the image processing thus stored; a thumbnail image creation unit for creating the thumbnail image from the image data according to the magnification ratio; and an image processing unit for applying the image processing to the thumbnail image thus created according to the contents of the image processing.

Abstract: An image processing apparatus is provided, the apparatus including: a color extracting section that extracts colors contained in processing image data as a flat color or a characteristic color; a first evaluation value calculating section that calculates a flat color evaluation value with respect to a combination of flat colors based on a color difference, and that calculates a characteristic color evaluation value with respect to a combination of characteristic colors based on a color difference; a second evaluation value calculating section that calculates a combination evaluation value with respect to a combination of a characteristic color and a flat color based on a color difference; and a deleting section that reduces a number of colors to a preset number by deleting a color extracted by the color extracting section in accordance with the characteristic color evaluation value, flat color evaluation value and the combination evaluation value.

Abstract: Described is a technology in which an image retrieval system is updated incrementally as new image data becomes available. Updating is incrementally performed and only triggered when the new image data is large enough or diverse enough relative to the image data currently in use for image retrieval. Incremental updating updates the leaf nodes of a vocabulary tree based upon the new image data. Each leaf node's feature frequency is evaluated against upper and/or lower threshold values, to modify the nodes of the tree based on the feature frequency. Upon completion of the incremental updating, a server that performed the incremental updating is switched to an active state with respect to handling client queries for image retrieval, and another server that was actively handling client queries is switched to an inactive state, awaiting a subsequent incremental updating before switching back to active state.

Abstract: In an image position recognition apparatus, an image signal acquirer acquires, from an image capture apparatus, red, blue, and green image signals obtained by capturing the picture from an image display apparatus. A position recognition unit uses the acquired red, blue, and green image signals as a basis for recognizing the position in a captured picture corresponding to a given region among respective regions obtained by plurally dividing the picture from the image display apparatus. When the position recognition unit recognizes the position in the captured picture corresponding to the given region of the picture from the image display apparatus, a correction data setting unit sets data for emphasizing the given region as the correction data corresponding to the given region of the red, blue, and green image signals in the image display apparatus.

Abstract: Provided is an image coding method for performing intra prediction achieving higher coding efficiency. The method for coding image data on a block-by-block basis includes: generating a predicted block by predicting a current block; computing a difference between the current block and the predicted block; coding the difference computed in the computing; decoding the difference coded in the coding; and adding the difference decoded in the decoding to the predicted block to generate a decoded block, wherein the generating includes: detecting an edge in a previously decoded block corresponding to a block adjacent to the current block; and extrapolating or interpolating previously decoded image data corresponding to a pixel included in the block adjacent to the current block, along a direction of the edge detected in the detecting to generate the predicted block.

Abstract: A system and method for detecting a waving motion from a sequence of ordered points is disclosed. In one embodiment, the method comprising receiving a sequence of ordered points, selecting a subset of the sequence of ordered points, determining if the subset defines a circular shape, and storing an indication of whether or not the subset defines a waving motion. Various metrics for determining if the subset defines a waving motion, which allow for a trade-off between accuracy and complexity, are disclosed.

Abstract: The present invention relates to color correction of video signals from a plurality of cameras. The apparatus has a correction order that defines a plurality of camera pairs and an order of the camera pairs, where each camera pair specifies a reference camera and a target camera. In the correction order, each camera is treated as a target camera at least one time, and the reference camera is treated as the target camera in a previous camera pair except for the first camera pair. The apparatus corrects a value of each pixel in a target image captured by the target camera based on a reference image captured by the reference camera, and replaces the target image by the corrected target image, for each camera pair in accordance with the order of the camera pairs.

Abstract: Disclosed herein is an information processing apparatus including: a partial decoding block configured to generate picture data by partially decoding a reversibly encoded picture code stream; an irreversible encoding block configured to encode irreversibly the picture data generated by the partial decoding block; and a control section configured to control the partial decoding block to determine the picture code stream part to be decoded in such a manner that a target compression rate of the picture data generated by the partial decoding block constitutes the compression rate corresponding to a target code quantity for the irreversible encoding block.

Abstract: A system reads a digital image that includes a set of pixels. The system determines a distribution of colors of the pixels of the digital image. Also, the system analyzes the distribution to identify a range of colors of a background of the digital image. Further, the system identifies pixels whose colors are within the identified range, and the system modifies the identified pixels to have a different color.

Abstract: A video analysis technique includes correlating frames from a processed video with frames from a pre-processed, original video. A linear approximation of a relationship between the correlated frames is determined. A disclosed example includes determining a linear approximation that maximizes the number of processed video frames that fit into the linear approximation. The linear approximation and whether any frames do not fit within the linear approximation is then used to provide quality information for analyzing a quality of the processed video.