Abstract: A method image data processor (318) includes a shape likelihood determiner (402) that processes voxels of image data and determines a likelihood that a voxel represents predetermined tissue of interest for a plurality of the voxels based on a shape of a tissue represented by the voxel, an opacity determiner (406) that determines an opacity suppression for each of the plurality of voxels based on the likelihood, a re-formatter (410) that re-formats the image data based on the determined opacity suppression, generating opacity suppressed re-formatted data, and a rendering engine (412) that visually presents the opacity suppressed re-formatted data.

Abstract: An image processor includes: a first division control information acquiring circuit configured to acquire division control information of a first chip; a second division control information acquiring circuit configured to acquire division control information of a second chip; a division process control circuit configured to determine a ratio at which a process is performed by the first image processing circuit and a ratio at which a process is performed by the second image processing circuit, based on the division control information of the first chip and the division control information of the second chip; a division circuit configured to divide data to be subjected to image processing into first-chip data and second-chip data at the determined ratios; the first image processing circuit configured to perform image processing on the first-chip data; and the second image processing circuit configured to perform image processing on the second-chip data.

Abstract: An information processing apparatus includes an image capturing section to capture an image of a hand; an extracting section to extract a hand area from the captured image; a reference line determining section to determine a reference pushdown line in the image on the hand area; a determining section to determine a pushdown move if the bottom part of the hand area comes below the reference pushdown line; a first position determining section to determine a depth position based on an aspect ratio of the hand area if the pushdown move is determined; a second position determining section to determine a lateral position based on a position of the bottom part of the hand area if the pushdown move is determined; and an input key determining section to determine an input key from the determined depth position and lateral position.

Abstract: Disclosed are object recognizing method and apparatus. The method may comprise extracting a feature point of an object to be recognized in an input image; generating a plurality of feature images by extracting orientation information of a region adjacent to the feature point and normalizing the region based in the orientation information; deriving a covariance descriptor based on the feature images; and recognizing the object in the input image by comparing the covariance descriptor with a reference covariance descriptor stored in advance. Thus, the object can be recognized fast and robustly to changes on the size of the input image, rotations of the input image, and illumination, whereby the recognition performance can be enhanced.

Abstract: A multi-object zonal traffic detection framework analyzes temporal and spatial information from one or more sensors in a classification engine that identifies and differentiates objects within a single identified traffic detection zone. The classification engine applies a whole scene analysis and an adaptive background zone detection model to classify cars, trucks, bicycles, pedestrians, incidents, and other objects within the single identified traffic detection zone and generates counts for each object type for traffic system management.

Abstract: The disclosure relates to a method for processing an image, the method comprising: providing a set of key points from the image; describing location information of the set of key points in form of a binary matrix; and scanning the binary matrix according to a pre-determined order, thereby creating a new representation of the location information of the set of key points.

Abstract: A system to recognize text, objects, or symbols in a captured image using machine learning models reduces computational overhead by generating a plurality of thumbnail versions of the image at different downscaled resolutions and aspect ratios, and then processing the downscaled images instead of the entire image, or sections of the entire image. The downscaled images are processed to produce a combine feature vector characterizing the overall image. The combined feature vector is processed using the machine learning model.

Abstract: According to one method, the method occurs at a data file analyzer. The method includes identifying at least one document identifier associated with a first document in a portable document format (PDF) file. The method further includes determining, using the at least one document identifier, a reference point identifier for identifying a reference point in the first document, an offset value for indicating a location of a first detection area in the first document, and size information for indicating a size of the first detection area in the first document. The method also includes identifying, using a reference point identifier, the reference point in the first document. The method further includes identifying, using the offset value and the size information, the first detection area in the first document and extracting, by processing binary data of the PDF file, data within the first detection area of the first document.

Abstract: An image measurement apparatus includes: an image acquiring unit that acquires a sample image that is an image obtained by capturing an image of a sample; a standard color space converting unit that converts the sample image into a standard sample image that is an image in a standard color space; a color distribution normalizing unit that converts the standard sample image into a normalized sample image by converting pixel values of pixels in the standard sample image to cause a color distribution vector indicating a color distribution of the standard sample image to match a predetermined standard color distribution vector indicating a standard color distribution; and a positive rate acquiring unit that detects positive pixels by performing threshold processing on pixel values of pixels in the normalized sample image, and acquires a positive rate indicating a proportion of the detected positive pixels.

Abstract: An information processing device includes an image acquirer that acquires a shot image of a user, a registered user information holder that holds face identification data of a registered user, a face recognition section that detects a face image of a registered user existing in the shot image by using face identification data held in the registered user information holder, and an information processing section that executes information processing based on a detection result by the face recognition section. The face identification data includes information on a face image of a user shot in advance and an after-processing image obtained by performing predetermined processing on the face image.

Abstract: An information processing method includes calculating a second spatial filter having a size of the number of elements larger than a blur size of an image using a finite first spatial filter having an anisotropy in resolution of the image and a finite filter in which a value of a total sum of elements is zero and at least two elements have a non-zero value, and generating a plurality of spatial filters having a predetermined number of elements or less from the second spatial filter.

Abstract: A system and method using a native mobile application for gardening is disclosed herein. An image of a plant or plant disease is uploaded and sent to a site for identification and advise from an expert or expert database. A response is provided to the gardener, along with helpful information.

Abstract: Devices, terminals and methods are provided for image processing. For example, a raw image is loaded to be viewed, the raw image being selected by a user terminal; a first image to be displayed is obtained by analyzing and encoding the raw image, the first image being of a predetermined image format; one or more preset image display parameters are acquired; a quality score of the first image is obtained by performing quality analysis and calculation for the first image using the preset image display parameters; and the first image and the quality score of the first image are displayed to the user terminal.

Abstract: A burden on a user is reduced by referring to a measurement region set for a certain measurement tool among a plurality of measurement tools to be used by an appearance inspection device as a measurement region of another measurement tool. A reference point or a search region serving as a reference of measurement for a first measurement tool such as a connector dimension inspection tool is set for a basic image acquired by imaging a non-defective product. Next, a second measurement tool such as an area measurement tool that performs measurement separate from the first measurement tool is selected by the user. For coordinate data of a measurement region, which is a region to be measured by the second measurement tool, coordinate data of the reference point or the search region set for the first measurement tool is adjusted if necessary and referred to.

Abstract: The disclosure provides an approach for generating a sparse representation of a light field. In one configuration, a sparse representation application receives a light field constructed from multiple images, and samples and stores a set of line segments originating at various locations in epipolar-plane images (EPI), until the EPIs are entirely represented and redundancy is eliminated to the extent possible. In addition, the sparse representation application determines and stores difference EPIs that account for variations in the light field. Taken together, the line segments and the difference EPIs compactly store all relevant information that is necessary to reconstruct the full 3D light field and extract an arbitrary input image with a corresponding depth map, or a full 3D point cloud, among other things. This concept also generalizes to higher dimensions. In a 4D light field, for example, the principles of eliminating redundancy and storing a difference volume remain valid.

Abstract: A method of decomposing an image or video into a plurality of components. The method comprises: obtaining (10) an intensity signal of the image or video; and decomposing (30) the intensity signal into a component representing the three-dimensional structure shape of one or more objects in the scene and at least one other component. Also provided is a method of performing an Empirical Mode Decomposition on data in two or more dimensions, using normalized convolution.

Abstract: In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of a plurality of superpixels of an input image and an object selection input for selecting part of a foreground object in the input image. The method includes determining first set of superpixels as first set of foreground seeds and second set of superpixels as first set of background seeds. The method includes filtering the first set of background seeds to generate second set of background seeds based on geodesic distances. The method includes adding superpixels of first set of unclassified superpixels to the second set of background seeds to generate third set of background seeds, and adding superpixels of second set of unclassified superpixels to the third set of background seeds to generate fourth set of background seeds based on applying a classifier on the second set of unclassified superpixels.

Abstract: A sequence of video images is generated of a pavement area of an airport which contains one or more objects. A processor accesses a background model of the pavement area and determines in a current image a single cluster of foreground pixels that is not part of the background model and assigns a first value to each foreground pixel in the cluster to create a foreground mask. The background model is updated by learning new conditions. A convex hull is generated from the foreground mask. A ratio is determined from pixels captured by the complex hull and pixels in the foreground mask. A ratio higher than a threshold value indicates an object not being an airplane and an alert is displayed on a computer display. Images may be thermal images. A surveillance system based on the calculated ratio is disclosed.

Abstract: A method, a data network arrangement and a computer program product, by means of which a comparator can be implemented in a data network, in which comparator a possibility of online chat can also be utilized. The comparator user stores his evaluation of the properties included in the targets being evaluated in a database in the server in the data network by using a graphic one- or two-dimensional evaluation frame. In the comparator the comparator user's evaluations are compared to property values set for each target being evaluated. During the comparison the comparator analyses the data entered into the comparator by the comparator user against predetermined online chat threshold values. If even one online chat threshold value is exceeded, it is in the comparator according to the invention possible to offer the comparator user an online chat with the service provider's customer servant in order to support the decision-making.

Abstract: Disclosed herein are systems and methods for extracting person image data comprising: obtaining at least one frame of pixel data and corresponding image depth data; processing the at least one frame of pixel data and the image depth data with a plurality of persona identification modules to generate a corresponding plurality of persona probability maps; combining the plurality of persona probability maps to obtain an aggregate persona probability map; and generating a persona image by extracting pixels from the at least one frame of pixel data based on the aggregate persona probability map.