The disclosure discloses a method and device for image recognition. The method comprises: placing instruction blocks in a required order; acquiring an encoded instruction block image that represents the instruction block pattern and the placement order; recognizing a single instruction block image from the encoded instruction block image; comparing the single instruction block image with a standard instruction block image to obtain the matching degree of the instruction block; determining, according to the matching degree, standard instruction blocks corresponding to respective instruction block images; sorting the instruction block images according to the coordinates in the encoded instruction block image; parsing the instruction block images according to the sorting order to obtain instructions. The method for image recognition can help children learning concepts such as manipulation, use and programming of intelligent programmable devices more easily.

Embodiments of an automated method of processing fingerprint images, identity information is extracted from prints typically classified as having no identification value because of sparse or missing minutiae by capturing ridge contour information. Bezier approximations of ridge curvature are used as Ridge Specific Markers. Control points arising from Bezier curves generate unique polygons that represent the actual curve in the fingerprint. The Bezier-based descriptors are then grouped together and compared to corresponding reference print Ridge Specific Marker data. The method makes it possible to fuse a plurality of individual latent print portions into a single descriptor of identity and use the resulting data for comparison and identification. Processing of poor quality reference prints according to the methods disclosed renders these prints useable for reference purposes.

An electronic apparatus for recognizing a user and a method therefor are provided. The electronic apparatus includes a communication interface, a dynamic vision sensor (DVS), a memory including a database in which one or more images are stored, and at least one processor. The at least one processor is configured to generate an image, in which a shape of an object is included, based on an event detected through the DVS, control the memory to store a plurality of images generated under a specified condition, in the database, identify shapes of the user included in each of the plurality of images stored in the database, and generate shape information for recognizing the user based on the identified shapes. The plurality of images may include a shape of a user.

An image processing device includes a data processing unit that processes data of a face image which is captured to include a face. The data processing unit generates an edge image by filtering the face image to detect an edge in a scanning direction, extracts a sampling value as the information regarding a gradient magnitude and whether the gradient is positive or negative from each of positions in the edge image corresponding to a plurality of points constituting the sampling curve, calculates a likelihood with respect to the sampling curve by setting points having a positive gradient and a negative gradient as likelihood evaluation targets in a first point group and a second point group, and detects a sampling curve having a maximum likelihood as a pupil or an iris among a plurality of sampling curves.

According to an embodiment, a recognition device includes a detector, a recognizer, and a matcher. The detector is configured to detect a character candidate from an input image. The recognizer is configured to generate recognition candidate from the character candidate. The matcher is configured to match the recognition candidate with a knowledge dictionary and contains modeled character strings to be recognized, and generate a matching result obtained by matching a character string presumed to be included in the input image with the dictionary. Any one of a real character code that represents a character and a virtual character code that specifies a command is assigned to an edge. The matcher gives, when shifting a state of the dictionary in accordance with an edge to which the virtual character code is assigned, a command specified by the virtual character code assigned to the edge to a command processor.

A work region evaluating system includes an imaging unit and a processing unit. The processing unit includes an image processing section that converts a three-dimensional image of a vehicle body component recorded by the imaging unit to three-dimensional image data, an extraction section that extracts work regions and reference regions respectively from both the three-dimensional image data and three-dimensional design data, an alignment section that aligns the three-dimensional image data with the three-dimensional design data with reference to the reference regions, and an evaluation section that evaluates a relation between the work region extracted from the three-dimensional image data and the work region extracted from the three-dimensional design data, based on the three-dimensional image data and the three-dimensional design data which are aligned with each other by the alignment section.

A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments include receiving a matrix formulation of at least a portion of the LP problem representing a supply chain planning problem for a supply chain network, generating an image based on the matrix formulation to identify connected components, partitioning the matrix formulation based, at least in part, on the connected components constraint into at least two partitions, formulating an LP subproblem from each of the at least two partitions, and solving the LP subproblems to generate a global solution to the supply chain planning problem.

Technologies and techniques for vehicle perception. Contours are extracted from image data, and a first and second contour of a current image and next image are determined along a plurality of image row boundaries, wherein the contours includes a plurality of contour points linked to each other via respective edges. An image velocity vector is applied to project each of the plurality of first contour points to the next image, wherein at least some of the projected first contour points are not located on an image row boundary. The projected contour points are processed to determine projected edges forming a projected link between each projected contour point. The projected contour points are moved along the projected link to align each of the moved projected contour points to a respective image boundary. Each of the moved projected contour points are then linked to each of the second contour points.

A headphone for spatial audio rendering includes a first database having an impulse response pair corresponding to a reference speaker location. A head sensor provides head orientation information to a second database having rotation filters, the filters corresponding to different azimuth and elevation positions relative to the reference speaker location. A digital signal processor combines the rotation filters with the impulse response pair to generate an output binaural audio signal to transducers of the headphone. Efficiencies in creating impulse response or HRTF databases are achieved by sampling the impulse response less frequently than in conventional methods. This sampling at coarser intervals reduces the number of data measurements required to generate a spherical grid and reduces the time involved in capturing the impulse responses. Impulse responses for data points falling between the sampled data points are generated by interpolating in the frequency domain.

A system and method are provided for segmentation of an anatomical structure in which a user may interactively specify a limited set of boundary points of the anatomical structure in a view of a medical image. The set of boundary points may, on its own, be considered an insufficient segmentation of the anatomical structure in the medical image, but is rather used to select a segmentation model from a plurality of different segmentation models. The selection is based on a goodness-of-fit measure between the boundary points and each of the segmentation models. For example, a best-fitting model may be selected and used for segmentation of the anatomical structure. It is therefore not needed for the user to delineate the entire anatomical structure, which would be time consuming and ultimately error prone, nor is it needed for a segmentation algorithm to autonomously have to select a segmentation model, which may yield an erroneous selection.