A geometric pattern matching method and a device for performing the method includes determining, by a geometric pattern matching device, information on reference geometric pattern contour points for a learning object on a learning image. The method can further include determining, by the geometric pattern matching device, information on detection object contour points for a detection object on a detection image, and performing, by the geometric pattern matching device, geometric pattern matching between the learning object and the detection object on the basis of the information on the reference geometric pattern contour points and the information on the detection object contour points.

An integrated gaze tracker includes a pixel array configured to capture an image to provide an output signal comprising the image; a memory configured to store an image data unit to be read out in units of lines based on the output signal of the pixel array; and a processor configured to generate a gaze vector based on partial image data comprising the image data unit, among entire image data included in the output signal.

Enhanced vectorization of raster images is described. An image vectorization module converts a raster image with bitmapped data to a vector image with vector elements based on mathematical formulas. In some embodiments, spatially-localized control of a vectorization operation is provided to a user. First, the user can adjust an intensity of a denoising operation differently at different areas of the raster image. Second, the user can adjust an automated segmentation by causing one segment to be split into two segments along a zone marked with an indicator tool, such as a brush. Third, the user can adjust an automated segmentation by causing two segments to be merged into a combined segment. The computation of the vector elements is based on the adjusted segmentation. In other embodiments, semantic information gleaned from the raster image is incorporated into the vector image to facilitate manipulation, such as joint selection of multiple vector elements.

A method of transmitting an image is performed by a first device and includes obtaining an image including an object, obtaining, based on a preset setting condition of the first device, contour information defining a contour of the object and color information about a color of the object, and transmitting the contour information and the color information to a second device.

A method for producing digital ink in a collaboration session between a first computing device and a second computing device that presents a digital canvas. In some embodiments, the method includes capturing a raster image of content using a camera operably coupled to the first computing device, deriving first image vectors and second image vectors based on first and second portions, respectively, of the raster image, sending the first image vectors to the second computing device for displaying a first digital ink object based on the first image vectors, and sending the second image vectors to the second computing device for displaying a second digital ink object based on the second image vectors after the displaying of the second digital ink object.

Various embodiments provide methods and systems for detecting one or more segments of an image that are related to a particular object in the image (e.g., a logo or trademark) and extracting at least one feature point, each of which is represented by one feature point descriptor, based at least upon a contour curvature of the one or more segments. The at least one feature point descriptor can be converted into one or more codewords to generate a codeword database. A discriminative codebook can then be generated based upon the codeword database and utilized to detect objects and/or features in a query image.

A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Enhanced vectorization of raster images is described. An image vectorization module converts a raster image with bitmapped data to a vector image with vector elements based on mathematical formulas. In some embodiments, spatially-localized control of a vectorization operation is provided to a user. First, the user can adjust an intensity of a denoising operation differently at different areas of the raster image. Second, the user can adjust an automated segmentation by causing one segment to be split into two segments along a zone marked with an indicator tool, such as a brush. Third, the user can adjust an automated segmentation by causing two segments to be merged into a combined segment. The computation of the vector elements is based on the adjusted segmentation. In other embodiments, semantic information gleaned from the raster image is incorporated into the vector image to facilitate manipulation, such as joint selection of multiple vector elements.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

Methods for characterizing two-dimensional concept drawings are disclosed. The concept drawings comprise cross-sections intersecting at cross-hairs. The method comprises: determining, for each cross-section: a plane on which the cross-section is located, the plane having a normal vector in a three-dimensional coordinate system; and, for each cross-hair on the cross-section, a tangent vector in the three-dimensional coordinate system which is tangent to the cross-section at the cross-hair. For each cross-hair comprising i.sup.th and j.sup.th intersecting cross-sections, one or more constraints are satisfied, the constraints comprising: the normal vector n.sub.i of the plane on which the i.sup.th cross-section is located is at least approximately orthogonal to the normal vector n.sub.j of the plane on which the j.sup.th cross-section is located; and the tangent vector t.sub.ij to the i.sup.th cross-section at the cross-hair is at least approximately orthogonal to the tangent vector t.sub.ji to the j.sup.th cross-section at the cross-hair.