Peking University

Xerox Corporation

Bingfeng Zhou

Jie Feng

Yadong Mu

Zhigang Fan

Francis Kapo Tse

Bencong Song

Tao He

He Tao

Apparatus and methods disclosed herein provide for a set of reference images obtained from a camera and a reference image obtained from a viewpoint to capture an entire concave region of an object; a silhouette processing module for obtaining a silhouette image of the concave region of the object; and a virtual-image synthesis module connected to the silhouette processing module for synthesizing a virtual inside-out image of the concave region from the computed silhouette images and for generating a visual hull of the object having the concave region.

Concave surface modeling in image-based visual hull

A system and method of connectivity-based image processing to identify and extract objects in image data is described. Variations on the method may include iterative local smoothing operations and various algorithmic solutions to improve real-time processing. Variations may also include object extraction processes based on user-provided information about an object in an image.

Connectivity similarity based graph learning for interactive multi-label image segmentation

This disclosure relates to simulating the light-reflective condition of an object when situated in a given environment. A spatial irradiance mapping of the environment may be obtained, from which a series of directional incidence light sources are determined. The reflective qualities of the object may be modeled as a bi-directional reflection distribution function to be applied to directional incidence light sources. The spatial irradiance mapping and/or bi-directional reflection distribution function may be obtained according to image-based techniques.

Image-based lighting simulation for objects

What is disclosed is a novel system and method for text vectorization for bitmap images with reduced artificial discontinuities. Dominant points are identified in a bitmap character image. An initial curve is fitted to edge points of the character image in a vicinity of a selected dominant point. A set of boundary parameters in a vicinity of the selected dominant point are estimated based upon the initial curve. The selected dominant point is then classified as one of a sharp dominant point and a smooth dominant point based upon the boundary parameters or alternatively upon predefined classifications produced by an optical character recognition process. Curves are fitted between the selected dominant point and adjacent dominant points. The fitted curves maintain the estimated boundary parameters in the vicinity of smooth dominant points. A vectorized representation of the text character image based upon the fitted curves is produced as output.

Between-segment discontinuity reduction for text vectorization using dominant point classification

Systems and methods are described that facilitate dominant point detection for text in a scanned document. The dominant points are classified as 'major' (e.g., structural) and 'minor' (e.g., serif). A set of rules or parameters for each character is determined off-line. During the text vectorization, OCR is performed and the rules (parameters) associated with the recognized character are selected. Both major and minor dominant points are detected as a maximization process with the parameter set. For minor dominant points, additional processes are optionally employed.

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Beijing, China

Bingfeng Zhou