Kyung-Shik Jang

Lip localization based on active shape model and gaussian mixture model

This paper describes an efficient method for locating lip. Lip deformation is modeled by a statistically deformable model based on Active Shape Model(ASM). In ASM based methods, it is assumed that a training set forms a cluster in shape parameter space. However if there are some clusters in shape parameter space due to an incorrect position of landmark point, ASM may not be able to locate new examples accurately. In this paper, Gaussian mixture is used to characterize the distribution of shape parameter. The Expectation Maximization algorithm is used to determine the maximum likelihood parameters of Gaussian mixture. During search, we resolved the updated locations by projecting a shape into the shape parameter space by using Gaussian mixture. The experiment was performed on many images, and showed very encouraging result.

Off-Line signature verification based on directional gradient spectrum and a fuzzy classifier

In this paper, a method for off-line signature verification based on spectral analysis of directional gradient density function and a weighted fuzzy classifier is proposed. The well defined outline of an incoming signature image is extracted in a preprocessing stage which includes noise reduction, automatic thresholding, image restoration and erosion process. The directional gradient density function derived from extracted signature outline is highly related to the overall shape of signature image, and thus its frequency spectrum is used as a feature set. With this spectral feature set, having a property to be invariant in size, shift, and rotation, a weighted fuzzy classifier is evaluated for the verification of freehand and random forgeries. Experiments show that less than 5% averaged error rate can be achieved on a database of 500 samples including signature images written by Korean letters as well.

A recognition of map using the geometric relations between lines and the structural information of objects

A map consists of many lines, and the process to recognize an object in map is one to group lines that are consistent with the structure of the object. This paper proposes a novel method for recognizing roads and districts in land register maps. The lines that constitute an object are searched in a reduced search space by defining the some relations between lines, and objects are recognized by using the structural information.