Interactive Imaging via Hand Gesture Recognition.

With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. As a sub-field, Interactive Image Processing emphasizes particularly on the communications between machine and human. The basic flowchart is definition of object, analysis and training phase, recognition and feedback. Generally speaking, the core issue is how we define the interesting object and track them more accurately in order to complete the interaction process successfully. This thesis proposes a novel dynamic simulation scheme for interactive image processing. The work consists of two main parts: Hand Motion Detection and Hand Gesture recognition. Within a hand motion detection processing, movement of hand will be identified and extracted. In a specific detection period, the current image is compared with the previous image in order to generate the difference between them. If the generated difference exceeds predefined threshold alarm, a typical hand motion movement is detected. Furthermore, in some particular situations, changes of hand gesture are also desired to be detected and classified. This task requires features extraction and feature comparison among each type of gestures. The essentials of hand gesture are including some low level features such as color, shape etc. Another important feature is orientation histogram. Each type of hand gestures has its particular representation in the domain of orientation histogram. Because Gaussian Mixture Model has great advantages to represent the object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample of each different gesture, the comparability between testing image and samples of each type of gestures will be estimated by Expectation-Maximization algorithm in Gaussian Mixture Model. The performance of this approach in experiments shows the proposed method works well and accurately

Segment-Based Hand Pose Estimation

The work presented here solves two major problems of hand pose recognition: (A) determining what pose is shown in a given, input picture and (B) detecting the presence of a known input pose in a given input video. It builds on the earlier work of Athitsos and Sclaroff (2003) toward solving problem A. Because that method relies upon lines found in the input data and requires computer-generated database models, it is unsuitable for the later, video problem. Our reworking of this framework uses different, region-based information to allow video frames to be used as the database in which to look for the test pose. It returns database images of hands in the same configuration as a query image by using a series of steps based on the number and direction of visible finger protrusions, Chamfer distance, orientation histograms, and a competitive, comparison-based matching of each visible finger segment. Detailed result data demonstrates the system\u27s feasibility and potential