Name
Abstract | ||
|---|---|---|
Image segmentation is a fundamental problem in computer vision, for which deformable models offer a partial solution. Most deformable models work by performing some kind of edge detection; complementary region growing methods have not often been used. As a result, deformable models that track regions rather than edges have yet to be developed to a great extent. Active region models are a relatively new type of deformable model driven by a region energy that is a function of the statistical characteristics of an image. This paper describes the use of constrained active region models for frame-rate tracking in color video images on widely available computer hardware. Two of the many color representations now in use are reviewed for this purpose: the intensity-based RGB space and the more intuitive HSV space. Normalized RGB, which is essentially a measure of hue and saturation, emerges as the preferred representation because it is invariant to illumination changes and can be obtained from many frame-grabbers via a simple fast software transformation. Three types of motion are examined for constraining deformable models: rigid models can only translate and rotate to fit image features; conformal models can also change size; affine models exhibit two kinds of shearing in addition to the other components. Two methods are described for producing affine motion, given the desired unconstrained motion calculated by searching for local energy minima lying perpendicular to the model boundary. An existing method, based on iterative gradient descent, computes translating, rotating, scaling, and shearing forces which can be combined to produce affine and other types of motion. A faster, more accurate method uses least-squares minimization to approximate the desired motion; with this method it is also possible to derive specific equations for rigid and conformal motion and to correct for the aperture problem associated with the perpendicular search method. The advantages of the new least-squares method are illustrated by using it to drive an active region model via an affine transformation which tracks the movements of a robot arm at frame rate in color video images. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1006/cviu.1997.0653 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
fast tracking,active region model,color image sequence,edge detection,computer vision,gradient descent,least square method,affine transformation,least square,region growing,robot arm,image features,shear force,aperture problem,image segmentation,color image | Affine transformation,Computer vision,Edge detection,Image processing,Image segmentation,Frame rate,RGB color model,Region growing,Artificial intelligence,Mathematics,Color image | Journal |
Volume | Issue | ISSN |
72 | 1 | Computer Vision and Image Understanding |
Citations | PageRank | References |
7 | 0.54 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jim Ivins | 1 | 89 | 10.95 |
| John Porrill | 2 | 352 | 85.11 |