Gang Xu

Robust active contours with insensitive parameters

Abstract Active contours, known as snakes, have found wide applications since their first introduction in 1987 by Kass et al. ( Int. J. Comput. Vision 1, 321–331). However, one problem with the current models is that the performance depends on proper internal parameters and initial contour position, which, unfortunately, cannot be determined a priori. It is usually a hard job to tune internal parameters and initial contour position. The problem comes from the fact that the internal normal force at each point of contour is also a function of contour shape. To solve this problem, we propose to compensate for this internal normal force so as to make it independent of shape. As a result, the new model works robustly with no necessity to fine-tune internal parameters, and can converge to high curvature points like corners.

A Motion Stereo Method Based on Coarse-to-Fine Control Strategy

This correspondence presents a motion stereo method based on coarse-to-fine control strategy. A camera sliding straight takes images that form a set of stereo pairs. The matching proceeds from the shortest baseline pair to the longest baseline pair, using the disparity map already obtained to guide in searching for the next pair.

A robust active contour model with insensitive parameters

Active contours, known as snakes, have found wide application since their first introduction in 1987 by M. Kass, A. Witkin and D. Terzopoulos. However, one problem with the current models is that the performance depends on proper internal parameters and initial contour position, which, unfortunately, cannot be determined a priori. It is usually difficult to tune internal parameters and initial contour position. The problem results from the fact that the internal normal force at each point of a contour is also a function of contour shape. To solve this problem, the authors propose to compensate for this internal normal force so as to make it independent of shape. As a result the new model works robustly with no necessity for tuning internal parameters and can converge to high curvature points like corners.<<ETX>>

Right straight homogeneous generalized cylinders with symmetrical cross-sections: recovery of pose and shape from image contours

The straight homogeneous generalized cylinder (SHGC) comprises a class of objects for which recovery of pose and shape from image is generally an underconstrained problem. It is shown that for a major subclass of SHGCs, namely, the right straight homogeneous generalized cylinders, the 3-D pose (tilt and slant) and shape (cross section and scaling function) can be completely determined if the cross sections are symmetrical. From the mutual orthogonality of the cylinder axis, the symmetry axis and transverse axis of the cross section, their slants can be determined from their tilts, the 2-D orientations of their projections onto the image. The 2-D cylinder axis and skewed symmetry axis of the cross sections are extracted by using the property that tangents to the image curves at corresponding points meet on the axes. Once the pose is recovered, the cross section and scaling function of the object can also be determined from the cross section contour and extremal contours, respectively.<<ETX>>

Minimal region extraction using expanding active contours

Segmenting images into objects is the first step towards object learning and recognition. The authors take a three-stage approach to this problem: (1) junctions and corners are detected from the image; (2) the minimal regions are extracted by applying an expanding active snake model to detect edge contours through junctions and corners, resulting in an image composed of closed regions; and to (3) merge regions that are depth-continuous, and separate regions at the depth discontinuities, using constraints imposed by the junction types. In this paper the second step is described.<<ETX>>