Unlike conventional photographic images that are characterized only by light intensity or signal energy, laser radar (ladar) range data, or other terrain imagery, contain distance information. Range images are traditionally processed for their three-dimensional content. Previous innovations in partial differential equation (PDE) and total variation based segmentation techniques show good results for conventional images. Fast, efficient variational segmentation techniques that use higher order image models are needed for the preprocessing of such data for applications including target detection and acquisition, compression, and image modeling. We develop a variational segmentation algorithm using higher-order piecewise smooth models for multichannel range imagery such as ladar images. The algorithm is computationally stable, and a fast solution may be found using the fast Cholesky decomposition and a modified binary search tree.

To use wavelet packets for lossy data compression, the following issues must be addressed: quantization of the wavelet subbands, allocation of bits to each subband, and best-basis selection. We present an algorithm for wavelet packets that systematically identifies all bit allocations/best-basis selections on the lower convex hull of the rate-distortion curve. We demonstrate the algorithm on tree-structured vector quantizers used to code image subbands from the wavelet packet decomposition.

In this paper, an algorithm for wavelet packet trees that can systematically identify all bit allocations/best-basis selections on the lower convex hull of the rate-distortion curve is presented. The algorithm is applied to tree-structured vector quantizers used to code image subbands that result from the wavelet packet decomposition. This method is compared to optimal bit allocation for the discrete wavelet transform.

We present an algorithm for image browsing systems that embeds the output of binary Floyd-Steinberg (1975) error diffusion, or a low bit-depth gray-scale or color error diffused image into higher bit-depth gray-scale or color error diffused images. The benefits of this algorithm are that a low bit-depth halftoned image can be directly obtained from a higher bit-depth halftone for printing or progressive transmission simply by masking one or more bits off of the higher bit-depth image. The embedding can be done in any bits of the output, although the most significant or the least significant bits are most convenient. Due to constraints on the palette introduced by embedding, the image quality for the higher bit-depth halftone may be reduced. To preserve the image quality, we present algorithms for color palette organization, or binary index assignment, to be used as a preprocessing step to the embedding algorithm.

We address the problem of bit allocation to wavelet subbands by extending the recursive optimal pruning algorithm of Kiang, Baker, Sullivan and Chiu (see IEEE Transactions on Image Processing, vol.1, no.4, p.162-9, 1992) to bit allocation. We apply the algorithm to tree-structured vector quantizers used to code image subbands that result from the wavelet decomposition. We compare this method to the GBFOS algorithm, that is, the generalized Breiman, Friedman, Olshen, and Stone (1984) bit allocation, and show that it produces many additional bit allocations that lie close to the rate-distortion curve.

We present an algorithm for image browsing systems that embeds a color halftone into another color halftone of a higher bit depth, as well as embedding a grayscale or binary halftone into a grayscale or color halftone. The benefit of this algorithm is that a low bit-depth halftone can be directly obtained from a higher bit-depth halftone for printing of progressive transmission simply by masking one or more bits off of the color halftone. The embedding can be done in any bit of the output depending on the organization of the image palettes, although the most significant or the least significant bits are most convenient. The embedding algorithm that we describe requires the use of scalar quantizers and ordered vector quantizers. We present several methods for color palette organization, or binary index assignment, to be used in conjunction with the embedded color error diffusion algorithm.

Presents an approach for performing edge detection which builds on prior work in fast facet edge detection using tree-structured vector quantization (TSVQ). The authors first extend the approach by using larger image vectors to reduce computational complexity by performing edge detection on multiple pixels at once. They then reduce the computational complexity of the edge detector without sacrificing performance by pruning the TSVQ with an edge detection-based criterion. They present results of edge detector performance on a sequence of images obtained from a mobile robot.>