One of the critical resources of the multi-channel echo canceller is the memory that stores both tapped delay lines of voice data and filter tap coefficients. A simple variable-length/run-length coding scheme for reducing coefficient memory for multi-channel echo cancellers is proposed. We also describe the corresponding memory system architecture. Simulations based on a bit-accurate Matlab model show that the proposed scheme is effective.

One of the problems in vector quantization (VQ) is its relatively long encoding time especially when an exhaustive search is made for the codevector. This paper presents a hashing-based technique to organize the codebook so that the search time can be significantly reduced. Hashing gives the speed advantages of a direct search, while maintaining a codebook of reasonable size. Experiments show that hashing-based VQ sustained image quality as the encoding time was reduced, while full search VQ suffered greatly. For example, for 2/spl times/2 vectors and with 1024 codebook entries, encoding time was reduced by a factor of 10 without significant loss of image quality.

Observation of the usage of codevectors in vector quantization (VQ) reveals the locality of the reference, both temporally and spatially. Cache VQ takes advantage of such property by providing a large main codebook for quality concerns and a much smaller cache codebook for compression and computational concerns. We evaluate some design parameters for the cache VQ system. Our experimental results show that for various combinations of cache sizes, distortion thresholds, and line sizes, bit ratios ranging from 60% to 97% can be achieved by the cache VQ, with PSNRs between 33 dB and 38 dB and with codevector dimensions of 4 and 16. The corresponding compression ratio without entropy coding ranges from 6:1 to 24:1. These results clearly indicate that a very good performance in terms of time as well as fidelity and bit rate can be realized with the cache VQ.>

A vector quantization (VQ) scheme with finite memory called dynamic finite-state vector quantization (DFSVQ) is presented. The encoder consists of a large codebook, so called super-codebook, where for each input vector a fixed number of its codevectors are chosen to generate a much smaller codebook (sub-codebook). This sub-codebook represents the best matching codevectors that could be found in the super-codebook for encoding the current input vector. The choice for the codevectors in the sub-codebook is based on the information obtained from the previously encoded blocks where directional conditional block probability (histogram) matrices are used in the selection of the codevectors. The index of the best matching codevector in the sub-codebook is transmitted to the receiver. An adaptive DFSVQ scheme is also proposed in which, when encoding an input vector, first the sub-codebook is searched for a matching codevector to satisfy a pre-specified waveform distortion. If such a codevector is not found in tile current sub-codebook then the whole super-codebook is checked for a better match. If a better match is found then a signaling flag along with the corresponding index of the codevector is transmitted to the receiver. Both the DFSVQ encoder and its adaptive version are implemented. Experimental results for several monochrome images with a super-codebook size of 256 or 512 and different sub-codebook sizes are presented.>

A new interframe coding technique called interframe hierarchical address-vector quantization (IHA-VQ) is presented. It exploits the local characteristics of a moving image's motion-compensated (via block matching) difference signals by using quadtree segmentation to divide each image into large, uniform regions and smaller, highly detailed regions. The detailed regions are encoded by vector quantization (VQ), and the larger, low-detail regions are replenished from the previous frame, IHA-VQ also exploits the correlation between the small blocks by encoding the addresses of neighboring vectors by using several codebooks of address code-vectors.>

An optimization approach is used to solve the correspondence problem for a set of features extracted from a pair of stereo images. A cost function is defined to represent the constraints on the solution, which is then mapped onto a two-dimensional Hopfield neural network for minimization. Each neuron in the network represents a possible match between a feature in the left image and one in the right image. Correspondence is achieved by initializing (exciting) each neuron that represents a possible match and then allowing the network to settle down into a stable state. The network uses the initial inputs and the compatibility measures between the matched points to find a stable state.

A terrain acquisition algorithm for an autonomous mobile robot to learn and model its surrounding terrain efficiently with respect to travel distance, and sensing operations is presented. It is shown that this algorithm enables a mobile robot to visit relatively few obstacle vertices to construct a map of a planar terrain occupied by polygonal obstacles. According to this algorithm, a mobile robot travels to a vertex of the obstacles only when additional terrain information can be obtained from there. The decision to move to a vertex is thus based on whether any incident edge of a vertex is missing or occluded.>

A model-based recognition method is introduced which is formulated as an optimization problem. An energy function is derived which represents the constraints on the best solution in order to find the best match. A two-dimensional binary Hopfield neural network is implemented to minimize the energy function. The state of each neuron in the Hopfield network represents the possibility of a match between a node in the model graph and a node in the scene graph. >

Polycurve codes is the name of a line-drawing model for describing object boundaries and contour lines. It belongs to chain-coding families such as chain codes and generalized chain codes. Polycurve codes make possible the direct extraction and labeling of predefined high-level line and arc segments from irregular object boundaries. Several algorithms for computing features of the objects from polycurve-encoded boundaries are presented. Some geometric calculation such as inverse and intersection points of two polycurves are also considered. The algorithms use look-up tables indexed by segment labels so that only elementary operations such as addition and subtraction may be required.>