Feedback related to motor performance is integral to improving the control, timing and coordination of movements. However, motor learning traditionally occurs within a group setting, limiting the quality of instruction and feedback. Even during one-on-one instruction, there are impediments to feedback such as physical separation between trainer and trainee, common in many sports such as snowboarding and swimming. We propose an inexpensive solution for real-time vibrotactile positioning and speed feedback that can complement traditional motor learning, and is compatible with existing vibrotactile motor instructions. We present a psychophysical study that examined participants' initial reactions to feedback stimuli pertaining to position and speed adjustments. Results support the proposed design in terms of both usability and naturalness, and provide insight into participants' conceptualization of feedback signals and feedback for rotational movements.

In this paper, we present a novel approach for teaching motor skills through the use of vibrotactile stimulation. We propose a kinematic-vibrotactile mapping that targets fundamental movements (basic building blocks of human motion) using saltatory vibration patterns where vibrations are delivered and interpreted as movement through a conceptual mapping. Two conceptual mappings are explored: the "follow me" concept and the push/pull metaphor. A user study, approved by a local ethics committee, was conducted to explore how these conceptual mappings affect learnability, recognition accuracy, response time and naturalness. Results show the approach to work effectively with a combination of vibration patterns under each conceptual mapping providing the most useful design.

This paper presents a video indexing tool for sequences which contain moving persons, using a model-based dynamic scene analysis. The authors show how scene-specific knowledge can be used to guide the analysis process. The authors' approach permits one to avoid some problems inherent in the segmentation of primitives. A model-based post-processing step is used to correct a posteriori the shape of the extracted objects. A scenario, describing the sequence in terms of basic events is proposed. These events constitute a first level of annotation and experiments show a good robustness regarding occlusions problems.

In this paper, we propose an affine transform based vector quantization (ATVQ) technique for image coding applications. Vector quantization (VQ) is intrinsically superior to predictive coding, transform coding, and other suboptimal and ad hoc procedures. The limitation of VQ is the very large codebook that must be generated and stored. The proposed affine transform based vector quantization technique addresses this problem. The image to be coded is partitioned into disjoint square blocks. Each block is regarded as a vector and is encoded by searching through a set of affine transforms and a codebook of templates. The transform- template pair that can reconstruct an approximate input vector with minimum distortion is selected. The parameters and the index of the affine transform and the index of the template constitute the codeword of the input vector. In decoding, the image vector is reconstructed by applying the inverse of the affine transform on the template. ATVQ can reconstruct more input vectors without any distortion than conventional VQ can reconstruct, using the same codebook. Simulation results show that the technique performs well using a universal codebook. This technique is also suitable for progressive image transmission as its performance is good at very low bit rates.

In this paper, we propose an MPEG-like video coder using a 3-dimensional (3D) adaptive pyramid. In the 3D adaptive pyramid, the temporal or spatial contraction is selected using the corresponding prediction differences. A video codec is designed using the 3D adaptive pyramid and intra-frame vector quantization. Encoding errors introduced at the upper levels are fed-forward to the lower levels of the pyramid which makes lossless coding possible. The proposed pyramidal video coder has a number of similarities with the MPEG-I video compression standard. Simulation results for the CCITT standard test sequences indicate that the 3D adaptive pyramid reduces the lossless bit rate by a factor of two. Excellent subjective quality as well as objective quality (PSNR value of 36.6 db) are obtained at a bit rate less than T1 rate (i.e., 1.544 Mb/s). Furthermore, smooth transition is achieved in the case of scene changes without sacrificing picture quality.

The authors present a study whose main objective has been to explore the use of high-bandwidth multimedia communications technology for medical applications physically distributed among hospitals, that is to say an interhospital multimedia communications system. The design and development issues of a prototype are described, as well as the results of an in-field trial. From the results of this trial, a performance study has been developed for evaluating the expected communications requirements of a large-scale configuration of the system.>

The authors introduce an image coding method which unifies two image coding techniques: variable-length transform coding (VLTC) and image-adaptive vector quantization (IAVQ). In both VLTC and IAVQ, the image is first decomposed into a set of blocks. VLTC encodes each block in the transform domain very efficiently: however, it ignores the interblock correlation completely. IAVQ addresses the interblock correlation by using a codebook generated from a subset of the blocks to vector-quantize all blocks. Although the resulting codebook represents the input image better than a universal codebook generated from a large number of training images, it has to be transmitted separately as an overhead, therefore degrading the coding performance at high bit rates.>

An image adaptive vector quantization technique is presented in the transform domain, where the codebook information is interleaved with the label information. The input image is first decomposed into a set of blocks. Then, each block undergoes a two-dimensional block discrete cosine transform (DCT). The DC coefficients are linearly quantized and encoded using a fixed-length code. The remaining AC coefficients are coded using adaptive vector quantization. The AC coefficients within blocks are combined into vectors from which a codebook is generated. In quantization, for each vector, the closest codeword is determined. If it is the first time the codeword is selected, the closest codeword is transmitted. Otherwise, the corresponding label is transmitted. To gain efficiency in transmitting the codewords, variable length coding (VLC) is used in encoding the codewords in the DCT domain. A reduction in bit rate required for transmitting the labels is accomplished by using a technique called codebook self-organization and by exploiting the correlation between the neighboring vectors.>

The authors propose a combined source-channel coding scheme which provides error protection in order to improve the coding performance over noisy channels for three frame replenishment coding techniques. The scheme provides a trade-off between the reconstruction accuracy associated with the source coder and the degree of error protection. The simulation results demonstrate that a small amount or error protection provides a significant improvement in performance without sacrificing transmission bandwidth.>

A review of various pyramid data structures for progressive image transmission, focusing on the steps involved in forming the pyramid and the performance comparisons, is presented. The simple mean pyramid, its variants including the truncated mean, reduced-sum, difference, reduced-difference and S-transform pyramids, and the more general Laplacian pyramid are discussed. It is also shown that the use of an interpolation function improves the quality of the full-resolution approximations both objectively and subjectively.>

A multistage vector quantization with optimal bit allocation (MVQ-OBA) in the transform domain is presented. A set of bit allocation planes is first obtained by slicing a (scalar) optimal bit allocation map where the number of bits assigned to each coefficient is proportional to the coefficient variance. The set of bit allocation planes determines the coefficients to be used and the codebook size at each stage. The vector dimensionalities are restricted to small values and relatively small codebooks are used, thus reducing both the overhead required for transmitting the codebooks and the complexity in codebook design. The computer simulation results demonstrate that MVQ-OBA is competitive with many other transform coding techniques including variable length transform coding. MVQ-OBA is well suited for progressive transmission.>

A systolic array architecture for image coding using adaptive vector quantization is presented. A basic systolic cell was designed with two modes of operation. In the forward mode, the cell executes the basic distortion operation, while in the reverse mode, the cell executes the centroid computation operation. Both modes co-exist in perfect synchronism. The systolic array module essentially consists of an array of L*N basic systolic cells connected in parallel and pipeline in the direction of the vector dimension, L, and codeword dimension, N, respectively. This architecture results in a speedup proportional to NL, and has the following advantages: there is no need for separate hardware to compute the new centroids; there is no need for a high speed interface to transfer the new centroids into the systolic array; and there are no delays involved in the computation and transfer of new centroids.>

The problem of communicating the radiologist's findings to the attending physician is introduced, and it is shown why it is essentially a multimedia communication problem. A multimedia communications system that was designed with a focus on database and medical reports organization and architecture is described. A report is presented of some of the key results of an in-house trial linking the Department of Radiological Sciences and the Emergency Department at the Ottawa Civic Hospital, Canada, a 950-bed tertiary-care teaching hospital.>

A segmentation algorithm based on sequential optimization which produces a hierarchical decomposition of the picture is presented. The decomposition is data driven with no restriction on segment shapes. It can be viewed as a tree, where the nodes correspond to picture segments and where links between nodes indicate set inclusions. Picture segmentation is first regarded as a problem of piecewise picture approximation, which consists of finding the partition with the minimum approximation error. Then, picture segmentation is presented as an hypothesis-testing process which merges only segments that belong to the same region. A hierarchical decomposition constraint is used in both cases, which results in the same stepwise optimization algorithm. At each iteration, the two most similar segments are merged by optimizing a stepwise criterion. The algorithm is used to segment a remote-sensing picture, and illustrate the hierarchical structure of the picture.>

A progressive image transmission scheme in which vector quantization is applied to images represented by pyramids is proposed. A mean pyramid representation of an image is first built up by forming a sequence of reduced-size images by averaging over blocks of 2*2 pixels. A difference pyramid is then built up by taking the differences between successive levels in the mean pyramid. Progressive transmission is achieved by sending all the nodes in the difference pyramid starting from the top level and ending at the bottom level. The kth approximate image can be formed by adding the information of level k to the previously reproduced (k-1)st approximation. To gain efficiency, vector quantization is applied to the difference pyramid of the image on a level-by-level basis. If the errors due to quantization at level k are properly delivered and included in the next level, k+1, then it is demonstrated that the original image can be reconstructed. An entropy coder is used to encode the final residual error image losslessly, thus ensuring perfect reproduction of the original image. The experiments demonstrate that it is possible to achieve simultaneously lossless and progressive transmission with compression. At the intermediate level, the use of vector quantization results in a coding gain over that obtained using only a Huffman coder. Excellent reproduction is achieved at a bit rate of only 0.06 bits/pixel.>

A progressive image transmission scheme is presented which combines block transforms, a quadtree data structure and vector quantization. The experimental results demonstrate that the scheme achieves lossless progressive transmission with compression. The quadtree data structure makes it possible to transmit the images progressively. Compression is obtained by using vector quantization on each level. Lossless reproduction is guaranteed by delivering the residual errors due to quantization from high level to low level and using an entropy coder on the final residual error image.>

An adaptive technique for image sequence coding that is based on vector quantization is described. Each frame in the sequence is first decomposed into a set of vectors. A codebook is generated using the vectors of the first frame as the training sequence, and a label map is created by quantizing the vectors. The vectors of the second frame are then used to generate a new codebook, starting with the first codebook as seeds. The updated codebook is then transmitted. At the same time, the label map is replenished by coding the position and the new values of the labels that have changed from one frame to the other. The process is repeated for subsequent frames. Experimental results for a test sequences demonstrate that the technique can track the changes and maintain a nearly constant distortion over the entire sequence.>

A progressive image transmission scheme based on iterative transform coding structure is proposed for application in interactive image communication over low-bandwidth channels. The scheme not only provides progressive transmission, but also guarantees lossless reproduction combined with a degree of compression. The image to be transmitted undergoes an orthogonal transform, and the transform coefficients are quantized (scalar or vector) before transmission. The novelty is that the residual error array due to quantization is iteratively fedback and requantized (scalar or vector); the coded residual error information is progressively transmitted and utilized in reconstructing the successive approximations. It is shown that the average reconstruction error variance converges to zero as the number of iterative stages approaches infinity. In practice, lossless reproduction can be achieved with a small number of iterations by using an entropy coder on the final residual-error image. Computer simulation results demonstrate the effectiveness of the technique.>

The paper demonstrates the application of adaptive vector quantization techniques to the coding of monochromatic and color pictures. Codebooks of representative vectors are generated for different portions of the image. By means of extensive simulations, the performance of the coder for monochrome images is estimated as a function of the various coding parameters-vector dimensionality, number of representative vectors, and degree of adaptivity. It is shown that for a vector size of dimension 4, there is a zone of operation, ranging from 1.0 to 1.5 bits/pixel, where adaptive vector quantization is advantageous. Despite the simplicity of the decoder, the performance of the adaptive vector quantizer is found to be comparable with block transform coding schemes for both monochrome and color pictures.

In this paper, a new interframe coding technique based upon vector quantization is presented. This algorithm has as its basis twodimensional block vector quantization, at the frame level, onto which is grafted the concept of adaptive codebook replenishment and frame replenishment. This algorithm includes three strategies: label replenishment, label replenishment with mean shift, and label replenishment with mean shift and selective codeword replacement. The last two strategies efficiently update the codebook to track the changes in local statistics on a frame basis. Compared with three-dimensional block vector quantization [16], for bit rates between 0.5 and 0.65 the normalized mean Squared error (NMSE) is reduced by a factor ranging between 2 and 2.5 by the use of replenishment.

This paper reports upon the design of an expert system at the Canada Centre for Remote Sensing for updating maps of forested areas using Landsat imagery. The system is decomposed into a number of specialist experts organized in a hierarchical fashion around a series of blackboards which are used for communications between the different levels. At the lowest level in the hierarchy, is an expert which acts as the interface to the low-level, procedural-based, image processing algorithms. A short review of expert systems, with special emphasis on the analysis of remotely sensed imagery, is first given. Various approaches to treating uncertainty in expert systems are then discussed, in particular, the approach based upon the theory of evidence. The forestry map updating problem is described and the classical solution presented. Our hierarchically organized expert system is then presented in the context of this problem. Finally, an expert interface to the image processing algorithms is described.

Vector quantization techniques are currently favored in speech compression. Recently a nonadaptive form of vector quantization was proposed for monochrome image compression. In this paper, color picture compression by adaptive vector quantization is presented. Both spatial and spectral redundancy is exploited. Adaptive vector quantization of color pictures is shown to produce better mean-square-error result than block cosine transform coding, for the same data rate. No statistical image model is assumed. Decoding is a simple process of table look-up, permitting video-rate decoding, thus only a small refresh memory containing the codewords is necessary.

This correspondence presents a simple algorithm to detect and label homogeneous areas in an image, using directed trees for region labeling. The scheme constructs directed trees with the image points as nodes, guided by an edge value computed at every point. These directed trees segment the image into disjoint regions. Because of a valley seeldng property of the tree construction procedure, the boundaries separating the resultant segments pass through the center of the edges. The algorithm thus performs wel1 with thick and wide edges of varying height, because no thresholding of the edge image is involved. The properties of the resultant segments are stated in terms of the edge image. The algorithm is shown to be simple, efficient, and effective for detecting homogeneous segments in the presence of noise. Results of application in the algorithm to segment a LANDSAT multispectral scene of an agricultural area are included.

A clustering scheme using a multidimensional histogram stored in a table is described and tested on four-dimensional data derived from LANDSAT imagery. By doing all clustering operations on the histogram rather than on the original measurement vectors, it is possible to reduce the computations by a large factor and handle the large sample sizes that are typically encountered in image processing. The clustering algorithm first isolates and delineates the peaks in the multidimensional histogram. These peaks are then used as cluster centers, and all the other measurement vectors in the histogram are assigned to the nearest center. The scheme initially identifies the most separable clusters in the data. It then runs on an interactive basis allowing the user to split specific clusters into subclusters at the expense of less separability. The histogram approach lends itself to statistical analysis using parametric models and the likelihood ratio test. As a starting point, it is assumed that the observed distribution is a mixture of several multivariate Gaussian distributions with unknown mean vectors, covariance matrices, and a priori probabilities. Estimates of the Gaussian parameters are determined ignoring the overlap of the neighboring distributions. The theoretical histogram is then calculated by integrating numerically the probability density function in each of the cells of the histogram, and the likelihood ratio test is applied to measure the departure of the model from the observed data. A statistical measure taking into account the number of degrees of freedom is defined and used to choose between alternative models.

A class of product codes for which an augmented syndrome can be defined is studied. By using this augmented syndrome, we are able to add new codewords to a product code while keeping the minimum distance fixed. In many cases the resulting code is as good as any known code. Decoders that may be simpler to implement than existing decoders are described for these augmented codes. It is also seen that in certain cases, by choosing not to correct a small number of correctable errors, significant simplifications in the decoder can be achieved. Finally, this augmentation technique is used in an example to generate a code of length 32 and minimum distance 8 with more codewords than any code previously known.