Over the past 20 years, the world has seen significant advances in and adoption of new technology, political events that have led to the opening of formerly closed societies, economic policies that have encouraged free trade, and the growth in multinational companies. These changes have resulted in a world with increased connectedness and interdependencies and a globalization of the workforce. This paper discusses the impact globalization has had on engineering and engineering education.

The computerized process of locating human facial features such as the eyes, nose and mouth in a head and shoulders image is crucial to such applications as automatic face identification and model-based video coding. A new model-based algorithm for locating these major features is developed. The algorithm estimates the parameters of the ellipse which best fits the head view in the image and uses these parameters to calculate the estimated locations of the facial features. It then refines the estimated coordinates of the eyes, mouth, and nose by exploiting the vertical and horizontal projections of the pixels in windows around the estimated locations of the features. The algorithm has been implemented and tested with over twelve hundred images, and simulation results indicate that the algorithm is robust to variations in subject head shape, eye shape, age, and motion such as tilting and nodding of the head.

The senior level digital signal processing (DSP) course at North Carolina State University is designed to teach basic methods of describing and analyzing digital signals and systems. In order to reinforce this approach, the laboratory is designed to demonstrate DSP concepts with actual signals and systems. The lab requires no programming but gives the student the opportunity to see the application of theory by manipulating and analyzing real signals. The basis for the lab is a dual channel, multiple scope software package that allows the student simultaneous viewing of time domain and frequency domain signals.

New properties of absolute moment block truncation coding (AMBTC) are presented with proof. The main purposes of this work are (1) to provide some fundamental insights into the AMBTC algorithm and (2) to show that AMBTC is a robust choice among the 1-bit quantizers considering both computational complexity and quantization error.>

Two-layer encoding of packet video may be a useful way to minimize network resource usage while coping with potential packet loss. A two-layer codec architecture modeled after the H.261 RM8 algorithm is proposed, and is shown to be a generalization of the requantization and spectral separation encoding techniques. Experiments were performed using two ten-second video sequences to examine the efficiency of two-layer encoding algorithms. Three techniques: periodic group of blocks (GOB) replenishment, conditional macroblock replenishment, and block error thresholding, are shown to improve the efficiency of two-layer encoding. A hybrid encoding method utilizing both spectral separation and requantization is shown to provide the best results.>

Dynamic bit allocation directly impacts the overall coding performance in block transform (e.g., DCT, Hadamard, etc.) coding, subband coding, and wavelet coding. In very low bit rate coding (such as the evolving MPEG-4 standard), additional factors impacting the bit allocation process need to be identified and properly exploited to achieve optimal coding performance. In the paper, a generalized optimum dynamic bit allocation algorithm is presented. The algorithm is based on the Shannon rate-distortion bound and a weighted distortion criterion using a Lagrange multiplier optimization technique. The results look promising for applications in real-time multimedia communications.>

Subband coding and vector quantization combined can provide a powerful method for compressing color images. The use of properties of the human visual system can increase the performance of such a system and allow one to achieve very high quality reconstructed images at compression ratios exceeding 10:1. The authors design a color subband-vector quantization system, and formulate the bit allocation problem as an optimization problem where the objective function depends on the distortion-rate curves of the quantizers and on a set of perceptual weights. These weights are derived from data provided by measurements of the mean detection threshold of the human visual system for color transitions along the luminance, red-green, and blue-yellow directions. Minimization of the objective function constrained by the desired bit rate gives a perceptually optimal bit allocation. Three subband/VQ cases are examined. In the first two cases (case 1 and case 2), the color components of the lowest frequency subband are scalar quantized. Case 1 combines the three color components of each pixel of the higher frequency subbands into a three-dimensional vector, while case 2 creates four-dimensional vectors from 2/spl times/2 blocks in each subband color component. The third case (case 3) is the same as case 2, except that the chrominance components of the lowest frequency subband are also vector quantized with 2/spl times/2 blocks in each component. To obtain the required compression ratio and the high color fidelity required for HDTV applications, the vector quantization is done in C.I.E. L*a*b* space and AC/sub 1/C/sub 2/ space.>

Neural network architectures have been proposed as new computer architectures and a Hopfield neural network has been shown to find good solutions very fast in solving complex optimization problems. It should be noted, however, that a Hopfield neural network with fixed neural gains only guarantees to find local optimum solutions, not the global optimum solution. Image segmentation, like other engineering problems, can be formalized as an optimization problem and implemented using neural network architectures if an appropriate optimization function is defined. To achieve a good image segmentation, the global or the nearly global optimum solutions of the appropriate optimization function need to be found. In this paper, we propose a new neural network architecture for image segmentation, `an annealed Hopfield neural network,' which incorporates an annealing schedule for the neural gains. We implemented image segmentation using this annealed Hopfield neural network with an optimization function proposed by Blake and Zisserman and achieved good image segmentation in detecting horizontal and vertical boundaries. Later, we proposed an extended optimization function to achieve better performance on detecting sharp corners and diagonally oriented boundaries. Finally, simulation results on synthetic and real images are shown and compared with general-purpose mean field annealing technique.

A novel method for obtaining dense displacement fields from a sequence of frames is presented. The algorithm is based on modeling the displacement field as a Markov random field, hence making it possible to use the equivalence of a Markov random field and a Gibbs distribution. The Gibbs distribution is used to define a mean field probability density function, and the displacement field is found using a mean field annealing algorithm. Mean field annealing is a technique which finds the global or near global minima in nonconvex optimization problems. Analysis of the algorithm and experimental results are presented.>

A subband/VQ coder that combines the three color components of each subband pixel into a length 3 vector and then vector quantizes each subband is presented. Perceptual weights derived from subjective visual experiments are used along with the rate-distortion curves to determine a perceptually optimal bit allocation. Advantages of this method are that fewer codebooks need to be designed and the resulting bit allocation problem is simplified. Preliminary results show that this method achieves results comparable to the more standard subband/VQ method where the color components of each subband are vector quantized separately using the perceptually optimal bit allocation algorithm. To obtain the required compression ratio and the high color fidelity required for HDTV applications, the vector quantization is done in a perceptually uniform color space.>

Motion estimation is cast as a problem in energy minimization. This is achieved by modeling the displacement field as a Markov random field. The equivalence of a Markov random field and a Gibbs distribution is then used to convert the problem into one of defining an appropriate energy function that describes the motion and any constraints imposed on it. The energy function is then minimized using the mean field annealing algorithm, a technique which finds the global or near-global minima in nonconvex optimization problems. Analysis of the algorithm and experimental results are presented.>

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image into texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes: perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. Good quality reconstructed images are obtained with 0.08 to 0.3 b/p for three different types of imagery.>

A combination of subband coding and absolute moment block truncation coding (AMBTC) is presented to effectively eliminate the blocking effect (or grid noise) that the AMBTC severely suffers. To dynamically allocate bits for each subband and efficiently achieve quality images for a given compression ratio, there are two fundamental questions; (1) how to choose subbands for applying AMBTC, and (2) how to choose the appropriate window sizes for these subbands. The concept of intra-subband and inter-subband bit allocation has been proposed to address the former issue, and a variable window size approach is suggested for the latter one. Each solution shows promising results.>

Motion artifacts due to heart motion and blood flow within the heart chambers are a significant barrier to accurate interpretation of cardiac magnetic resonance images (MRI). The post-processing techniques of Wiener filtering, alternating projections onto convex sets (POCS), and mean field annealing (MFA) to remove these artifacts are studied. Removal of noise from the images is accomplished with MFA by setting up a restoration problem whose objective function encapsulates both data-dependent and a priori knowledge about the image. Two important aspects of MFA are the use of a penalty rather than a constraint, and the fact that it converges twenty times as rapidly as normal simulated annealing. Images of the results of Wiener filtering and MFA are shown, and the methods of POCS and MFA are briefly explained.>

A new segmentation-based image coding technique which performs segmentation based on roughness of textural regions and on properties of the human visual system (HVS) is presented. The image is segmented into texturally homogeneous region with respect to the degree of roughness as perceived by the HVS. The fractal dimension is used to measure the roughness of the textural regions. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into several classes. Three texture classes are chosen: perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each texture class.>

An image coding technique with the capability of producing good image quality at a compression ratio over 1000:1 is described. The main idea is to break the image into complicated primitives using a priori knowledge. Usually these primitives are not coded and transmitted, but are matched to a previously created database, and the necessary information about the best match is coded and transmitted. This information is used at the receiver to retrieve a replica of the original primitive from a duplicate database. The primitives are assembled as in computer animation to produce a faithful reconstruction of the original image. The proposed technique allow real-time transmission of video signals over low-bit-rate channels (less than 64 kb/s). The application of this technique to teleconference and picture phone images is discussed.>

The concept of higher-order fractal dimensions is introduced in the field of computer vision and image processing. An easily computable technique based on higher-order fractal dimensions for texture classification is presented. It is angularly independent and invariant under linear gray-level transformations. This new approach is used to classify some sets of textures. The initial experimental results show that a perfect classification is achieved.>

With the availability of a 144 Kbits/sec user's digital access to the integrated service digital network (ISDN), transmission of video data over the telephone network will become a reality. However, the required compression is extremely high and improving the visual quality of the decoded image data continues to be a challenge. The authors present a new image coding algorithm which includes a technique for segmenting image sequences and a modified adaptive 3-D arithmetic coder (MAAC). With these enhancements, it will be possible to transmit an image sequence at 30 frames per second over a digital telephone line.>

Recently, ways to obtain a new generation of image-coding techniques have been proposed. The incorporation of the human visual system (HVS) models and tools of the image analysis, such as segmentation, are two defining features of these techniques. In this paper, a new image coding technique is presented which utilizes a human visual system model and a image analysis criteria together with the technique of projection onto convex sets (POCS). The human visual system model is used to determine the most probable perceptual information of an image and the removal of irrelevant information. The projection onto convex sets operation is used as a restoration technique aimed at correcting the degradations caused by the conventional coders in the transmitter part of the channel. And finally, the image analysis based technique is used as an interpolator in order to predict the unknown regions of the received image. This new technique can be used in single frame image coding applications to obtain acceptable images at extremely high compression rates.

A new segmentation method based on the properties of the human visual system is described in this paper. The segmentation method is part of a new second generation image coder. In addition, one of the properties is that it is not necessary to transmit (or store) the visual residual for use in reconstructing the received signal. It is assumed that the characteristics of this visual residual are known at the receiver and can be used in the reconstruction process.

This paper presents and analyzes a pel-recursive, motioncompensated, image sequence compression algorithm [1]. The analysis retains all the terms of the Taylor's series expansion and yields a set of equations for which the convergence criteria and the convergence rate of the motion estimate are more easily seen. The existing motion prediction schemes are also reviewed and a new motion prediction scheme is presented which is shown to be superior to the existing schemes. Simulations run on actual image sequences to verify the analytical results indicate that implementing the analytical model as opposed to the generally used heuristic technique does yield a decrease in the information rate and the computational requirements. Simulation results also are included which use the "projection-along-the-motiontrajectory" or PAMT prediction scheme. Third, zeroth-order entropy encoding is shown to reduce the bit rate on the order of 12 percent, and to reduce the mean square error in the reconstructed images on the order of 60 percent when compared to first-order entropy encoding. Fourth, field-to-field motion prediction is compared to frame-to-frame motion prediction.

Projection onto convex sets has recently been suggested as an approach to time-varying image compression. In this paper, an image coding technique based on such an approach is developed. In particular, pixel and frequency domain information is combined in order to improve image quality over standard transform coding at given bit rates. The use of nonintersecting convex sets is shown to be optimum for two sets and a given error measure. Convex sets and projections are discussed in general and then in the context of image compression. Simulation results are presented for single frame images as well as for image sequences.

In image coding, image information can often be transmitted with relatively little bandwidth requirement. This is the case in the pixel domain if there are large blocks of homogeneous intensity within a single frame, or when comparing adjacent frames there are large blocks of unchanged pixels. In the frequency domain, this is the case when only a few of the frequency coefficients are deemed significant. This paper presents an algorithm for improving the bandwidth compression of the error pattern generated by typical motion-compensated coding techniques. The algorithm involves two well established techniques; conventional transform coding and the method of successive Projection onto Convex Sets.

This paper addresses various aspects of motion-compensated interframe coding. The image sequence data is investigated in an attempt to develop a more thorough analysis of the convergence requirements and the convergence rate. A new motion prediction technique is used which increases the validity of the assumptions made in proving convergence and which decreases the total prediction error. Simulation results are presented to indicate the improvement of the proposed motion prediction scheme and to indicate the results of the convergence analysis.

The Least Mean Squares (LMS) Algorithm may be used to adapt the coefficients of an adaptive prediction filter for image source encoding. It is shown that if one allows a small amount of distortion in the received image, then the LMS algorithm allows a dramatic reduction in the required channel transmission bandwidth. Results are presented which show LMS provides up to a 1-2 bits per symbol reduction in transmitted bit rate (compared to DPCM) when distortion levels are approximately the same in both cases. Further, LMS can equivalently be used in fixed channel bandwidth environments to decrease the image distortion. When compared to results for fixed coefficient DPCM, one is able to reduce reconstructed image distortion by as much as 7-10 dB using LMS.

This paper describes a new technique for calculating the displacement field in a motion sequence of two-dimensional imagery generated by a moving sensor. It proposes a scheme wherein a simple algorithm for calculating single displacement vectors is applied over numerous subimages to create a displacement field. This algorithm is an application of the MDT (motion detection transform) [15,16], thus is based on the 1D (one-dimensional) Fourier transform.

The Least Mean Squares (LMS) adaptive algorithm is applied to the problem of reducing the mean square power of the information sequence required for transmission of video or teleconferencing image sequences. The results of processing actual teleconferencing sequences are presented and compared with the results of using a fixed coefficient predictive algorithm. Operating on actual teleconferencing sequences averaging 2% - 12% moving area, the LMS adaptive filter is shown to provide approximately 1-4 dB reduction in transmitted sequence power when compared to a fixed coefficient filter. Further, the performance of the LMS adaptive system is shown to be quite insensitive to the value of the feedback gain parameter, α, for a wide range of α. This allows the user to achieve a substantial performance gain (compared to the fixed coefficient system) without encountering problems of adaptive filter stability.

An adaptive nonlinear Kalman-type filter is presented for the restoration of two-dimensional images degraded by general image formation system degradations and additive white noise. A vector difference equation model is used to represent the degradation process. Due to the nonstationarity of an image the object plane distribution function, i.e. the original image, is partitioned into disjoint regions based on the amount of spatial activity in the image. Difference equation models are used to characterize each of the regions of this nonstationary object plane distribution function. Features of the restoration filter include the ability to account for the response of the human visual system to additive noise in the image; a two-dimensional interpolation scheme to improve the estimates of the initial states in each region; and a nearest neighbor algorithm to choose the previous state vector for the state of pixel (i,j).