Acquisition of three-dimensional information of a real-world scene from two-dimensional images has been one of the most important issues in computer vision and image understanding in the last two decades. Noncontact range acquisition techniques can be essentially classified into two classes: Passive and active. This paper concentrates on passive depth extraction techniques which have the advantage that 3-D information can be obtained without affecting the scene. Passive range sensing techniques are often referred to as shape-from-x, where x is one of visual cues such as shading, texture, contour, focus, stereo, and motion. These techniques produce 2.5-D representations of visible surfaces. This survey discusses aspects of this research field and reviews some recent advances including video-rate range imaging sensors as well as emerging themes and applications.

In this paper, the authors present general views of computer vision and image processing based on optimization. Relaxation and regularization in both broad and narrow senses are used in various fields and problems of computer vision and image processing, and they are currently being combined with general-purpose optimization algorithms. The principle and case examples of relaxation and regularization are discussed; the application of optimization to shape description that is a particularly important problem in the field is described; and the use of a genetic algorithm (GA) as a method of optimization is introduced.

The reconstruction of motion and structure from multiple images is fundamental and important problem in computer vision. This paper highlights the recovery of the camera motion and the object shape under some camera projection model from feature correspondences especially the epipolar geometry and the factorization method for mainly used projection models.

This paper surveys the results of various studies on 3-D image coding. Themes are focused on efficient compression and display-independent representation of 3-D images. Most of the works on 3-D image coding have been concentrated on the compression methods tuned for each of the 3-D image formats (stereo pairs, multi-view images, volumetric images, holograms and so on). For the compression of stereo images, several techniques concerned with the concept of disparity compensation have been developed. For the compression of multi-view images, the concepts of disparity compensation and epipolar plane image (EPI) are the efficient ways of exploiting redundancies between multiple views. These techniques, however, heavily depend on the limited camera configurations. In order to consider many other multi-view configurations and other types of 3-D images comprehensively, more general platform for the 3-D image representation is introduced, aiming to outgrow the framework of 3-D "image" communication and to open up a novel field of technology, which should be called the "spatial" communication. Especially, the light ray based method has a wide range of application, including efficient transmission of the physical world, as well as integration of the virtual and physical worlds.

The goal of this paper is to present a critical survey of existing literature on an omnidirectional sensing. The area of vision application such as autonomous robot navigation, telepresence and virtual reality is expanding by use of a camera with a wide angle of view. In particular, a real-time omnidirectional camera with a single center of projection is suitable for analyzing and monitoring, because we can easily generate any desired image projected on any designated image plane, such as a pure perspective image or a panoramic image, from the omnidirectional input image. In this paper, I review designs and principles of existing omnidirectional cameras, which can acquire an omnidirectional (360 degrees) field of view, and their applications in fields of autonomous robot navigation, telepresence, remote surveillance and virtual reality.

Computational sensor (smart sensor, vision chip in other words) is a very small integrated system, in which processing and sensing are unified on a single VLSI chip. It is designed for a specific targeted application. Research activities of computational sensor are described in this paper. There have been quite a few proposals and implementations in computational sensors. Firstly, their approaches are summarized from several points of view, such as advantage vs. disadvantage, neural vs. functional, architecture, analog vs. digital, local vs. global processing, imaging vs. processing, new processing paradigms. Then, several examples are introduced which are spatial processings, temporal processings, A/D conversions, programmable computational sensors. Finally, the paper is concluded.

Human faces convey various information, including that is specific to each individual person and that is part of mutual communication among persons. Information exhibited by a "face" is what is called "non-verbal information" and usually verbal media cannot easily describe such information appropriately. Recently, detailed studies on the processing of face images by a computer have been carried out in the engineering field for applications to communication media and human computer interaction as well as automatic identification of human faces. Two main technical topics are the recognition of human faces and the synthesis of face images. The objective of the former is to enable a computer to detect and identify users and further to recognize their facial expressions, while that of the latter is to provide a natural and impressive user interface on a computer in the form of a "face. " These studies have also been found to be useful in various non-engineering fields related to a face, such as psychology, anthropology, cosmetology and dentistry. Most of the studies in these different fields have been carried out independently up to now, although all of them deal with a "face. " Now in virtue of the progress in the above engineering technologies a common study tools and databases for facial information have become available. On the basis of these backgrounds, this paper surveys recent research trends in the processing of face images by a computer and its typical applications. Firstly, the various characteristics of faces are considered. Secondly, recent research activities in the recognition and synthesis of face images are outlined. Thirdly, the applications of digital processing methods of facial information are discussed from several standpoints: intelligent image coding, media handling, human computer interaction, caricature, facial impression, psychological and medical applications. The common tools and databases used in the studies of processing of facial information and some related topics are also described.

The subject about document image understanding is to extract and classify individual data meaningfully from paper-based documents. Until today, many methods/approaches have been proposed with regard to recognition of various kinds of documents, various technical problems for extensions of OCR, and requirements for practical usages. Of course, though the technical research issues in the early stage are looked upon as complementary attacks for the traditional OCR which is dependent on character recognition techniques, the application ranges or related issues are widely investigated or should be established progressively. This paper addresses current topics about document image understanding from a technical point of view as a survey.

In this article we present a survey of medical image processing with the stress on applications of image generation and pattern recognition / understanding to computer aided diagnosis (CAD) and surgery (CAS). First, topics and fields of research in medical image processing are summarized. Second the importance of the 3D image processing and the use of virtualized human body (VHB) is pointed out. Thirdly the visualization and the observation methods of the VHB are introduced. In the forth section the virtualized endoscope system is presented from the viewpoint of the observation of the VHB with the moving viewpoints. The fifth topic is the use of VHB with deformation such as the simulation of surgical operation, intra-operative aids and image overlay. In the seventh section several topics on image processing methodologies are introduced including model generation, registration, segmentation, rendering and the use of knowledge processing.

Image processing about the vehicle is considered in this paper. When a vehicle is in a factory, image processing is applied for design and inspection, and when vehicle is on the road image processing is useful for Intelligent Transport Systems, which recently have been developed widely. There have been many researches and implementations using image sensors to get information for traffic control and vehicle control. The image seen from camera located beside or upon the road can be used for vehicle detection, velocity of car or car group measurement, parking car detection, etc. Moreover the image seen from camera located in vehicle can be used for preceding car detection, measurement of the distance to preceding car, obstacle detection, lane detection, etc. In this paper, studies about Image Processing for vehicle on the road are described.

The star graph has been known as an attractive alternative to the hypercube multiprocessor. Like the hypercube, the star graph possesses the properties of symmetry, partionability and fault tolerance, but with a smaller diameter and degree than those of the hypercube. When tasks arrive at the star graph, the tasks should be assigned appropriate free processors before execution. A new model, called Star graph (Sg)-lattice, is proposed to model the construction and free configuration of the star graph. Based on this model, the Sg-lattice scheme can fully recognize the substars. Finally, mathematical analyses and simulation results show that the Sg-lattice scheme outperforms the previous work in the storage and time complexities and the average allocation time.

This paper presents a new approach to the precise interrupt handling problem in modern processors with multiple out-of-order issues. It is difficult to implement a precise interrupt scheme in the processors because later instructions may change the process states before their preceding instructions have completed. We propose a fast precise interrupt handling scheme which can recover the precise state in one cycle if an interrupt occurs. In addition, the scheme removes all the associative searching operations which are inevitable in the previous approaches. To deal with the renaming of destination registers, we present a new bank-based register file which is indexed by bank index tables containing the bank identifiers of renamed register entries. Simulation results based on the superscalar MIPS architecture show that the register file with 3 banks is a good trade-off between high performance and low complexity.

In a mobile computing environment, in which communication channels are limited and have low-bandwidths, mobile transactions are long-lived and frequently disconnected with their wireless network in processing. Such peculiarities of mobile transactions make existing transaction scheduling schemes inadequate and raise new challenging research problems. In this paper, we propose a new scheduling scheme called OTS/MT (Optimistic Timestamp Scheme for Mobile Transactions) for mobile transaction scheduling. OTS/MT is based on an optimistic approach that is suitable for low data contention, and prevents indefinite postponement and cascading delay which are major drawbacks of the existing optimistic concurrency control scheme and the timestamp ordering scheme. In addition, the OTS/MT algorithm is inherently a deadlock-free scheduling scheme. In order to schedule mobile transactions, OTS/MT postpones the detection of conflict between mobile transactions until transaction commit time to improve the performance deterioration of TO. In this paper, we attempt to show that this application of optimism to TO is justified by way of simulation.

An extended version of weighted round robin scheduling algorithm for variable length packets is discussed. This can serve each flow fairly even if the length of packets varies. Further, an adaptive scheduling algorithm for bursty traffic is added to better treat multimedia sources. In this paper, an overview of the algorithm is described. Subsequently the throughput, latency, work complexity and elasticity for the algorithm are systematically defined and analyzed. These results show advantages of the algorithm compared with a normal or packet-based weighted round robin algorithm.

In this paper, we propose a novel fault-tolerant multicast algorithm for n-dimensional wormhole routed hypercubes. The multicast algorithm will remain functional if the number of faulty nodes in an n-dimensional hypercube is less than n. Multicast is the delivery of the same message from one source node to an arbitrary number of destination nodes. Recently, wormhole routing has become one of the most popular switching techniques in new generation multicomputers. Previous researches have focused on fault-tolerant one-to-one routing algorithms for n-dimensional meshes. However, little research has been done on fault-tolerant one-to-many (multicast) routing algorithms due to the difficulty in achieving deadlock-free routing on faulty networks. We will develop such an algorithm for faulty hypercubes. Our approach is not based on adding physical or virtual channels to the network topology. Instead, we integrate several techniques such as partitioning of nodes, partitioning of channels, node label assignments, and dual-path multicast to achieve fault tolerance. Both theoretical analysis and simulation are performed to demonstrate the effectiveness of the proposed algorithm.

In this paper, the generalized learning vector quantization (GLVQ) algorithm is applied to design a hand-written Chinese character recognition system. The system proposed herein consists of two modules, feature transformation and recognizer. The feature transformation module is designed to extract discriminative features to enhance the recognition performance. The initial feature transformation matrix is obtained by using Fisher's linear discriminant (FLD) function. A template matching with minimum distance criterion recognizer is used and each character is represented by one reference template. These reference templates and the elements of the feature transformation matrix are trained by using the generalized learning vector quantization algorithm. In the experiments, 540100 (5401 100) hand-written Chinese character samples are used to build the recognition system and the other 540100 (5401 100) samples are used to do the open test. A good performance of 92.18 % accuracy is achieved by proposed system.

A new dynamic programming (DP) based algorithm for monotonic and continuous two-dimensional warping (2DW) is presented. This algorithm searches for the optimal pixel-to-pixel mapping between a pair of images subject to monotonicity and continuity constraints with by far less time complexity than the algorithm previously reported by the authors. This complexity reduction results from a refinement of the multi-stage decision process representing the 2DW problem. As an implementation technique, a polynomial order approximation algorithm incorporated with beam search is also presented. Theoretical and experimental comparisons show that the present approximation algorithm yields better performance than the previous approximation algorithm.

The Scalable-Delay-Insensitive (SDI) model is proposed for high-performance asynchronous system design. In this paper, we focus on checking whether a circuit under SDI model satisfies some untimed properties, and formally show that checking these properties in the SDI model can be reduced to checking the same properties in the bounded delay model. This result suggests that the existing verification algorithms for the bounded delay model can be used for the verification of SDI circuits, which significantly helps the designers of SDI circuits.

In order to reduce state explosion problem, techniques such as symbolic state space traversal and partial order reduction have been proposed. Combining these two techniques, however, seems difficult, and only a few research projects related to this topic have been reported. In this paper, we propose handling single place zero reachability problem of Petri nets by using both partial order reduction and symbolic state space traversal based on ZBDDs. We also show experimental results of several examples.

Feedback of class memberships is incorporated into multimodal pattern classifiers and their unsupervised learning algorithm is presented. Classification decision at low levels is revised by the feedback information which also enables the reconstruction of patterns at low levels. The effects of the feedback are examined for the McGurk effect by using a simple model.