Jenq-Neng Hwang

Fast and automatic video object segmentation and tracking for content-based applications

The new video-coding standard MPEG-4 enables content-based functionality, as well as high coding efficiency, by taking into account shape information of moving objects. A novel algorithm for segmentation of moving objects in video sequences and extraction of video object planes (VOPs) is proposed . For the case of multiple video objects in a scene, the extraction of a specific single video object (VO) based on connected components analysis and smoothness of VO displacement in successive frames is also discussed. Our algorithm begins with a robust double-edge map derived from the difference between two successive frames. After removing edge points which belong to the previous frame, the remaining edge map, moving edge (ME), is used to extract the VOP. The proposed algorithm is evaluated on an indoor sequence captured by a low-end camera as well as MPEG-4 test sequences and produces promising results.

Handbook of Neural Network Signal Processing

From the Publisher: The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

Query-based learning applied to partially trained multilayer perceptrons

An approach is presented for query-based neural network learning. A layered perceptron partially trained for binary classification is considered. The single-output neuron is trained to be either a zero or a one. A test decision is made by thresholding the output at, for example, one-half. The set of inputs that produce an output of one-half forms the classification boundary. The authors adopted an inversion algorithm for the neural network that allows generation of this boundary. For each boundary point, the classification gradient can be generated. The gradient provides a useful measure of the steepness of the multidimensional decision surfaces. Conjugate input pairs are generated using the boundary point and gradient information and presented to an oracle for proper classification. These data are used to refine further the classification boundary, thereby increasing the classification accuracy. The result can be a significant reduction in the training set cardinality in comparison with, for example, randomly generated data points. An application example to power system security assessment is given.

A Review on Video-Based Human Activity Recognition

This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.

Neural network architectures for robotic applications

The authors propose a ring VLSI systolic architecture for implementing artificial neural networks (ANNs) with applications to robotic processing. Key design issues concerning algorithms, applications, and architectures are examined. A variety of neural networks is considered, including single-layer feedback neural networks, competitive learning networks, and multilayer feed-forward networks. It is demonstrated that the ANNs are suitable to all three levels of robotic processing applications including task planning, path planning, and path control levels. For these applications, a programmable systolic array is developed than can exploit the strength of VLSI to provide intensive and pipelined computing. Both the retrieving and learning phases are integrated in the design. The proposed architecture, which is more versatile than other existing ANNs, can accommodate all the useful neural networks for robotic processing. >

A unified systolic architecture for artificial neural networks

Abstract This paper proposes a generic iterative model for a wide variety of artificial neural networks (ANNs), single-layer feedback networks, multilayer feed-forward networks, and hierarchical competitive networks, as well as some probabilistic models. A unified formulation is provided for both the retrieving and the learning phases of most ANNs. On the basis of the formulation, a programmable ring systolic array is developed. The architecture maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. It may be adopted as a basic structure for a universal neurocomputer architecture.

Fractal analysis of region-based vascular change in the normal and non-proliferative diabetic retina

Purpose. Evaluation of normal and abnormal vascular pattern in the human retina using a novel method: quantitative region-based fractal analysis. Methods. Binary (black/white) vascular patterns of the human retina originating at the optic disc were obtained by semi-automatic computer processing of digital images from 60-degree fundus fluorescein angiography of 5 normal eyes and 5 eyes with non-proliferative diabetic retinopathy (NPDR). As determined by image resolution, vascular patterns included vessels with diameters =50 µm and excluded small vessels and capillaries. The density of linearized (i.e., skeletonized) vascular patterns in the macular region versus paramacular region (termed “region-based” linearized vascular pattern) was quantified with the fractal dimension (D f) and confirmed by grid intersection (? v) . Results. By region-based quantification, D f and ? v were significantly higher in the normal macular region than in the NPDR macular region (p = 0.008 and p = 0.019, respectively). However, differences in D f and ? v between the normal and NPDR paramacular regions were not strongly signficant (p = 0.168 and p = 0.337, respectively). Conclusions. Results from the retrospective analytical study demonstrate the feasibility of using quantitative region-based fractal analysis of early-stage vascular disease in the human retina. The results are encouraging for a broader study of diverse patient populations.

A real-time interactive virtual classroom multimedia distance learning system

In this paper, we present the design and development of a real-time interactive virtual classroom multimedia distance learning system at the University of Washington. There has been rapid progress in digital media compression research, and the delivery of media data on the public Internet is becoming widespread. A real-time interactive virtual classroom allows a remote participant to not only receive a live class feed, but also to interact in a live class by asking questions with audio, video in real-time using an Internet connection. Many instructors use electronically prepared slides during their class. The traditional video coding algorithms are not able to compress this slide data very well at low bit-rates. We propose a Web-based real-time presentation system for the electronic slides. Instructors also write text on a white board or a piece of paper during the class. At low bit-rates, conventional video encoding algorithms cannot encode this handwritten text video with enough fidelity, resulting in an illegible decoded video. We propose an extension of the well known bilevel image encoding algorithms to handle the handwritten text video. Our method results in decoded video frames which can be read very clearly when encoded at low bit-rates. We have developed a set of tools which allows recording the live classroom session and automatic creation of a synchronized multimedia integration language (SMIL) presentation, which can be used for a later viewing.

Closed contour edge detection of blood vessel lumen and outer wall boundaries in black-blood MR images

Quantitative measurements of the blood vessel wall area may provide useful information of atherosclerotic plaque burden, progression and/or regression. Magnetic resonance imaging is a promising technique for identifying both luminal and outer wall boundaries of the human blood vessels. Currently these boundaries are primarily defined manually, a process viewed as labor intensive and subject to significant operator bias. Fully automated post-processing techniques used for identifying the lumen and wall boundaries, on the other hand, are also problematic due to the complexity of signal features in the vicinity of the blood vessels. The goals of this study were to develop a robust, automated closed contour edge detection algorithm, apply this algorithm to high resolution human carotid artery images, and assess its accuracy, and reproducibility. Our algorithm has proven to be sensitive to various contrast situations and is reasonably accurate and highly reproducible.

An integrated scheme for object-based video abstraction

In this paper, we present a novel scheme for object-based key-frame extraction facilitated by an efficient video object segmentation system. Key-frames are the subset of still images which best represent the content of a video sequence in an abstracted manner. Thus, key-frame based video abstraction transforms an entire video clip to a small number of representative images. The challenge is that the extraction of key-frames needs to be automated and context dependent so that they maintain the important contents of the video while remove all redundancy. Among various semantic primitives of video, objects of interest along with their actions and generated events can play an important role in some applications such as object-based video surveillance system. Furthermore, on-line processing combined with fast and robust video object segmentation is crucial for real-time applications to report unwanted action or event as soon as it happens. Experimental results on the proposed scheme for object-based video abstraction are presented.