Chih-Hsien Hsia

Bio: Chih-Hsien Hsia is an academic researcher from National Ilan University. The author has contributed to research in topics: Discrete wavelet transform & Computer science. The author has an hindex of 14, co-authored 94 publications receiving 909 citations. Previous affiliations of Chih-Hsien Hsia include Chinese Culture University & Tamkang University.

Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Abstract: Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.

Hierarchical Method for Foreground Detection Using Codebook Model

Abstract: This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.

Contact-free hand geometry-based identification system

Abstract: This paper presents an approach for personal identification using hand geometrical features, in which the infrared illumination device is employed to improve the usability of this hand recognition system. In the proposed system, prospective users can place their hand freely in front of the camera without any pegs or templates. Moreover, the proposed system can be widely used under dark environment and complex background scenarios. To achieve better detection accuracy, in total 13 important points are detected from a palm image, and 34 features calculated from these points are used to further recognition. Experimental results demonstrate that the averaged Correct Identification Rate (CIR) is 96.23% and averaged False Accept Rate (FAR) is 1.85%. These results prove that the proposed contact-free system can be considered as an effective identity verification system for practical applications.

Memory-Efficient Hardware Architecture of 2-D Dual-Mode Lifting-Based Discrete Wavelet Transform

Abstract: Memory requirements (for storing intermediate signals) and critical path are essential issues for 2-D (or multidimensional) transforms. This paper presents new algorithms and hardware architectures to address the above issues in 2-D dual-mode (supporting 5/3 lossless and 9/7 lossy coding) lifting-based discrete wavelet transform (LDWT). The proposed 2-D dual-mode LDWT architecture has the merits of low transpose memory (TM), low latency, and regular signal flow, making it suitable for very large-scale integration implementation. The TM requirement of the $N\times N$ 2-D 5/3 mode LDWT and 2-D 9/7 mode LDWT are $2N$ and $4N$ , respectively. Comparison results indicate that the proposed hardware architecture has a lower lifting-based low TM size requirement than the previous architectures. As a result, it can be applied to real-time visual operations such as JPEG2000, motion-JPEG2000, MPEG-4 still texture object decoding, and wavelet-based scalable video coding applications.

Improved Low-Complexity Algorithm for 2-D Integer Lifting-Based Discrete Wavelet Transform Using Symmetric Mask-Based Scheme

Abstract: Wavelet coding performs better than discrete cosine transform in visual processing. Moreover, it is scalable, which is important for modern video standards. The transpose memory requirement and operation speed are the two major concerns in 2-D lifting-based discrete wavelet transform (LDWT) implementation. This letter presents a novel algorithm, called 2-D symmetric mask-based discrete wavelet transform (SMDWT), to improve the critical issue of the 2-D LDWT, and then obtains the benefit of low-latency reduced complexity, and low transpose memory. The SMDWT also has the advantages of reduced complexity, regular signal coding, short critical path, reduced latency time, and independent subband coding processing. Furthermore, the 2-D LDWT performance can also be easily improved by exploiting an appropriate parallel method inherent to SMDWT. The proposed method has a significantly better lifting-based latency and complexity in 2-D DWT than normal 2-D 5/3 integer LDWT without degradation in image quality. The algorithm can be applied to real-time image/video applications.

The Nature and Origins of Mass Opinion.

Abstract: Originally published in Contemporary Psychology: APA Review of Books, 1994, Vol 39(2), 225. Reviews the book, The Nature and Origins of Mass Opinion by John Zaller (1992). The author's commendable effort to specify a model of mass opinion formation offers readers an introduction to the prevailing vi

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Color Appearance Models

Abstract: Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.