Detection of Unnatural Movement Using Epitomic Analysis

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

As modern image and video processing applications handle increasingly higher image resolutions, the buffering requirements between communicating functional modules increase correspondingly. The performance and cost of these applications can change dramatically depending on the implementation methods for FIFO buffers and the data delivery methods between modules. This paper introduces a new FIFO hardware mapping algorithm based on pointer-based token delivery from dataflow semantics for image and video processing applications. This approach significantly improves the performance of dataflow based implementation of image and video processing systems, and allows effective prediction of changes in performance and buffer memory requirements associated with changes in image resolution. Our pointer-based token delivery method allows indirect token delivery between actors by pointers in conjunction with use of a shared memory. Each pointer references a data block stored in the shared memory. In pointer-based token delivery, a buffer can be configured to be implemented as the combination of a small, fast FIFO and a larger, relatively cheap shared memory while providing an attractive trade-off between performance and hardware cost. We present the complete semantics of our pointer-based modeling method, systematic techniques for mapping representations using these semantics into efficient implementations, and experimental results that demonstrate the performance of the proposed pointer-based techniques.

Provision of free, downloadable industry-standard reconfigurable hardware EDA tools is a growing and welcome trend. Low cost, ever-more-complex FPGA-based embedded system development platforms with host interface control are becoming more readily available. This paper describes a series of structured VHDL workshop modules which use the Xilinx ISE Webpack/ModelsimXE electronic design automation (EDA) tools and Digilent Spartan-3 hardware development system. The described course implements an extendable embedded system, which performs a selection of image and audio processing applications, controlled by a host graphical user interface (GUI). A formal structured design, documentation and test methodology is incorporated throughout the course. Enhancements to EDA tool VHDL and diagram creation wizard support are proposed to aid VHDL capture automation and design visualisation. Improved student understanding and achievement level in VHDL programming, digital embedded system design techniques and FPGA implementation has resulted

Window-based operations such as two dimensional (2-D) convolution operations are commonly used in image and video processing applications. In this paper, a new design technique that considers the neighboring pixels within the window to detect and eliminate redundant or unnecessary computations for power reduction is presented. A novel on-chip detection technique is developed for the proposed neighborhood dependent approach (NDA) to reduce computations. In addition, data partitioning methodology is employed in the on chip buffer design support real-time operations. This NDA method is applied to different window buffering schemes and experimental results are presented.

PLX is a concise instruction set architecture (ISA) that combines the most useful features from previous generations of multimedia instruction sets with newer ISA features for high-performance, low-cost multimedia information processing. Unlike previous multimedia instruction sets, PLX is not added onto a base processor ISA, but designed from the beginning as a standalone processor architecture optimized for media processing. Its design goals are high performance multimedia processing, general-purpose programmability to support an ever-growing range of applications, simplicity for constrained environments where low power and low cost are paramount, and scalability for higher performance in less constrained multimedia systems. Another design goal of PLX is to facilitate exploration and evaluation of novel techniques in instruction set architecture, microarchitecture, arithmetic, VLSI implementations, compiler optimizations, and parallel algorithm design for new computing paradigms. Key characteristics of PLX are a fully subword-parallel architecture with novel features like wordsize scalability from 32-bit to 128-bit words, a new definition of predication, and an innovative set of subword permutation instructions. We demonstrate the use and high performance of PLX on some frequently-used code kernels selected from image, video, and graphics processing applications: discrete cosine transform, pixel padding, clip test, and median filter. Our results show that a 64-bit PLX processor achieves significant speedups over a basic 64-bit RISC processor and over IA-32 processors with MMX and SSE multimedia extensions. Using PLX's wordsize scalability feature, PLX-128 often provides an additional 2× speedup over PLX-64 in a cost-effective way. Superscalar or VLIW (Very Long Instruction Word) PLX implementations can also add additional performance through inter-instruction, rather than intra-instruction parallelism. We also describe the PLX testbed and its software tools for architecture and related research.

Many real-time signal processing applications are dominated by iterative loop constructs which exhibit a large amount of parallelism. In general, a realisation matched to the required rate of these applications exploits only a relatively small part of the parallelism available in the algorithm. This paper addresses the important problem of selecting the appropriate algorithmic-level decisions, in particular loop manipulations and the like, to arrive at an area-optimized specification for use in register-transfer level synthesis tools. One of the crucial cost factors in this optimisation is memory storage related. An effective model and methodology are proposed to derive an optimized architecture with fully matched throughput, while avoiding a full traversal of the large search space. The effectiveness of our approach is substantiated with several realistic test cases. >

In this paper, we propose an efficient memory partitioning algorithm for parallel data access via data reuse. We found that for most of the applications in image and video processing, a large amount of data can be reused among different iterations in a loop nest. Motivated by this observation, we propose to cache these reusable data by on-chip registers. The on-chip registers used to cache the re-fetched data can be organized as chains of registers. The non-reusable data are then partitioned into several memory banks by a memory partition algorithm. We revise the existing padding method to cover cases occurring frequently in our method that some components of partition vector are zeros. Experimental results have demonstrated that compared with the state-of-the-art algorithms the proposed method can reduce the required number of memory banks by 59.8% on average. The corresponding resources for bank mapping is also significantly reduced. The number of LUTs is reduced by 78.6%. The number of Flip-Flops is reduced by 66.8%. The number of DSP48Es is reduced by 41.7%. Moreover, the storage overheads of the proposed method are zeros for most of the widely used access patterns in image filtering.

Bilateral filter is one of the state-of-the-art methods for noise reduction in images. The plausible visual result the filter produces makes it a common choice for image and video processing applications, yet, its high computational complexity makes a real-time implementation a challenging task. Presented here is a parallel version of the bilateral filter using a lazy sliding window, suitable for SIMD-type architectures.

This paper presents a new method for accurate skin detection. Skin detection techniques are used in various image and video processing applications, such as image categorization, face detection and tracking and, more recently, in selective image enhancement for digital TV products. The proposed method achieves high accuracy using a combination of color information and pseudo-morphological processing, and is suitable for low-cost and custom hardware implementations.

Wavelet transform-based methods are currently used in a variety of image and video processing applications and are popular candidates for future image and video processing standards. Very little, however, has been done to develop efficient and simple stochastic models for wavelet image data. In this paper we review some existing modeling approaches for wavelet image data. Inspired by our recent estimation-quantization image coder, we introduce an efficient graphical stochastic model for wavelet image coefficients. Specifically, we propose to model wavelet image coefficients as Gaussian random variables with parameters determined by an underlying hidden Markov-type process. This stochastic model is defined using a factor graph framework. We test our model for denoising images corrupted by additive Gaussian noise. Our results are among the state-of-the-art in the field and they indicate the promise of the proposed model.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. This paper introduces an image quality index, Visual region of interest Weighted Quality Index (VroiWQI). The index integrates the notion of Visual regions of interest with the measurement of structural distortion between the original image and the distorted image to effectively match with Human Visual System (HVS). The method evaluates the structural distortion between the original image and the distorted image to arrive at the quality index in a local region. These local indices are then weighted based on the Visual interest of the corresponding region, characterized by entropy in the local region that emphasizes the texture variance. VroiWQI of the image is calculated by averaging these weighted indices, considering different subsets of visual regions with degree of interest greater than a threshold value. Test results show that VroiWQI correlates well with subjective scores.

Automatic detection of region of interest (ROIs) in a complex image or video, such as angiogram pictures and neurosurgery video, is a critical task in many medical image and video processing applications. In this paper, we present a new method that addresses several challenges in automatic detection of ROI of neurosurgical video for ROI coding which is used for neurophysiological intraoperative monitoring (IOM) system. This method is based on an object tracking technique with the multivariate density estimation theory, combined with the shape information of the object in the neurosurgical video. By defining the ROIs for neurosurgical video, this method produces a smooth and convex emphasis region within which surgical procedures are performed. A large bandwidth budget is assigned within the ROI to archive high-fidelity Internet transmission. Outside the ROI, a small bandwidth budget is allocated to efficiently utilize the bandwidth resource in Internet connection. And we believe this method also can be used to the image-guide surgery (IGS) system to track the positions of surgical instruments in the physical space occupied by the patient after some improvement.

The objective measurement of blurring artifacts plays an important role in the dynamic monitoring, adjusting image quality, optimizing algorithms and parameter settings in a variety of image and video processing applications. In this paper, we propose a no-reference metric to estimate blurriness distortion. The work addresses the problem that existing no-reference metrics fail to predict the correct amount of blurriness in images and video with varying content. The proposed metric uses a human vision system (HVS) based approach to detect the edges in the images and also uses singular value decomposition (SVD) to estimate the blurriness. The metric performance is demonstrated by comparing with other existing no-reference metrics for two different publicly available data bases. Test results show that the proposed metric exhibits a strong correlation with subjective quality scores.

Measurement of visual quality is of fundamental importance for numerous stereoscopic image and video processing applications. The effect of distortions on the perceived stereoscopic images quality already been investigated. But few works focus on the weight preference between distortion types. This paper firstly analyzed the weight preference of stereoscopic images between distortion types. The results show that human rating on quality of stereoscopic image will be insensitive, when the quality of left view image keep constant and the right view is Gaussian blurred. Secondly, this paper investigates whether low-pass filtering on JPEG compressed stereoscopic image can enhance the stereoscopic perceptual quality. The results of the experiments show that low-pass filtering may be used to enhance the perceived quality of stereoscopic images in which the right view image is JPEG compressed at an extent of distortion. Furthermore, the enhancement is image content dependent.

As wireless video products evolve, they demand more sophisticated processing at higher resolutions and frame rates. Computational performance and energy efficiency have become critical design issues. This paper presents the Quantized Color Pack eXtension (QCPX) combined with a loop unrolling (LU) technique to improve execution performance and energy efficiency of color image and video processing applications. QCPX applied to a 32-bit datapath processor supports parallel operations on two packed 16-bit YCbCr (Y: luminance, Cr and Cb: chrominance) color pixels, providing greater subword-level parallelism by increasing the number of smaller color pixels packed into a word. Instruction-level parallelism can be further enhanced through loop unrolling. These techniques provide greater performance and efficiency for multimedia workloads on mobile systems. Experimental results on a set of media benchmark applications indicate that the LU plus QCPX-optimized version achieves a speedup ranging from 3.8 to 7.9 while reducing the energy consumption from 76% to 87% over the baseline version on identically configured, dynamically scheduled ILP superscalar processors. The LU plus QCPX-optimized version also outperforms the LU plus MDMX-like (MIPS’s multimedia extension) version.