Shing-Chow Chan

Plenoptic sampling

This paper studies the problem of plenoptic sampling in image-based rendering (IBR). From a spectral analysis of light field signals and using the sampling theorem, we mathematically derive the analytical functions to determine the minimum sampling rate for light field rendering. The spectral support of a light field signal is bounded by the minimum and maximum depths only, no matter how complicated the spectral support might be because of depth variations in the scene. The minimum sampling rate for light field rendering is obtained by compacting the replicas of the spectral support of the sampled light field within the smallest interval. Given the minimum and maximum depths, a reconstruction filter with an optimal and constant depth can be designed to achieve anti-aliased light field rendering. Plenoptic sampling goes beyond the minimum number of images needed for anti-aliased light field rendering. More significantly, it utilizes the scene depth information to determine the minimum sampling curve in the joint image and geometry space. The minimum sampling curve quantitatively describes the relationship among three key elements in IBR systems: scene complexity (geometrical and textural information), the number of image samples, and the output resolution. Therefore, plenoptic sampling bridges the gap between image-based rendering and traditional geometry-based rendering. Experimental results demonstrate the effectiveness of our approach.

Survey of image-based representations and compression techniques

We survey the techniques for image-based rendering (IBR) and for compressing image-based representations. Unlike traditional three-dimensional (3-D) computer graphics, in which 3-D geometry of the scene is known, IBR techniques render novel views directly from input images. IBR techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative techniques. IBR techniques demonstrate a surprising diverse range in their extent of use of images and geometry in representing 3-D scenes. We explore the issues in trading off the use of images and geometry by revisiting plenoptic-sampling analysis and the notions of view dependency and geometric proxies. Finally, we highlight compression techniques specifically designed for image-based representations. Such compression techniques are important in making IBR techniques practical.

BIOMECHANICAL ASSESSMENT OF PLANTAR FOOT TISSUE IN DIABETIC PATIENTS USING AN ULTRASOUND INDENTATION SYSTEM

The biomechanical properties of plantar tissues were investigated for four older neuropathic diabetic patients and four healthy younger subjects. Indentation tests were performed at four high-pressure areas with three postures in each subject. The tissue thickness and effective Youngs modulus were measured by an ultrasound (US) indentation system. The system comprised a pen-size probe having a US transducer at the tip and a load cell connected in series with it. Results showed that the plantar soft tissues of the elderly diabetic patients were significantly stiffer and thinner when compared with the healthy young subjects. For the diabetic subjects tested, the Youngs modulus at the 1st metatarsal head was significantly larger than those at the other three sites. This site-dependence was not observed in the healthy young subjects. The plantar tissue became significantly stiffer in the healthy young subjects as a result of posture changes. This posture-dependence of the Youngs modulus was not established for the elderly diabetic group.

A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.

Least mean M-estimate algorithms for robust adaptive filtering in impulse noise

Adaptive filters with suitable nonlinear devices are very effective in suppressing the adverse effect due to impulse noise. In a previous work, the authors have proposed a new class of nonlinear adaptive filters using the concept of robust statistics [1,2]. The robust M-estimator is used as the objective function, instead of the mean square errors, to suppress the impulse noise. The optimal coefficient vector for such nonlinear filter is governed by a normal equation which can be solved by a recursive least squares like algorithm with O(N2) arithmetic complexity, where N is the length of the adaptive filter. In this paper, we generalize the robust statistic concept to least mean square (LMS) and transform domain LMS algorithms. The new fast nonlinear adaptive filtering algorithms called the least mean M-estimate (LMM) and transform domain LMM (TLMM) algorithms are derived. Simulation results show that they are robust to impulsive noise in the desired and input signals with an arithmetic complexity of order O(N).

Image-Based Rendering and Synthesis

One of the most important applications in multiview imaging (MVI) is the development of advanced immersive viewing or visualization systems using, for instance, 3DTV. With the introduction of multiview TVs, it is expected that a new age of 3DTV systems will arrive in the near future. Image-based rendering (IBR) refers to a collection of techniques and representations that allow 3-D scenes and objects to be visualized in a realistic way without full 3-D model reconstruction. IBR uses images as the primary substrate. The potential for photorealistic visualization has tremendous appeal, and it has been receiving increasing attention over the years. Applications such as video games, virtual travel, and E-commerce stand to benefit from this technology. This article serves as a tutorial introduction and brief review of this important technology. First the classification, principles, and key research issues of IBR are discussed. Then, an object-based IBR system to illustrate the techniques involved and its potential application in view synthesis and processing are explained. Stereo matching, which is an important technique for depth estimation and view synthesis, is briefly explained and some of the top-ranked methods are highlighted. Finally, the challenging problem of interactive IBR is explained. Possible solutions and some state-of-the-art systems are also reviewed.

A new two-dimensional fast cosine transform algorithm

The discrete cosine transform (2-D DCT) is based on a one-dimensional fast cosine transform (1-D FCT) algorithm. Instead of computing the 2-D transform using the row-column method, the 1-D algorithm is extended by means of the vector-radix approach. Derivation based on both the sequence splitting and Kronecker matrix product method are discussed. The sequence splitting approach has the advantage that all the underlying operations are shown clearly, while the matrix product representations are more compact and readily generalized to higher dimensions. The bit reversal operations are placed before the recursive additions so that the recursive operations can be performed in a very regular manner. This greatly simplifies the indexing problem in the software implementation of the algorithms. The vector-radix algorithm saves 25% multiplications as compared with the row-column method. >

The design and multiplier-less realization of software radio receivers with reduced system delay

This work studies the design and multiplier-less realization of a new software radio receiver (SRR) with reduced system delay. It employs low-delay finite-impulse response (FIR) and digital allpass filters to effectively reduce the system delay of the multistage decimators in SRRs. The optimal least-square and minimax designs of these low-delay FIR and allpass-based filters are formulated as a semi-definite programming (SDP) problem, which allows zero magnitude constraint at /spl omega/=/spl pi/ to be incorporated readily as additional linear matrix inequalities (LMIs). By implementing the sampling rate converter (SRC) using a variable digital filter (VDF) immediately after the integer decimators, the needs for an expensive programmable FIR filter in the traditional SRR is avoided. A new method for the optimal minimax design of this VDF-based SRC using SDP is also proposed and compared with traditional weight least squares method. Other implementation issues including the multiplier-less and digital signal processor (DSP) realizations of the SRR and the generation of the clock signal in the SRC are also studied. Design results show that the system delay and implementation complexities (especially in terms of high-speed variable multipliers) of the proposed architecture are considerably reduced as compared with conventional approaches.

Fast algorithms for computing the discrete cosine transform

Efficient methods for mapping odd-length type-II, type-II, and type-IV DCTS to a real-valued DFT are presented. It is found that odd-length type-II and type-III DCTs can be transformed, by means of an index mapping, to a real-valued DFT of the same length using permutations and sign changes only. The real-valued DFT can then be computed by efficient real-valued FFT algorithms such as the prime factor algorithm. Similar mapping is introduced to convert a type-IV DCT to a real-valued DFT up to a scaling factor and some additions. Methods for computing DCTs with even lengths are also discussed. >

Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera

This paper presents a new superpixel-based hand gesture recognition system based on a novel superpixel earth movers distance metric, together with Kinect depth camera. The depth and skeleton information from Kinect are effectively utilized to produce markerless hand extraction. The hand shapes, corresponding textures and depths are represented in the form of superpixels, which effectively retain the overall shapes and color of the gestures to be recognized. Based on this representation, a novel distance metric, superpixel earth movers distance (SP-EMD), is proposed to measure the dissimilarity between the hand gestures. This measurement is not only robust to distortion and articulation, but also invariant to scaling, translation and rotation with proper preprocessing. The effectiveness of the proposed distance metric and recognition algorithm are illustrated by extensive experiments with our own gesture dataset as well as two other public datasets. Simulation results show that the proposed system is able to achieve high mean accuracy and fast recognition speed. Its superiority is further demonstrated by comparisons with other conventional techniques and two real-life applications.