A Practical Sequential Method for Principal Component Analysis

When increasing numbers of principalcomponents are extracted by using the sequentialmethod proposed in [1] by Banour and Azimi-Sadjadi, the accumulated extractionerror will become dominant and affect the extractionsof the remaining principal components. To improvethis, we suggest that the initial weight vector forthe extraction of the next component should beorthogonal to the eigensubspace spanned by the alreadyextracted weight vectors. Simulation results showthat both the convergence and the accuracy of theextraction are improved. Our improved method is alsocapable of extracting full eigenspace accurately.     

Data compression with spherical wavelets and wavelets for the image-based relighting

Image based relighting (IBL) solves the illumination control problem in the image-based modelling and rendering. However, it trades a drastic increase of storage requirement caused by the tremendous reference images pre-captured under various illumination conditions. In this paper, we propose a spherical wavelet transform and wavelet transform (SWT–WLT) based approach to compress the huge IBL dataset. Two major steps are involved. First, the spherical wavelet transform (SWT) is used to reduce the correlation between different reference images. Second, wavelet transform (WLT) is applied to compress those SW transformed images (SWT images). Due to the locality of SWT and WLT, the proposed method inherits an advantage of low memory requirement, hence, it is suitable for compressing arbitrarily large dataset. Using its integer format implementation this method can be further sped up with the help of bit shift operation. Simulations are given to evaluate its good features.     

GPU-friendly rendering for illumination adjustable images

An illumination adjustable image (IAI) contains a large number of pre-recorded reference images under various lighting directions. It describes the appearance of a scene illuminated under various lighting directions. Synthesized images about the scene illuminated by complicated lighting conditions are generated from those reference images. This paper presents practical and real-time rendering methods for IAIs based on spherical Gaussian kernel functions (SGKFs). The lighting property of an IAI is represented by a few number of lightmaps. With those lightmaps, we can use consumer-level graphics processing units (GPUs) to perform the rendering process. The rendering methods for directional light source, point light source and slide projector are discussed. Compared with the conventional spherical harmonic (SH) approach, the proposed SGKF approach offers similar distortion performance but it consumes less graphics memory and has a faster rendering speed.     

等立方体球面映射

立方体球面环境映射被广泛用于各种计算机图形学应用中.针对立方体球面映射中存在的采样不均匀问题,提出一种基于6个面结构的、在球面上均匀采样的球面映射,称为等立方体球面映射.由于该方法所得采样点在球面上均匀分布并具有相同的立方角,故可得到更高质量的绘制效果.等立方体球面映射的计算代价较低,并能直接使用GPU所提供的立方体球面映射的各种纹理操作,可达到非常高的渲染帧率.     

Stability and statistical properties of second-order bidirectionalassociative memory

In this paper, a bidirectional associative memory (BAM) model with second-order connections, namely second-order bidirectional associative memory (SOBAM), is first reviewed. The stability and statistical properties of the SOBAM are then examined. We use an example to illustrate that the stability of the SOBAM is not guaranteed. For this result, we cannot use the conventional energy approach to estimate its memory capacity. Thus, we develop the statistical dynamics of the SOBAM. Given that a small number of errors appear in the initial input, the dynamics shows how the number of errors varies during recall. We use the dynamics to estimate the memory capacity, the attraction basin, and the number of errors in the retrieved items. Extension of the results to higher-order bidirectional associative memories is also discussed.     

Analysis on Extended Ant Routing Algorithms for Network Routing and Management

Advances in mobile agent research have brought in a new method for network routing, ant routing. Recently, we have derived some preliminary results regarding the agent population growth property and the jumping behavior for an ant routing algorithm. The focus was on the expected number of agents in a node. In practice, the number of agents propagating on each network channel is also critical as the network channel bandwidth is limited. In this paper, we first propose two extended ant routing algorithms, and then provide an in-depth analysis on the population growth behavior of the propagating agents for these algorithms, both at nodes (hosts) and on edges (channels) of the network.     

The effect of weight fault on associative networks

In the past three decades, the properties of associative networks has been extensively investigated. However, most existing results focus on the fault-free networks only. In implementation, network faults can be exhibited in different forms, such as open weight fault and multiplicative weight noise. This paper studies the effect of weight fault on the performance of the bidirectional associative memory (BAM) model when multiplicative weight noise and open weight fault present. Assuming that connection weights are corrupted by these two common fault models, we study how many number of pattern pairs can be stored in a faulty BAM. Since one of important feature of associative network is error correction, we also study the number of pattern pairs can be stored in a faulty BAM when there are some errors in the initial stimulus pattern.     

Parallelization of cellular neural networks on GPU

Recently, cellular neural networks (CNNs) have been demonstrated to be a highly effective paradigm applicable in a wide range of areas. Typically, CNNs can be implemented using VLSI circuits, but this would unavoidably require additional hardware. On the other hand, we can also implement CNNs purely by software; this, however, would result in very low performance when given a large CNN problem size. Nowadays, conventional desktop computers are usually equipped with programmable graphics processing units (GPUs) that can support parallel data processing. This paper introduces a GPU-based CNN simulator. In detail, we carefully organize the CNN data as 4-channel textures, and efficiently implement the CNN computation as fragment programs running in parallel on a GPU. In this way, we can create a high performance but low-cost CNN simulator. Experimentally, we demonstrate that the resultant GPU-based CNN simulator can run 8–17 times faster than a CPU-based CNN simulator.     

Evolving Mazes from Images

We propose a novel reaction diffusion (RD) simulator to evolve image-resembling mazes. The evolved mazes faithfully preserve the salient interior structures in the source images. Since it is difficult to control the generation of desired patterns with traditional reaction diffusion, we develop our RD simulator on a different computational platform, cellular neural networks. Based on the proposed simulator, we can generate the mazes that exhibit both regular and organic appearance, with uniform and/or spatially varying passage spacing. Our simulator also provides high controllability of maze appearance. Users can directly and intuitively “paint” to modify the appearance of mazes in a spatially varying manner via a set of brushes. In addition, the evolutionary nature of our method naturally generates maze without any obvious seam even though the input image is a composite of multiple sources. The final maze is obtained by determining a solution path that follows the user-specified guiding curve. We validate our method by evolving several interesting mazes from different source images.