We have developed a new efficient neural network-based algorithm for Alife application in a competitive world whereby the effects of interactions among organisms are evaluated in a weak form by exploiting the position of nearest food elements into consideration but not the positions of the other competing organisms. Two online learning algorithms, an instructive ASL (adaptive supervised learning) and an evaluative feedback-oriented RL (reinforcement learning) algorithm developed have been tested in simulating Alife environments with various neural network algorithms. The constructive compound neural network algorithm FuzGa guided by the ASL learning algorithm has proved to be most efficient among the methods experimented including the classical constructive cascaded CasCor algorithm of [18],[19] and the fixed non-constructive fuzzy neural networks. Adopting an adaptively selected best sequence of feedback action period Δα which we have found to be a decisive parameter in improving the network efficiency, the ASL-guided FuzGa had a performance of an averaged fitness value of 541.8 (standard deviation 48.8) as compared with 500(53.8) for ASL-guided CasCor and 489.2 (39.7) for RL-guided FuzGa. Our FuzGa algorithm has also outperformed the CasCor in time complexity by 31.1%. We have elucidated how the dimensionless parameter food availability FA representing the intensity of interactions among the organisms relates to a best sequence of the feedback action period Δα and an optimal number of hidden neurons for the given configuration of the networks. We confirm that the present solution successfully evaluates the effect of interactions at a larger FA, reducing to an isolated solution at a lower value of FA. The simulation is carried out by thread functions of Java by ensuring the randomness of individual activities.

This paper presents a new compound constructive algorithm of neural networks whereby the fuzzy logic technique is explored as an efficient learning algorithm to implement an optimal network construction from an initial simple 3-layer network while the genetic algorithm is used to help design an improved network by evolutions. Numerical simulations on artificial life demonstrate that compared with the existing network design algorithms such as the constructive algorithms, the pruning algorithms and the fixed, static architecture algorithm, the present algorithm, called FuzGa, is efficient in both time complexity and network performance. The improved time complexity comes from the sufficiently small 3 layer design of neural networks and the genetic algorithm adopted partly because the relatively small number of layers facilitates an utilization of an efficient steepest descent method in narrowing down the solution space of fuzzy logic and partly because trappings into local minima can be avoided by genetic algorithm, contributing to considerable saving in time in the processing of network learning and connection. Compared with 54. 8 minutes of MLPs with 65 hidden neurons, 63. 1 minutes of FlexNet or 96. 0 minutes of Pruning, our simulation results on artificial life show that the CPU time of the present method reaching the target fitness value of 100 food elements eaten for the present FuzGa has improved to 42. 3 minutes by SUN's SPARCstation-10 of SuperSPARC 40 MHz machine for example. The role of hidden neurons is elucidated in improving the performance level of the neural networks of the various schemes developed for artificial life applications. The effect of population size on the performance level of the present FuzGa is also elucidated.

A wide variety of visual textures could be successfully modeled as spatially variant by quantitatively describing them through the variation of their local spatial frequency and/or local orientation components. This class of patterns includes flow-like, granular or oriented textures. Modeling is achieved by assuming that locally, textured images contain a single dominant component describing their local spatial frequency and modulating amplitude or contrast. Spatially variant textures are non-homogeneous in the sense of having nonstationary local spectra, while remaining locally coherent. Segmenting spatially variant textures is the challenging task undertaken in this paper. Usually, the goal of texture segmentation is to split an image into regions with homogeneous textural properties. However, in the case of image regions with spatially variant textures, there is no global homogeneity present and thus segmentation passes through identification of regions with globally nonstationary, but locally coherent, textural content. Local spatial frequency components are accurately estimated using Gabor wavelet outputs along with the absolute magnitude of the convolution of the input image with the first derivatives of the underlying Gabor function. In this paper, a frequency estimation approach is used for segmentation. Indeed, at the boundary between adjacent textures, discontinuities occur in texture local spatial frequency components. These discontinuities are interpreted as corresponding to texture boundaries. Experimental results are in remarkable agreement with human visual perception, and demonstrate the effectiveness of the proposed technique.

Reversible variable length codes (RVLCs), which make instantaneous decoding possible in both forward and backward directions, are exploited to code data stream in noisy enviroments. Because there is no redundancy in code words of RVLCs, RVLCs are suitable for very low bit-rate video coding. Golomb-Rice code, one of variable length code for infinite number of symbols, is widely used to encode exponentially distributed non-negative integers. We propose a reversible variable length code by modifying Golomb-Rice code, which is called parity check reversible Golomb-Rice code and abbreviated to P-RGR code. P-RGR code has the same code length distribution as GR code but can detect one-bit error in any arbitrary position of the code stream. The sets of P-RGR code words in both directions are identical so that they can be constructed by nearly the same algorithm. Furthermore, this paper also gives a general construction method for all instantaneously decodable RGR codes.

A framework is proposed for segmenting image textures by using Gabor filters to detect boundaries between adjacent textured regions. By performing a multi-channel filtering of the input image with a small set of adaptively selected Gabor filters, tuned to underlying textures, feature images are obtained. To reduce the variance of the filter output for better texture boundary detection, a Gaussian post-filter is applied to the Gabor filter response over each channel. Significant local variations in each channel response are detected using a gradient operator, and combined through channel grouping to produce the texture gradient. A subsequent post-processing produces expected texture boundaries. The effectiveness of the proposed technique is demonstrated through experiments on synthetic and natural textures.

Unlike the time-consuming contour tracking method of snakes [5] which requires a considerable number of iterated computations before contours are successfully tracked down, we present a faster and accurate model-based landmarks" tracking method where a single iteration of the dynamic programming is sufficient to obtain a local minimum to an integral measure of the elastic and the image energy functionals. The key lies in choosing a relatively small number of salient land-marks", or features of objects, rather than their contours as a target of tracking within the image structure. The landmarks comprising singular points along the model contours are tracked down within the image structure all inside restricted search areas of 41 41 pixels whose respective locations in image structure are dictated by their locations in the model. A Manhattan distance and a template corner detection function of Singh and Shneier [7] are used as elastic energy and image energy respectively in the algorithm. A first approximation to the image contour is obtained in our method by applying the thin-plate spline transformation of Bookstein [2] using these landmarks as fixed points of the transformation which is capable of preserving a global shape information of the model including the relative configuration of landmarks and consequently surrounding contours of the model in the image structure. The actual image contours are further tracked down by applying an active edge tracker using now simplified line search segments so that individual differences persisting between the mapped model contour are substantially eliminated. We have applied our method tentatively to portraits of a class album to demonstrate the effectiveness of the method. Our experiments convincingly show that using only about 11 feature points our method provides not only a much improved computational complexity requiring only 0.94sec. in CPU time by SGI's indigo2 but also more accurate shape representations than those obtained by the snakes methods. The method is powerful in a problem domain where the model-based approach is applicable, possibly allowing real time processing because a most time consuming algorithm of corner template evaluation can be easily implemented by parallel processing firmware.