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151.
We extend a dynamic approach of behavior generation to the representation of spatial information. Two levels of dynamics integrate dead-reckoning, dominant far from home bases, and piloting, dominant near home bases. When the view-based piloting system recognizes a home base, visual place information recalibrates the dead-reckoning system, inverting the hierarchical ordering of the two dynamic levels by time scale inversion. Reference views taken at discrete home bases are recognized invariantly under rotation of views. This process yields compass information. Continuous translational information is obtained as a neural place representation built from view correlations with a scattered set of local views. This self-calibrating cognitive map couples into a dynamics of heading direction integrating the behaviors of obstacle avoidance and target acquisition. Targets can be designated in terms of the cognitive map. We demonstrate the dynamical model in simulation. 相似文献
152.
R. S. Fyath K. N. Darraj M. S. Alam M. N. Islam M. M. Alkhatib 《Optical Memory & Neural Networks》2007,16(3):125-135
A new joint transform correlation (JTC) technique, named two-channel JTC (TJTC), is proposed in this paper for optical pattern
recognition applications. The TJTC technique independently evaluates the autocorrelation and crosscorrelation values of the
reference and the target images and employs a modified decision algorithm. In addition, optical threshold operation and fringe-adjusted
filter are incorporated in the proposed technique to enhance the correlation output and to improve the discrimination performance.
The proposed technique shows better recognition performance compared to existing JTC techniques. Computer simulation are presented
to investigate the salient features of the proposed TJTC technique with noise-free as well as noisy input scenes.
The text was submitted by the authors in English. 相似文献
153.
In the area of image retrieval from data bases and for copyright protection of large image collections there is a growing demand for unique but easily computable fingerprints for images. These fingerprints can be used to quickly identify every image within a larger set of possibly similar images. This paper introduces a novel method to automatically obtain such fingerprints from an image. It is based on a reinterpretation of an image as a Riemannian manifold. This representation is feasible for gray value images and color images. We discuss the use of the spectrum of eigenvalues of different variants of the Laplace operator as a fingerprint and show the usability of this approach in several use cases. Contrary to existing works in this area we do not only use the discrete Laplacian, but also with a particular emphasis the underlying continuous operator. This allows better results in comparing the resulting spectra and deeper insights in the problems arising. We show how the well known discrete Laplacian is related to the continuous Laplace-Beltrami operator. Furthermore, we introduce the new concept of solid height functions to overcome some potential limitations of the method. 相似文献
154.
医疗专家系统主要使用基于知识的技术,其中的决策规则和策略来自于人类的专家。把这些知识和各种推理方法结合,可以建立一个模拟专家决策过程的系统。建立这样一个系统,需要经常与专家磋商,以获取专家的知识,因而需要大量的时间和精力。为此,本文提出直接从数据中提取有效的信息,即用神经网络提取隐含在大量数据中对医疗诊断有效的信息,继之与基于规则的知识,各种推理方法相结合,建立一个神经网络专家系统。 相似文献
155.
Target differentiation with simple infrared sensors using statistical pattern recognition techniques
Billur Barshan Author Vitae Tayfun Aytaç Author VitaeAuthor Vitae 《Pattern recognition》2007,40(10):2607-2620
This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry and/or surface type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for. 相似文献
156.
157.
本文探讨经短时信号处理后的语音信号帧间相关信息对基于隐马尔可夫模型(HMM)的语音识别系统识虽精度的影响,鉴于HMM的输出独立假设导致语音帧间相关信息的损失,本文提出了一种描述帧间相关信息的统计模型-马尔可夫链(MCM)用来弥补HMM在这方面的缺陷;经非特定人和多话者孤立字实验表明,用MCM作为HMM的辅助模型,可将原有HMM系统的识别率提高约1~6个百分点。 相似文献
158.
Loschky Lester C.; Sethi Amit; Simons Daniel J.; Pydimarri Tejaswi N.; Ochs Daniel; Corbeille Jeremy L. 《Canadian Metallurgical Quarterly》2007,33(6):1431
People can recognize the meaning or gist of a scene from a single glance, and a few recent studies have begun to examine the sorts of information that contribute to scene gist recognition. The authors of the present study used visual masking coupled with image manipulations (randomizing phase while maintaining the Fourier amplitude spectrum; random image structure evolution [RISE]; J. Sadr & P. Sinha, 2004) to explore whether and when unlocalized Fourier amplitude information contributes to gist perception. In 4 experiments, the authors found that differences between scene categories in the Fourier amplitude spectrum are insufficient for gist recognition or gist masking. Whereas the global 1/f spatial frequency amplitude spectra of scenes plays a role in gist masking, local phase information is necessary for gist recognition and for the strongest gist masking. Moreover, the ability to recognize the gist of a target image was influenced by mask recognizability, suggesting that conceptual masking occurs even at the earliest stages of scene processing. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
159.
On-Line Shape recognition with incremental training using binary synaptic weights algorithm 总被引:2,自引:0,他引:2
Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set. 相似文献
160.