一种新的不基于Hough变换的随机椭圆检测算法

椭圆检测在模式识别领域中占据着非常重要的位置。常见的基于Hough变换的椭圆检测算法(如RHT算法)存在着占用大量存储空间及计算耗时等缺点。本文提出一种高效随机的椭圆检测算法(RED)。该算法不基于Hough变换,其原理是:首先从一幅图像中随机地挑选出6个点,并定义一个约束距离以确定在此图像中是否存在一个可能的椭圆;当可能椭圆确定之后,引入椭圆点收集过程以进一步确定可能椭圆是否是待检测的真实椭圆。通过对具有不同噪声的合成图像以及真实图像进行测试,结果表明RED算法在低噪声与适度噪声的情况下,速度明显快于RHT算法。     

Multi-ellipses detection on images inspired by collective animal behavior

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact with each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions), while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed, and robustness.     

Schema genetic algorithm for fractal image compression

In this paper, fractal image compression using schema genetic algorithm (SGA) is proposed. Utilizing the self-similarity property of a natural image, the partitioned iterated function system (PIFS) will be found to encode an image through genetic algorithm (GA) method. In SGA, the genetic operators are adapted according to the schema theorem in the evolutionary process performed on the range blocks. Such a method can speed up the encoder and also preserve the image quality. Simulations show that the encoding time of our method is over 100 times faster than that of the full search method, while the retrieved image quality is still acceptable. The proposed method is also compared to another GA method proposed by Vences and Rudomin. Simulations also show that our method is superior to their method in both the speedup ratio and retrieved quality. Finally, a comparison of the proposed SGA to the traditional GA is presented to demonstrate that when the schema theorem is embedded, the performance of GA has significant improvement.     

基于随机Hough变换的深度图像分割

提出了基于随机Hough变换的深度图像分割算法,该算法采用随机Hough变换在深度图像中寻找平面,具有对噪声不敏感的优点．通过对一常用深度图像数据库(ABW图像库)的分割实验,并将实验结果同4种经典的深度图像分割算法在同一数据库中的分割结果作了比较分析,表明该算法对噪声不敏感,分割性能优于4种经典算法。     

局部PCA参数约束的Hough多椭圆分层检测算法

针对随机Hough变换(RHT)在复杂图像中检测圆及椭圆时随机采样所造成的大量无效采样、无效累积以及运算时间长等问题,提出基于局部PCA感兴趣参数约束Hough多椭圆分层检测思路。首先利用边缘检测算子获得边缘信息并去除边缘交叉点,在边缘图像中标记并提取出满足一定长度的连续曲线段;其次利用线段PCA方向分析确定是否属于有效曲线段;然后,对所有感兴趣曲线段按照标记顺序依次利用椭圆拟合办法初步得到感兴趣椭圆粗略参数,根据拟合结果进而模糊约束Hough变换参数搜索范围,得到精确椭圆参数;最后利用检测结果更新图像空间,删除已经检测到的椭圆,依次进行,直到所有椭圆检测完毕。实验结果表明,该算法在计算、存储消耗上均大大减少。     

Object Detection Using the Statistics of Parts

In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively.Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated class-conditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.     

基于中心提取的RHT在椭圆检测中的应用

充分利用椭圆的几何性质,借助椭圆的形状控制点约束和弦端点法向约束,大幅降低随机Hough变换(RHT)的无效采样和累积次数,并采用基于视觉感知聚类的模糊置信度对由同一个形变椭圆引入的多个虚假候选椭圆进行有效去除.实验结果表明:该算法与基于RHT的其他椭圆检测方法相比,具有检测速度快、精度高、抵抗椭圆的部分缺失和形变能力强等优点.     

Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)

An iterative randomized Hough transform (IRHT) is developed for detection of incomplete ellipses in images with strong noise. The IRHT iteratively applies the randomized Hough transform (RHT) to a region of interest in the image space. The region of interest is determined from the latest estimation of ellipse parameters. The IRHT “zooms in” on the target curve by iterative parameter adjustments and reciprocating use of the image and parameter spaces. During the iteration process, noise pixels are gradually excluded from the region of interest, and the estimation becomes progressively close to the target. The IRHT retains the advantages of RHT of high parameter resolution, computational simplicity and small storage while overcoming the noise susceptibility of RHT. Indivisible, multiple instances of ellipse can be sequentially detected. The IRHT was first tested for ellipse detection with synthesized images. It was then applied to fetal head detection in medical ultrasound images. The results demonstrate that the IRHT is a robust and efficient ellipse detection method for real-world applications.     

Application of the Fisher-Rao Metric to Ellipse Detection

The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is derived from a Gaussian model for the blurring of ellipses in the image. Two points in the parameter space are close together under the Fisher-Rao metric if the corresponding ellipses are close together in the image. The Fisher-Rao metric is accurately approximated by a simpler metric under the assumption that the blurring is small compared with the sizes of the ellipses under consideration. It is shown that the parameter space for the ellipses in the image has a finite volume under the approximation to the Fisher-Rao metric. As a consequence the parameter space can be replaced, for the purpose of ellipse detection, by a finite set of points sampled from it. An efficient algorithm for sampling the parameter space is described. The algorithm uses the fact that the approximating metric is flat, and therefore locally Euclidean, on each three dimensional family of ellipses with a fixed orientation and a fixed eccentricity. Once the sample points have been obtained, ellipses are detected in a given image by checking each sample point in turn to see if the corresponding ellipse is supported by the nearby image pixel values. The resulting algorithm for ellipse detection is implemented. A multiresolution version of the algorithm is also implemented. The experimental results suggest that ellipses can be reliably detected in a given low resolution image and that the number of false detections can be reduced using the multiresolution algorithm.     

Image Parsing: Unifying Segmentation,Detection, and Recognition

In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a parsing graph, in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches—generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns—generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. IEEE Trans. PAMI, 24(5):657–673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.     

Hybrid models based on biological approaches for speech recognition

This paper aims to adapt the Clonal Selection Algorithm (CSA) which is usually used to explain the basic features of artificial immune systems to the learning of Neural Networks, instead of Back Propagation. The CSA was first applied to a real world problem (IRIS database) then compared with an artificial immune network. CSA performance was contrasted with other versions of genetic algorithms such as: Differential Evolution (DE), Multiple Populations Genetic Algorithms (MPGA). The tested application in the simulation studies were IRIS (vegetal database) and TIMIT (phonetic database). The results obtained show that DE convergence speeds were faster than the ones of multiple population genetic algorithm and genetic algorithms, therefore DE algorithm seems to be a promising approach to engineering optimization problems. On the other hand, CSA demonstrated good performance at the level of pattern recognition, since the recognition rate was equal to 99.11% for IRIS database and 76.11% for TIMIT. Finally, the MPGA succeeded in generalizing all phonetic classes in a homogeneous way: 60% for the vowels and 63% for the fricatives, 68% for the plosives.     

An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times

In this paper we consider a multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times by minimizing total weighted tardiness and maximum completion time simultaneously. Whereas these kinds of problems are NP-hard, thus we proposed a multi-population genetic algorithm (MPGA) to search Pareto optimal solution for it. This algorithm comprises two stages. First stage applies combined objective of mentioned objectives and second stage uses previous stage’s results as an initial solution. In the second stage sub-population will be generated by re-arrangement of solutions of first stage. To evaluate performance of the proposed MPGA, it is compared with two distinguished benchmarks, multi-objective genetic algorithm (MOGA) and non-dominated sorting genetic algorithm II (NSGA-II), in three sizes of test problems: small, medium and large. The computational results show that this algorithm performs better than them.     

Projective reconstruction of ellipses from multiple images

This paper presents a new approach for reconstructing 3D ellipses (including circles) from a sequence of 2D images taken by uncalibrated cameras. Our strategy is to estimate an ellipse in 3D space by reconstructing N(≥5) 3D points (called representative points) on it, where the representative points are reconstructed by minimizing the distances from their projections to the measured 2D ellipses on different images (i.e., 2D reprojection error). This minimization problem is transformed into a sequence of minimization sub-problems that can be readily solved by an algorithm which is guaranteed to converge to a (local) minimum of the 2D reprojection error. Our method can reconstruct multiple 3D ellipses simultaneously from multiple images and it readily handles images with missing and/or partially occluded ellipses. The proposed method is evaluated using both synthetic and real data.     

Intermediate-level feature extraction in novel parallel environments

Abstract. Parallel systems provide an approach to robust computing. The motivation for this work arises from using modern parallel environments in intermediate-level feature extraction. This study presents parallel algorithms for the Hough transform (HT) and the randomized Hough transform (RHT). The algorithms are analyzed in two parallel environments: multiprocessor computers and workstation networks. The results suggest that both environments are suitable for the parallelization of HT. Because scalability of the parallel RHT is weaker than with HT, only the multiprocessor environment is suitable. The limited scalability forces us to use adaptive techniques to obtain good results regardless of the number of processors. Despite the fact that the speedups with HT are greater than with RHT, in terms of total computation time, the new parallel RHT algorithm outperforms the parallel HT. Received: 8 December 2001 / Accepted: 5 June 2002 Correspondence to: V. Kyrki     

函数级进化型硬件在模式识别中的应用

设计了一个基于函数级进化型硬件(FEHW)的高速模式识别系统，并提出了一种适合此系统的改进遗传学习算法——可变染色体长度遗传算法(VGA)。利用VGA代替简单的遗传算法(SGA)来处理大输入的图像数据，实时实现了3类飞机识别。仿真结果表明，VGA进化速度是SGA的9倍，识别率达到80％以上。     

Three self-adaptive multi-objective evolutionary algorithms for a triple-objective project scheduling problem

Finding a Pareto-optimal frontier is widely favorable among researchers to model existing conflict objectives in an optimization problem. Project scheduling is a well-known problem in which investigating a combination of goals eventuate in a more real situation. Although there are many different types of objectives based on the situation on hand, three basic objectives are the most common in the literature of the project scheduling problem. These objectives are: (i) the minimization of the makespan, (ii) the minimization of the total cost associated with the resources, and (iii) the minimization of the variability in resources usage. In this paper, three genetic-based algorithms are proposed for approximating the Pareto-optimal frontier in project scheduling problem where the above three objectives are simultaneously considered. For the above problem, three self-adaptive genetic algorithms, namely (i) A two-stage multi-population genetic algorithm (MPGA), (ii) a two-phase subpopulation genetic algorithm (TPSPGA), and (iii) a non-dominated ranked genetic algorithm (NRGA) are developed. The algorithms are tested using a set of instances built from benchmark instances existing in the literature. The performances of the algorithms are evaluated using five performance metrics proposed in the literature. Finally according to the technique for order preference by similarity to ideal solution (TOPSIS) the self-adaptive NRGA gained the highest preference rank, followed by the self-adaptive TPSPGA and MPGA, respectively.     

基于中心估计的椭圆检测

椭圆检测在图像理解中有重要的作用。为克服标准Hough变换对时空需求高的缺点,设计了一种改进算法。通过椭圆对称特性估计图像中可能存在的椭圆中心,利用长轴确定椭圆中心及夹角参数只需一维累积数组对椭圆短轴的投票,采用聚类分析技术将检测到的虚椭圆归类到对应的真实椭圆。对合成图像和实际图像的实验表明算法的正确和高效。     

一种提取直线的随机方法

基于Hough变换提取直线的方法，由于要预先量化参数空间，因此需要很大的存储量和计算量．基于RHT(Randomized Hough Transform)提取直线的方法是通过随机选取两个点得到直线的参数，而后在参数空间对相应的参数进行累加、判断，该方法虽然无需预先量化参数空间，但是其在直线检测时，收敛速度慢．为此提出一种新的随机检测直线(Random Line Detection)的方法，在图象边缘点构成的数据空间中随机选取3个点，根据距离准则获得一条可能的直线，然后在数据空间中进一步判断直线的真实性，实验证实了该方法能有效的减少存储空间并降低计算量。     

改进的自适应免疫遗传算法在图像增强中的应用

针对传统图像增强方法中图像细节丢失、图像对比度不明显以及方法普适性差等缺点,提出了一种自适应免疫遗传算法用于图像增强。该算法与传统遗传算法的不同在于引入免疫算子抑制优化过程中出现的退化现象,根据个体适应度自适应调整遗传算子的概率值和基因变异位数,从而增强了种群多样性,提高了算法快速性和全局收敛性。实验结果表明：基于该算法的图像增强具有图像细节清楚、对比度强、方法普适性强等优点。     

Chemical genetic algorithms – a coevolutionary method to optimize code translation in GAs

We propose a chemical genetic algorithm (CGA) in which several types of molecule react with each other in a cell. A cell includes a binary string (DNA) and smaller molecules, and the fundamental mapping from binary substrings on DNA (genotype) to real values for the output parameters (phenotype) is specified by a set of molecules called aminoacyl-tRNAs. Through evolutionary modification of the genetic information on the DNA, the codes on the DNA and the genotype-to-phenotype translation coevolve, which allows optimization of the code translation during evolution. The CGA is applied to several benchmark problems, and its effectiveness is demonstrated in comparison with a simple genetic algorithm (SGA).This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003