基于三角形和正方形的NAM灰度图像表示方法研究

非对称逆布局模型(NAM)适用于图像模式、语音模式、文本模式、视频模式的表示,是一个通用的模式表示模型。图像表示是计算机图形学、计算机视觉、机器人、图像处理和模式识别等领域里的一个重要问题。借助于三角形和正方形Packing问题的思想,本文提出了一种基于三角形和正方形的NAM灰度图像表示方法,简称NAMTS表示方法。给出了灰度图像的NAMTS表示算法,并对其存储结构和总数据量进行了详细分析。理论分析和实验结果表明:与经典的基于线性四元树的表示方法相比,基于三角形和正方形的NAM灰度图像表示方法能更有效地减少子模式数和数据存储空间,是灰度图像的一种良好的表示方法。     

矩形NAM图像表示及其上欧拉数计算

更少的数据量和更快速的运算是图像表示方法所追求的目标,传统的图像层次结构因为强调对称性而不能得到最优分割,非对称逆布局模式表示模型（Non-Symmetry and Anti-Packing Pattern Representation Model,NAM）是借鉴Packing问题的思想提出的一种图像表示方法,它的非对称层次结构使其与传统的层次结构（如四元树）相比需要更少的数据量,而采用的基于像素块的运算方式使它在进行一些图像处理运算时速度要优于基于像素点的运算方式,采用矩形子模式的矩形NAM图像表示和基于它的欧拉数计算很好的证明了这一点.     

一种新的TRNAM彩色图像表示算法

非对称逆布局模型(NAM)适用于图像模式、语音模式、文本模式、视频模式的表示,是一个通用型的模式表示模型.借助于格雷码表示图像像素的思想,提出了一种新的三角形和矩形NAM彩色图像表示算法(简称NTRNAM算法).给出了算法的原理及形式化描述,并对其存储结构、总数据量和时空复杂性进行了分析.理论分析和实验结果表明:NTRNAM算法能够显著降低子模式数和存储空间,是一种有效的彩色图像表示算法.     

改进的三角形和矩形NAM的二值图像表示方法

图像表示是计算机图形学、计算机视觉、机器人、图像处理和模式识别等领域里的一个重要问题.提出一种改进的三角形和矩形NAM的二值图像(NAMTR)表示方法,简称INAMTR表示方法.给出了算法的形式化描述,并对其存储结构、总数据量和时空复杂性进行了详细的分析.以图像处理领域里惯用的标准′Lena′、′Baboon′和′Peppers′等二值图像作为典型测试对象,理论分析和实验结果均表明:与目前新提出的NAMTR表示方法和经典的线性四元树表示方法相比,INAMTR表示方法具有更少的子模式数(或节点数),能够更有效地减少数据存储空间,是二值图像模式的一种更优的表示方法.     

A Tensor Approximation Approach to Dimensionality Reduction

Dimensionality reduction has recently been extensively studied for computer vision applications. We present a novel multilinear algebra based approach to reduced dimensionality representation of multidimensional data, such as image ensembles, video sequences and volume data. Before reducing the dimensionality we do not convert it into a vector as is done by traditional dimensionality reduction techniques like PCA. Our approach works directly on the multidimensional form of the data (matrix in 2D and tensor in higher dimensions) to yield what we call a Datum-as-Is representation. This helps exploit spatio-temporal redundancies with less information loss than image-as-vector methods. An efficient rank-R tensor approximation algorithm is presented to approximate higher-order tensors. We show that rank-R tensor approximation using Datum-as-Is representation generalizes many existing approaches that use image-as-matrix representation, such as generalized low rank approximation of matrices (GLRAM) (Ye, Y. in Mach. Learn. 61:167–191, 2005), rank-one decomposition of matrices (RODM) (Shashua, A., Levin, A. in CVPR’01: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, p. 42, 2001) and rank-one decomposition of tensors (RODT) (Wang, H., Ahuja, N. in ICPR ’04: ICPR ’04: Proceedings of the 17th international conference on pattern recognition (ICPR’04), vol. 1, pp. 44–47, 2004). Our approach yields the most compact data representation among all known image-as-matrix methods. In addition, we propose another rank-R tensor approximation algorithm based on slice projection of third-order tensors, which needs fewer iterations for convergence for the important special case of 2D image ensembles, e.g., video. We evaluated the performance of our approach vs. other approaches on a number of datasets with the following two main results. First, for a fixed compression ratio, the proposed algorithm yields the best representation of image ensembles visually as well as in the least squares sense. Second, proposed representation gives the best performance for object classification. A shorter version of this paper was published at IEEE CVPR 2005 (Wang and Ahuja 2005).     

Pulsed-Field Gel Elec rophoresis Pa ern Recogni ion of Bac erial DNA: A Systemic Approach

Pulsed-Field Gel Electrophoresis (PFGE) images contain ‘banding pattern’ information that can be used in bacterial identification.A systemic approach was designed to target the bacterial DNA PFGE pattern recognition problem that requires image processing, feature extraction, data pre-processing, classification and final decision-making. This paper describes the research, development and testing of a complete system to address this identification problem, including Receiver Operating Characteristic Curve (ROC) analysis. Variations due to gel preparation in various laboratories by different technicians are considered. The approach was applied to recognition of E. coli O157:H7 bacteria with excellent results.     

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.     

A new non-symmetry and anti-packing model and its application to image contrast enhancement

In this paper, a non-symmetry and anti-packing image representation model (NAM) has been proposed. NAM is a hierarchical image representation method and it aims to provide faster operations and less storage requirement. By taking a rectangle sub-pattern, for example, we describe the idea of NAM and its encoding algorithm.In addition, an approach for adaptive area histogram equalization for image contrast enhancement based on a NAM image is presented. The contrast enhancement approach is designed to meet the NAM image representation and it can be duplicated with faster operation. The complexity analysis and the experimental results show that the NAM based algorithm for image contrast enhancement is faster and more effective than that based on matrix image.     

DSM as a knowledge capture tool in CODE environment

A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system/ process. This paper shows how DSM, a system engineering tool, is applied as a knowledge capture (acquisition) tool in a generic NPD process. The acquired knowledge (identified in the DSM) is provided in the form of Questionnaires, which are organized into five performance indicators of the organization namely ‘Marketing’, ‘Technical’, ‘Financial’, ‘Resource Management’, and ‘Project Management’. Industrial application is carried out for knowledge validation. It is found form the application that the acquired knowledge helps NPD teams, managers and stakeholders to benchmark their NPD endeavor and select areas to focus their improvement efforts (up to 80% valid).     

Handwritten signature verification based on code representation

A method to obtain a code representation of handwritten signatures is described and an algorithm for signature verification based on such representations is proposed. Results of tests to determine efficient methods of image compression for the purpose of signature verification are presented. Konstantin Alekseev. Born 1979. Received Master’s degree in engineering and technology (Radioengineering) in 2002. Currently post-graduate student at St. Petersburg State Electrotechnical University “LETI”, chair of television and video. Scientific interests: digital image processing and pattern recognition. Author of three papers. Svetlana Egorova. Born 1931. Graduated from St. Petersburg State Electrotechnical University “LETI” in 1955, received Candidates degree (Eng.) in 1965; since 1968 a senior lecturer at the chair of television and video, St. Petersburg State Electrotechnical University “LETI”. Scientific interests: optical and digital image processing and compression methods in signal processing. Author of 141 papers.     

Fast Computation of Moments on Compressed Grey Images using Block Representation

In image processing, moments are useful tools for analyzing shapes. Suppose that the input grey image with size N×N has been compressed into the compressed image using the block representation, where the number of blocks used is K, commonly K<N² due to the compression effect. This paper presents an efficient O (N√K)-time algorithm for computing moments on the compressed image directly. Experimental results reveal a significant computational advantage of the proposed algorithm, while preserving a high accuracy of moments and a good compression ratio. The results of this paper extend the previous results in [7] from the binary image domain to the grey image domain.     

基于格雷码的NAM彩色图像表示方法

提出了一个重要定理,即所有格雷码(Cray Codc)位面图的复杂性之和小于所有二值位面图的复杂性之和,并将格雷码应用到基于NAM的彩色图像表示方法中,提出了一种基于格雷码的NAM彩色图像表示方法(简称为GNAM表示方法)。给出了GNAM表示算法的形式化描述,并对其存储结构、总数据量和时空复杂性进行了详细的分析。理论分析和实验结果均表明,与无格雷码的NAM表示方法和经典的线性四元树表示方法相比,GNAM表示方法具有更少的子模式数(或节点数),能够更有效地减少数据存储空间,是一种有效的彩色图像表示方法。     

Ring to Line Mapping and Orientation Invariant Transform for Object Recognition

This research uses the ring to line mapping technique to map the object image to the straight-line signals. The ‘vector magnitude invariant transform’ technique is used to transfer the object signal to an invariant vector magnitude quantity for object-identification. The ‘vector magnitude invariant transform’ technique can solve the image rotation problem. Various vertical magnitude quantity strips are generated to cope with the image-shifting problem. In this research, 105 comparisons are conducted to find the accuracy-rate of the developed algorithm. Within those 105 comparisons, 15 comparisons are conducted for self-comparison. The other 90 comparisons are conducted for comparisons between two different object images. The algorithm developed in this research can precisely classify the object image.     

A framework for traversing dense annotation lattices

Pattern matching, or querying, over annotations is a general purpose paradigm for inspecting, navigating, mining, and transforming annotation repositories—the common representation basis for modern pipelined text processing architectures. The open-ended nature of these architectures and expressiveness of feature structure-based annotation schemes account for the natural tendency of such annotation repositories to become very dense, as multiple levels of analysis get encoded as layered annotations. This particular characteristic presents challenges for the design of a pattern matching framework capable of interpreting ‘flat’ patterns over arbitrarily dense annotation lattices. We present an approach where a finite state device applies (compiled) pattern grammars over what is, in effect, a linearized ‘projection’ of a particular route through the lattice. The route is derived by a mix of static grammar analysis and runtime interpretation of navigational directives within an extended grammar formalism; it selects just the annotations sequence appropriate for the patterns at hand. For expressive and efficient pattern matching in dense annotations stores, our implemented approach achieves a mix of lattice traversal and finite state scanning by exposing a language which, to its user, provides constructs for specifying sequential, structural, and configurational constraints among annotations.     

矩形NAM图像表示及其上的连通区域标记算法

既能减少数据量又能直接快速地进行运算是图像表示方法所追求的目标。本文为克服传统的图像层次结构限制条件过多的缺陷，在借鉴Packing问题的思想的基础上，提出了非对称逆布局模式表示模型（Non-Symmetry and Anti-Packing Pattern Representation Model，NAM）。NAM模型的非对称层次结构使其在表示一幅图像时没有过多的限制条件，因此可以获得更高的压缩比，而且它可以直接进行某些图像处理运算，其基于像素块的运算方式使它的运算效率更高，矩形NAM图像表示和基于它的连通区域标记算法证明了这一点。     

Accurate and speedy computation of image Legendre moments for computer vision applications

A novel algorithm that permits the fast and accurate computation of the Legendre image moments is introduced in this paper. The proposed algorithm is based on the block representation of an image and on a new image representation scheme, the Image Slice Representation (ISR) method. The ISR method decomposes a gray-scale image as an expansion of several two-level images of different intensities (slices) and thus enables the partial application of the well-known Image Block Representation (IBR) algorithm to each image component. Moreover, using the resulted set of image blocks, the Legendre moments’ computation can be accelerated through appropriate computation schemes. Extensive experiments prove that the proposed methodology exhibits high efficiency in calculating Legendre moments on gray-scale, but furthermore on binary images. The newly introduced algorithm is suitable for the computation of the Legendre moments for pattern recognition and computer vision applications, where the images consist of objects presented in a scene.     

一种基于非对称逆布局模型的彩色图像表示方法

借助于Packing问题的思想,提出了一种基于非对称逆布局的模式表示模型(non-symmetry and anti- packing pattern representation model,简称NAM)的彩色图像表示方法.通过描述NAM和彩色图像的二进制位平面分解(binary-bit plane decomposition,简称BPD)方法,给出了一种全新的基于NAM的彩色图像表示算法,并对算法的总数据量进行了分析.理论分析和实验结果均表明,与流行的基于分层结构的线性四元树的彩色图像表示方法相比,基于NAM的表示方法能够更有效地减少数据存储空间,是彩色图像模式表示的一种良好方法.这种方法可以应用于彩色图像模式表示的各个方面,在降低存储空间、提高传输速度、加快处理过程、模式匹配等方面具有良好的理论参考意义和实际应用价值.     

Impact of object extraction methods on classification performance in surface inspection systems

In surface inspection applications, the main goal is to detect all areas which might contain defects or unacceptable imperfections, and to classify either every single ‘suspicious’ region or the investigated part as a whole. After an image is acquired by the machine vision hardware, all pixels that deviate from a pre-defined ‘ideal’ master image are set to a non-zero value, depending on the magnitude of deviation. This procedure leads to so-called “contrast images”, in which accumulations of bright pixels may appear, representing potentially defective areas. In this paper, various methods are presented for grouping these bright pixels together into meaningful objects, ranging from classical image processing techniques to machine-learning-based clustering approaches. One important issue here is to find reasonable groupings even for non-connected and widespread objects. In general, these objects correspond either to real faults or to pseudo-errors that do not affect the surface quality at all. The impact of different extraction methods on the accuracy of image classifiers will be studied. The classifiers are trained with feature vectors calculated for the extracted objects found in images labeled by the user and showing surfaces of production items. In our investigation artificially created contrast images will be considered as well as real ones recorded on-line at a CD imprint production and at an egg inspection system.     

Parameterized real-time moment computation on gray images using block techniques

Moments constitute a well-known tool in the field of image analysis and pattern recognition, but they suffer from the drawback of high computational cost. Efforts for the reduction of the required computational complexity have been reported, mainly focused on binary images, but recently some approaches for gray images have been also presented. In this study, we propose a simple but effective approach for the computation of gray image moments. The gray image is decomposed in a set of binary images. Some of these binary images are substituted by an ideal image, which is called “half-intensity” image. The remaining binary images are represented using the image block representation concept and their moments are computed fast using block techniques. The proposed method computes approximated moment values with an error of 2–3% from the exact values and operates in real time (i.e., video rate). The procedure is parameterized by the number m of “half-intensity” images used, which controls the approximation error and the speed gain of the method. The computational complexity is O(kL ²), where k is the number of blocks and L is the moment order.