The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

On-line construction of suffix trees

/pdf/template-matching-techniques-in-computer-vision-433exjz3wi.pdf

Template Matching Techniques in Computer Vision

Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words.

/pdf/understanding-plagiarism-linguistic-patterns-textual-1ysubwdzsv.pdf

Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods

Tutorial presented at High Performance Computing Center, Stuttgart. 1 July 2005, Stuttgart, Germany.

Introduction to computational biology

We present a new algorithm for multiple approximate string matching. It is based on reading backwards enough l-grams from text windows so as to prove that no occurrence can contain the part of the window read, and then shifting the window.We show analytically that our algorithm is optimal on average. Hence our first contribution is to fill an important gap in the area, since no average-optimal algorithm existed for multiple approximate string matching.We consider several variants and practical improvements to our algorithm, and show experimentally that they are resistant to the number of patterns and the fastest for low difference ratios, displacing the long-standing best algorithms. Hence our second contribution is to give a practical algorithm for this problem, by far better than any existing alternative in many cases of interest. On real-life texts, our algorithm is especially interesting for computational biology applications.In particular, we show that our algorithm can be successfully used to search for one pattern, where many more competing algorithms exist. Our algorithm is also average-optimal in this case, being the second after that of Chang and Marr. However, our algorithm permits higher difference ratios than Chang and Marr, and this is our third contribution.In practice, our algorithm is competitive in this scenario too, being the fastest for low difference ratios and moderate alphabet sizes. This is our fourth contribution, which also answers affirmatively the question of whether a practical average-optimal approximate string-matching algorithm existed.

/pdf/average-optimal-single-and-multiple-approximate-string-i8owg92dqf.pdf

Average-optimal single and multiple approximate string matching

We develop a new exact bit-parallel string matching algorithm, based on the Shift-Or algorithm (Baeza-Yates & Gonnet, 1992). Assuming that the pattern representation fits into a single computer word, this algorithm has optimal O(n logσm / m) average running time, as well as optimal O(n) worst case running time, where n, m and σ are the sizes of the text, the pattern, and the alphabet, respectively. We also study several implementation details. The experimental results show that our algorithm is the fastest in most of the cases where it can be applied, displacing even the long-standing BNDM (Navarro & Raffinot, 2000) family of algorithms. Finally, we show how to adapt our techniques for the Shift-Add algorithm (Baeza-Yates & Gonnet, 1992), obtaining optimal time for searching under Hamming distance.

Practical and optimal string matching

Efficient parameterized string matching

The large class sizes typical for an undergraduate programming course mean that it is nearly impossible for a human marker to accurately detect plagiarism, particularly if some attempt has been made to hide the copying. While it would be desirable to be able to detect all possible code transformations we believe that there is a minimum level of acceptable performance for the application of detecting student plagiarism. It would be useful if the detector operated at a level that meant for a piece of work to fool the algorithm would require that the student spent a large amount of time on the assignment and had a good enough understanding to do the work without plagiarising.

https://web-ext.u-aizu.ac.jp/~mozgovoy/homepage/papers/mfwjs05.pdf

Kimmo Fredriksson

Papers

Average-optimal single and multiple approximate string matching

Practical and optimal string matching

Efficient parameterized string matching

Fast plagiarism detection system

Shift-or string matching with super-alphabets