An Overview of Advances of Pattern Recognition Systems in Computer Vision

Kpalma, Kidiyo; Ronsin, Joseph

doi:10.5772/4960

Cited by 20 publications

(10 citation statements)

References 49 publications

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“…A very interesting overview of the advances of pattern recognition systems in Computer Vision is presented in reference 6 . A classification of shape description approaches in two great categories, region based shape description and contour based shape description are illustrated and detailed ( fig.2).…”

Section: Approaches In Invariant Pattern Recognitionmentioning

confidence: 99%

The recognition of graphical patterns invariant to geometrical transformation of the models

et al. 2010

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In case that a pattern recognition system is used for images recognition (in robot vision, handwritten recognition etc.), the system must have the capacity to identify an object indifferently of its size or position in the image. The problem of the invariance of recognition can be approached in some fundamental modes. One may apply the similarity criterion used in associative recall. The original pattern is replaced by a mathematical transform that assures some invariance (e.g. the value of two-dimensional Fourier transformation is translation invariant, the value of Mellin transformation is scale invariant). In a different approach the original pattern is represented through a set of features, each of them being coded indifferently of the position, orientation or position of the pattern. Generally speaking, it is easy to obtain invariance in relation with one transformation group, but is difficult to obtain simultaneous invariance at rotation, translation and scale. In this paper we analyze some methods to achieve invariant recognition of images, particularly for digit images. A great number of experiments are due and the conclusions are underplayed in the paper.

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Section: Approaches In Invariant Pattern Recognitionmentioning

confidence: 99%

The recognition of graphical patterns invariant to geometrical transformation of the models

et al. 2010

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“…In this approach, pattern is represented as a hierarchical structure composed of substructures [33,48]. All patterns related to one class contains same structural properties.…”

Section: Syntactic or Structural Approachmentioning

confidence: 99%

“…Examples of the patterns are face image, speech signal, bar code, fingerprint image, a word etc. Observing and extracting multiple features is a less complex task (in some cases it is obvious) for humans whereas for machines it is much more complex [33]. The pattern recognition is a science of Observing and extracting the patterns by machines.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Modeling for Emotion Recognition

Anne¹,

Kuchibhotla²,

Vankayalapati³

2015

SpringerBriefs in Electrical and Computer Engineering

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SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical topics might include: timely report of state-of-the art analytical techniques, a bridge between new research results, as published in journal articles, and a contextual literature review, a snapshot of a hot or emerging topic, an in-depth case study or clinical example and a presentation of core concepts that students must understand in order to make independent contributions.More information about this series at

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“…There are many techniques available in the classification method that can be applied. The classification method can be traced from template matching [23][24][25], statistical approach [26][27][28], syntactic [29] and neural network [30].…”

Section: Classificationmentioning

confidence: 99%

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

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Abstract-The development of handwriting character recognition (HCR) is an interesting area in pattern recognition.HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

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An Overview of Advances of Pattern Recognition Systems in Computer Vision

Cited by 20 publications

References 49 publications

The recognition of graphical patterns invariant to geometrical transformation of the models

The recognition of graphical patterns invariant to geometrical transformation of the models

Acoustic Modeling for Emotion Recognition

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

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