Rough Set Based Image Texture Recognition Algorithm

2004 ◽  
pp. 772-778 ◽  
Author(s):  
Zheng Zheng ◽  
Hong Hu ◽  
Zhongzhi Shi
Keyword(s):  
Rough Set ◽  
Recognition Algorithm ◽  
Image Texture ◽  
Texture Recognition

A Rock Cracks Dynamic Recognition Algorithm Based on Fingerprint

2012 ◽  
Vol 466-467 ◽  
pp. 176-180
Author(s):  
Ling Ling Xiao ◽  
Yan Ting Liu
Keyword(s):  
Labor Force ◽  
Rock Fracture ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Texture Recognition ◽  
Detection Technology ◽  
Dynamic Recognition ◽  
Rock Texture

Traditional detection technology of rock cracks in mine is asking somebody to go deep into the mine and detect them. This method doesn’t only waste so much labor force, but also lose timeliness. It will bring amount of risk to the workers and the results will contain subjectivity. As complex texture of rock is similar to the fingerprint, we give a rock fracture recognition algorithm based on fingerprint. In this paper, the fingerprint recognition algorithm is introduced into the rock texture recognition, and to be improved by us. We demonstrate the algorithm is feasibility with experiments.

scholarly journals A Study of Digit Recognition Algorithm for Meter based on Rough Set and Neural Network

2016 ◽  
Vol 10 (8) ◽  
pp. 155-168
Author(s):  
Xiaochen Zhang ◽  
Yuanchang Zhong ◽  
Jiajia Shen ◽  
Kun Li ◽  
Congjun Feng
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Recognition Algorithm ◽  
Digit Recognition

Content-Based Image Classification and Retrieval

2011 ◽  
pp. 68-83 ◽  
Author(s):  
Jafar M. Ali
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Data Storage ◽  
Rough Set ◽  
Texture Features ◽  
Feature Reduction ◽  
Image Texture ◽  
Content Based Image Retrieval ◽  
Attribute Vector ◽  
Real Value

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop appropriate information systems to efficiently manage these datasets. Image classification and retrieval is one of the most important services that must be supported by such systems. The most common approach used is content-based image retrieval (CBIR) systems. This paper presents a new application of rough sets to feature reduction, classification, and retrieval for image databases in the framework of content-based image retrieval systems. The suggested approach combines image texture features with color features to form a powerful discriminating feature vector for each image. Texture features are extracted, represented, and normalized in an attribute vector, followed by a generation of rough set dependency rules from the real value attribute vector. The rough set reduction technique is applied to find all reducts with the minimal subset of attributes associated with a class label for classification.

CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE

2005 ◽  
Vol 19 (04) ◽  
pp. 551-564
Author(s):  
YING SHAN ◽  
HARPREET S. SAWHNEY ◽  
ART POPE
Keyword(s):  
Similarity Measure ◽  
Selection Process ◽  
Sequence Similarity ◽  
Image Sequences ◽  
Image Texture ◽  
Sequence Matching ◽  
Matching Problem ◽  
Texture Recognition ◽  
Background Clutter ◽  
Similar Pair

We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-to-sequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.

scholarly journals DYNAMIC TEXTURE RECOGNITION ALGORITHM

2018 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Anna Vladimirovna Pyataeva ◽  
Ksenia Vladislavovna Raevich
Keyword(s):  
Recognition Algorithm ◽  
Dynamic Texture ◽  
Texture Recognition

Постановка проблемы: Обнаружение динамических текстур на видеоизображениях в настоящее время находит все более широкое применение в системах компьютерного зрения. Например, обнаружение дыма и пламени в системах экологического мониторинга, анализ автомобильного трафика при мониторинге загруженности дорог, и в других системах. Поиск объекта интереса на динамическом фоне часто бывает затруднен за счет похожих текстурных признаков или признаков движения у фона и искомого объекта. В связи с этим возникает необходимость разработки алгоритма классификации динамических текстур для выделения объектов интереса на динамическом фоне.Методы: распознавание образов, компьютерное зрение.Результаты: В данной работе рассматривается обработка видеоизображений содержащих объекты с динамическим поведением на динамическом фоне, такие как вода, туман, пламя, текстиль на ветру и др. Разработан алгоритм отнесения объектов видеоизображения к одной из четырех предлагаемых категорий. Извлекаются признаки движения, цветовые особенности, фрактальности, энергетические признаки Ласа, строятся ELBP-гистограммы. В качестве классификатора использован бустинговый случайный лес.Практическая значимость: Разработан метод, позволяющий разделить динамические текстур на категории: по типу движения (периодическое и хаотичное) и типу объектов интереса (природные и искусственные). Экспериментальные исследования подтверждают эффективность предложенного алгоритма для отнесения объектов изображения к той или иной категории. Средняя точность классификации составила 95.2%.

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