14 research outputs found

    Integration of feature distributions for colour texture segmentation

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    This paper proposes a new framework for colour texture segmentation and determines the contribution of colour and texture. The distributions of colour and texture features provides the discrimination between different colour textured regions in an image. The proposed method was tested using different mosaic and natural images. From the results, it is evident that the incorporation of colour information enhanced the colour texture segmentation and the developed framework is effective

    Vision based surveillance system

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    Due to the numerous amounts of surveillance cameras available, security guards seem to be ubiquitously watching over. However, the number of existing cameras exceeds the number of humans to monitor them and the supervision of all the sensors' output is costly. Thus, video footage from cameras is most often only used as a forensic tool. This suggests the need of an intelligent video surveillance system providing continuous 24-hour monitoring, replacing the traditional ineffective systems. This paper presents an automated vision based surveillance system which is capable to detect and track humans and vehicles from a video footage. Simulation results have shown that the Object Classification module manages to achieve an accuracy of 97.31% and 97.14% for the person and vehicle classification respectively. Furthermore, the system manages to successfully track the objects 97% of the time under no occlusion and 94.14% in presence of occlusion.peer-reviewe

    Texture Segmentation using LBP embedded Region Competition

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    In this paper, we modify the region competition method to segment textures. First, local Binary pattern (LBP) histogram is adopted to capture the texture information. Then, considering the specific goal of texture segmentation, we propose new assumption about region competition and rewrite the energy function based on LBP histograms. We also develop the two-stage iterative algorithm to make our energy converge to a local minimum. Because of the fast LBP operator and nonparametric histogram model, we can simplify the step of parameter estimating, which is always the most time-consuming. Besides, LBP' s high performance for texture characterization helps to make our method more suitable for texture segmentation problem. Experiments show that the performance of our proposed method is promising, and a robust and fast segmentation of texture images is obtained

    Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

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    There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease

    Segmentation -Offset Based Image Classification

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    Abstract In industrial applications, product identification is the most common thing now days. To kept in mind that we focus on the classification of our industrial product with the help of its texture using segmentatio

    Vision based surveillance system

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    Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

    Get PDF
    There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease

    GENERALIZATION OF THE COOCCURRENCE MATRIX FOR COLOUR IMAGES: APPLICATION TO COLOUR TEXTURE CLASSIFICATION

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    ApplicabilitĂ© de la texture couleur Ă  la diffĂ©rentiation des classes d’occupation du territoire sur des images satellitales multispectrales

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    La texture est un Ă©lĂ©ment clĂ© pour l’interprĂ©tation des images de tĂ©lĂ©dĂ©tection Ă  fine rĂ©solution spatiale. L’intĂ©gration de l’information texturale dans un processus de classification automatisĂ©e des images se fait habituellement via des images de texture, souvent crĂ©Ă©es par le calcul de matrices de co-occurrences (MCO) des niveaux de gris. Une MCO est un histogramme des frĂ©quences d’occurrence des paires de valeurs de pixels prĂ©sentes dans les fenĂȘtres locales, associĂ©es Ă  tous les pixels de l’image utilisĂ©e; une paire de pixels Ă©tant dĂ©finie selon un pas et une orientation donnĂ©s. Les MCO permettent le calcul de plus d’une dizaine de paramĂštres dĂ©crivant, de diverses maniĂšres, la distribution des frĂ©quences, crĂ©ant ainsi autant d’images texturales distinctes. L’approche de mesure des textures par MCO a Ă©tĂ© appliquĂ©e principalement sur des images de tĂ©lĂ©dĂ©tection monochromes (ex. images panchromatiques, images radar monofrĂ©quence et monopolarisation). En imagerie multispectrale, une unique bande spectrale, parmi celles disponibles, est habituellement choisie pour gĂ©nĂ©rer des images de texture. La question que nous avons posĂ©e dans cette recherche concerne justement cette utilisation restreinte de l’information texturale dans le cas des images multispectrales. En fait, l’effet visuel d’une texture est crĂ©Ă©, non seulement par l’agencement particulier d’objets/pixels de brillance diffĂ©rente, mais aussi de couleur diffĂ©rente. Plusieurs façons sont proposĂ©es dans la littĂ©rature pour introduire cette idĂ©e de la texture Ă  plusieurs dimensions. Parmi celles-ci, deux en particulier nous ont intĂ©ressĂ©s dans cette recherche. La premiĂšre façon fait appel aux MCO calculĂ©es bande par bande spectrale et la seconde utilise les MCO gĂ©nĂ©ralisĂ©es impliquant deux bandes spectrales Ă  la fois. Dans ce dernier cas, le procĂ©dĂ© consiste en le calcul des frĂ©quences d’occurrence des paires de valeurs dans deux bandes spectrales diffĂ©rentes. Cela permet, en un seul traitement, la prise en compte dans une large mesure de la « couleur » des Ă©lĂ©ments de texture. Ces deux approches font partie des techniques dites intĂ©gratives. Pour les distinguer, nous les avons appelĂ©es dans cet ouvrage respectivement « textures grises » et « textures couleurs ». Notre recherche se prĂ©sente donc comme une analyse comparative des possibilitĂ©s offertes par l’application de ces deux types de signatures texturales dans le cas spĂ©cifique d’une cartographie automatisĂ©e des occupations de sol Ă  partir d’une image multispectrale. Une signature texturale d’un objet ou d’une classe d’objets, par analogie aux signatures spectrales, est constituĂ©e d’une sĂ©rie de paramĂštres de texture mesurĂ©s sur une bande spectrale Ă  la fois (textures grises) ou une paire de bandes spectrales Ă  la fois (textures couleurs). Cette recherche visait non seulement Ă  comparer les deux approches intĂ©gratives, mais aussi Ă  identifier la composition des signatures texturales des classes d’occupation du sol favorisant leur diffĂ©rentiation : type de paramĂštres de texture / taille de la fenĂȘtre de calcul / bandes spectrales ou combinaisons de bandes spectrales. Pour ce faire, nous avons choisi un site Ă  l’intĂ©rieur du territoire de la CommunautĂ© MĂ©tropolitaine de MontrĂ©al (Longueuil) composĂ© d’une mosaĂŻque d’occupations du sol, caractĂ©ristique d’une zone semi urbaine (rĂ©sidentiel, industriel/commercial, boisĂ©s, agriculture, plans d’eau
). Une image du satellite SPOT-5 (4 bandes spectrales) de 10 m de rĂ©solution spatiale a Ă©tĂ© utilisĂ©e dans cette recherche. Puisqu’une infinitĂ© d’images de texture peuvent ĂȘtre crĂ©Ă©es en faisant varier les paramĂštres de calcul des MCO et afin de mieux circonscrire notre problĂšme nous avons dĂ©cidĂ©, en tenant compte des Ă©tudes publiĂ©es dans ce domaine : a) de faire varier la fenĂȘtre de calcul de 3*3 pixels Ă  21*21 pixels tout en fixant le pas et l’orientation pour former les paires de pixels Ă  (1,1), c'est-Ă -dire Ă  un pas d’un pixel et une orientation de 135°; b) de limiter les analyses des MCO Ă  huit paramĂštres de texture (contraste, corrĂ©lation, Ă©cart-type, Ă©nergie, entropie, homogĂ©nĂ©itĂ©, moyenne, probabilitĂ© maximale), qui sont tous calculables par la mĂ©thode rapide de Unser, une approximation des matrices de co-occurrences, c) de former les deux signatures texturales par le mĂȘme nombre d’élĂ©ments choisis d’aprĂšs une analyse de la sĂ©parabilitĂ© (distance de Bhattacharya) des classes d’occupation du sol; et d) d’analyser les rĂ©sultats de classification (matrices de confusion, exactitudes, coefficients Kappa) par maximum de vraisemblance pour conclure sur le potentiel des deux approches intĂ©gratives; les classes d’occupation du sol Ă  reconnaĂźtre Ă©taient : rĂ©sidentielle basse et haute densitĂ©, commerciale/industrielle, agricole, boisĂ©s, surfaces gazonnĂ©es (incluant les golfs) et plans d’eau. Nos principales conclusions sont les suivantes a) Ă  l’exception de la probabilitĂ© maximale, tous les autres paramĂštres de texture sont utiles dans la formation des signatures texturales; moyenne et Ă©cart type sont les plus utiles dans la formation des textures grises tandis que contraste et corrĂ©lation, dans le cas des textures couleurs, b) l’exactitude globale de la classification atteint un score acceptable (85%) seulement dans le cas des signatures texturales couleurs; c’est une amĂ©lioration importante par rapport aux classifications basĂ©es uniquement sur les signatures spectrales des classes d’occupation du sol dont le score est souvent situĂ© aux alentours de 75%; ce score est atteint avec des fenĂȘtres de calcul aux alentours de11*11 Ă  15*15 pixels; c) Les signatures texturales couleurs offrant des scores supĂ©rieurs Ă  ceux obtenus avec les signatures grises de 5% Ă  10%; et ce avec des petites fenĂȘtres de calcul (5*5, 7*7 et occasionnellement 9*9) d) Pour plusieurs classes d’occupation du sol prises individuellement, l’exactitude dĂ©passe les 90% pour les deux types de signatures texturales; e) une seule classe est mieux sĂ©parable du reste par les textures grises, celle de l’agricole; f) les classes crĂ©ant beaucoup de confusions, ce qui explique en grande partie le score global de la classification de 85%, sont les deux classes du rĂ©sidentiel (haute et basse densitĂ©). En conclusion, nous pouvons dire que l’approche intĂ©grative par textures couleurs d’une image multispectrale de 10 m de rĂ©solution spatiale offre un plus grand potentiel pour la cartographie des occupations du sol que l’approche intĂ©grative par textures grises. Pour plusieurs classes d’occupations du sol un gain apprĂ©ciable en temps de calcul des paramĂštres de texture peut ĂȘtre obtenu par l’utilisation des petites fenĂȘtres de traitement. Des amĂ©liorations importantes sont escomptĂ©es pour atteindre des exactitudes de classification de 90% et plus par l’utilisation des fenĂȘtres de calcul de taille variable adaptĂ©es Ă  chaque type d’occupation du sol. Une mĂ©thode de classification hiĂ©rarchique pourrait ĂȘtre alors utilisĂ©e afin de sĂ©parer les classes recherchĂ©es une Ă  la fois par rapport au reste au lieu d’une classification globale oĂč l’intĂ©gration des paramĂštres calculĂ©s avec des fenĂȘtres de taille variable conduirait inĂ©vitablement Ă  des confusions entre classes.Texture is a key element in interpreting remotely sensed images of fine spatial resolution. The integration of textural information in an automatic image-classification process is usually done via textural images, which are often created by calculating gray levels co-occurrences matrices (COM). A COM is a histogram of frequencies of pairs of pixel values present in local windows associated with all pixels in the used image; each pixel pair being formed using a given orientation and spacing. COM allows calculation for more than a dozen of parameters describing in various ways the frequency distribution, creating thus as many different textural images. Texture measurements approach based on COMs had been mainly applied on monochrome images (e.g. panchromatic, single polarisation and frequency radar images). In the case of multispectral images, a single spectral band, among those available, is usually chosen to generate texture images. The question we asked in this research concerns precisely this limited use of textural information in the case of multispectral images. In fact, the visual effect of a texture is created, not only by the spatial arrangement of variable objects/pixels brightness, but also of different colors. Several ways are suggested in the literature to introduce this concept of multi-dimensional texture. In this research, two of them were of particularly interested us. In the first way COMs are calculated spectral band by band and in the second one, generalized COMs are applied involving the joint use of two spectral bands. In the latter case, the pairs of pixel values are defined in two different spectral bands. This allows, in a single treatment, for a broad accounting of the "color" element composing a texture. These two approaches are called integrative techniques. To distinguish them, we call them respectively “gray textures” and “color textures”. Our research concerns the comparative analysis of the opportunities offered by applying these two types of textural signatures in the specific case of an automated land cover mapping using multispectral images. A textural signature of an object or class of objects, by analogy to spectral signatures, consists in a set of texture parameters measured; band by band (grey textures), or by pairs of bands (color textures).This research was designed not only to compare the two integrative approaches, but also to identify the components of textural signatures favouring the differentiation of land cover classes: texture parameters, window sizes and bands selection. To do this, a site within the territory of the Montreal Metropolitan Community (Longueuil) was chosen with a diversity of land covers representative of a semi-urban area. (residential, industrial / commercial, woodlots , agriculture, water bodies
). A SPOT-5 (4 spectral bands) image of 10m spatial resolution was used in this research. Since an infinite number of texture images can be created by varying the design parameters of COM, and to better define our problem, we have decided, taking into account studies published in this field: a) to vary the computation window from 3*3 to 21*21 pixels while setting the pixel spacing and direction to (1,1); that is to say, an spacing of 1 and an orientation of 135 ° between pairs of pixels. b) limit the COM analysis to eight texture parameters (contrast, correlation, standard deviation, energy, entropy, homogeneity, average, maximum probability), all of which are computable by the Unser’s fast-COM-approximation method, c) form the two textural signatures by the same number of elements chosen from a separability analysis (Bhattacharya distance) between land cover classes, and d) analyse the results (confusion matrices, accuracies, kappa) obtained using a maximum likelihood classification algorithm to conclude on the potential of both integrative approaches; classes to be recognized included: low and high density residential, commercial / industrial, agricultural, woodlots, turf (including golf) water bodies, clouds and their shadows. Our main conclusions are as follows a) except maximum probability, all other textural parameters are useful in the formation of textural signatures; mean and standard deviation are most useful in the formation of gray textures while contrast and correlation, are the best in the case of color textures b) the overall classification accuracy achieved an acceptable score (85%), only in the case of color textural signatures. This is a significant improvement compared to classifications based solely on spectral signatures, whose accuracies are often situated around 75%. This score is reached with windows size from about 11*11 to 15 * 15 pixels, c) Textural colors signatures offer higher scores, ranging from 5% to 10%, than those obtained by gray signatures. This is true while using the smaller process windows (5*5, 7*7, and occasionally 9*9) d) For several land cover classes examined individually, the accuracy is above 90% regardless of the used textural signatures e) Only one class is better separated from the rest by gray textures, the agricultural one; f) Classes creating a lot of confusion, which largely explains the overall classification score of 85 %, are the two residential classes (high and low density). As a final conclusion, we can say that the integrative approach by color textures provides a greater potential for mapping land covers using multispectral images than the integrative approach by gray textures. For several land cover classes an appreciable gain computing time of textural parameters may be obtained using smaller size windows. Significant improvements of the classification results (even better than 90%) are expected using calculation windows with sizes better adapted to each classes particular texture characteristics, A hierarchical classification method could then be used to separate each class at a time from all others, instead of a broad classification where the integration of parameters calculated with varying size windows, would inevitably lead to confusion between classes
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