Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods. (C) 2011 Elsevier Ltd. All rights reserved.

Bilayer segmentation of live video in uncontrolled environments for background substitution: an overview and main challenges

Bilayer segmentation of live video in uncontrolled environments is an essential task for home applications in which the original background of the scene must be replaced, as in videochats or traditional videoconference. The main challenge in such conditions is overcome all difficulties in problem-situations (e. g., illumination change, distract events such as element moving in the background and camera shake) that may occur while the video is being captured. This paper presents a survey of segmentation methods for background substitution applications, describes the main concepts and identifies events that may cause errors. Our analysis shows that although robust methods rely on specific devices (multiple cameras or sensors to generate depth maps) which aid the process. In order to achieve the same results using conventional devices (monocular video cameras), most current research relies on energy minimization frameworks, in which temporal and spacial information are probabilistically combined with those of color and contrast.

Stripe based clothes segmentation

[EN]In this paper, a clothes segmentation method for fashion parsing is described. This method does not rely in a previous pose estimation but people segmentation. Therefore, novel and classic segmentation techniques have been considered and improved in order to achieve accurate people segmentation. Unlike other methods described in the literature, the output is the bounding box and the predominant color of the different clothes and not a pixel level segmentation. The proposal is based on dividing the person area into an initial fixed number of stripes, that are later fused according to similar color distribution. To assess the quality of the proposed method the experiments are carried out with the Fashionista dataset that is widely used in the fashion parsing community.

A Comparative Evaluation of Three Skin Color Detection Approaches

Skin segmentation is a challenging task due to several influences such as unknown lighting conditions, skin colored background, and camera limitations. A lot of skin segmentation approaches were proposed in the past including adaptive (in the sense of updating the skin color online) and non-adaptive approaches. In this paper, we compare three skin segmentation approaches that are promising to work well for hand tracking, which is our main motivation for this work. Hand tracking can widely be used in VR/AR e.g. navigation and object manipulation. The first skin segmentation approach is a well-known non-adaptive approach. It is based on a simple, pre-computed skin color distribution. Methods two and three adaptively estimate the skin color in each frame utilizing clustering algorithms. The second approach uses a hierarchical clustering for a simultaneous image and color space segmentation, while the third approach is a pure color space clustering, but with a more sophisticated clustering approach. For evaluation, we compared the segmentation results of the approaches against a ground truth dataset. To obtain the ground truth dataset, we labeled about 500 images captured under various conditions.

Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting

In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process.

Desarrollo de métodos de procesado de imágenes laríngeas en color para la detección del espacio glotal y de las cuerdas vocales

La medicina ha evolucionado de forma que las imágenes digitales tienen un papel de gran relevancia para llevar a cabo el diagnóstico de enfermedades. Son muchos y de diversa naturaleza los problemas que pueden presentar el aparato fonador. Un paso previo para la caracterización de imágenes digitales de la laringe es la segmentación de las cuerdas vocales. Hasta el momento se han desarrollado algoritmos que permiten la segmentación de la glotis. El presente proyecto pretende avanzar un paso más en el estudio, procurando asimismo la segmentación de las cuerdas vocales. Para ello, es necesario aprovechar la información de color que ofrecen las imágenes, pues es lo que va a determinar la diferencia entre una región y otra de la imagen. En este proyecto se ha desarrollado un novedoso método de segmentación de imágenes en color estroboscópicas de la laringe basado en el crecimiento de regiones a partir de píxeles-semilla. Debido a los problemas que presentan las imágenes obtenidas por la técnica de la estroboscopia, para conseguir óptimos resultados de la segmentación es necesario someter a las imágenes a un preprocesado, que consiste en la eliminación de altos brillos y aplicación de un filtro de difusión anisotrópica. Tras el preprocesado, comienza el crecimiento de la región a partir de unas semillas que se obtienen previamente. La condición de inclusión de un píxel en la región se basa en un parámetro de tolerancia que se determina de forma adaptativa. Este parámetro comienza teniendo un valor muy bajo y va aumentando de forma recursiva hasta alcanzar una condición de parada. Esta condición se basa en el análisis de la distribución estadística de los píxeles dentro de la región que va creciendo. La última fase del proyecto consiste en la realización de las pruebas necesarias para verificar el funcionamiento del sistema diseñado, obteniéndose buenos resultados en la segmentación de la glotis y resultados esperanzadores para seguir mejorando el sistema para la segmentación de las cuerdas vocales. ABSTRACT Medicine has evolved so that digital images have a very important role to perform disease diagnosis. There are wide variety of problems that can present the vocal apparatus. A preliminary step for characterization of digital images of the larynx is the segmentation of the vocal folds. To date, some algorithms that allow the segmentation of the glottis have been developed. This project aims to go one step further in the study, also seeking the segmentation of the vocal folds. To do this, we must use the color information offered by images, since this is what will determine the difference between different regions in a picture. In this project a novel method of larynx color images segmentation based on region growing from a pixel seed is developed. Due to the problems of the images obtained by the technique of stroboscopy, to achieve optimal results of the segmentation is necessary a preprocessing of the images, which involves the removal of high brightness and applying an anisotropic diffusion filter. After this preprocessing, the growth of the region from previously obtained seeds starts. The condition for inclusion of a pixel in the region is based on a tolerance parameter, which is adaptively determined. It initially has a low value and this is recursively increased until a stop condition is reached. This condition is based on the analysis of the statistical distribution of the pixels within the grown region. The last phase of the project involves the necessary tests to verify the proper working of the designed system, obtaining very good results in the segmentation of the glottis and encouraging results to keep improving the system for the segmentation of the vocal folds.

Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches.

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

Methods of Color Images Procession for Further Identification of the Object

The offered paper deals with the problems of color images preliminary procession. Among these are: interference control (local ones and noise) and extraction of the object from the background on the stage preceding the process of contours extraction. It was considered for a long time that execution of smoothing in segmentation through the boundary extraction is inadmissible, but the described methods and the obtained results evidence about expedience of using the noise control methods.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Superpixel-based roughness measure for multispectral satellite image segmentation

The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.

Preference Mapping Of Dulce De Leche Commercialized In Brazilian Markets.

Dulce de leche samples available in the Brazilian market were submitted to sensory profiling by quantitative descriptive analysis and acceptance test, as well sensory evaluation using the just-about-right scale and purchase intent. External preference mapping and the ideal sensory characteristics of dulce de leche were determined. The results were also evaluated by principal component analysis, hierarchical cluster analysis, partial least squares regression, artificial neural networks, and logistic regression. Overall, significant product acceptance was related to intermediate scores of the sensory attributes in the descriptive test, and this trend was observed even after consumer segmentation. The results obtained by sensometric techniques showed that optimizing an ideal dulce de leche from the sensory standpoint is a multidimensional process, with necessary adjustments on the appearance, aroma, taste, and texture attributes of the product for better consumer acceptance and purchase. The optimum dulce de leche was characterized by high scores for the attributes sweet taste, caramel taste, brightness, color, and caramel aroma in accordance with the preference mapping findings. In industrial terms, this means changing the parameters used in the thermal treatment and quantitative changes in the ingredients used in formulations.

Desenvolvimento de uma metodologia para reconstrução da superficie do pe humano por videogrametria

Universidade Estadual de Campinas . Faculdade de Educação Física

Effect of staining solutions and repolishing on color stability of direct composites

OBJECTIVES: The purpose of this study was to assess the color change of three types of composite resins exposed to coffee and cola drink, and the effect of repolishing on the color stability of these composites after staining. MATERIALS AND METHODS: Fifteen specimens (15 mm diameter and 2 mm thick) were fabricated from microhybrid (Esthet-X; Dentsply and Filtek Z-250; 3M ESPE) and high-density hybrid (Surefil; Dentsply) composites, and were finished and polished with aluminum oxide discs (Sof-Lex; 3M ESPE). Color of the specimens was measured according to the CIE L*a*b* system in a refection spectrophotometer (PCB 6807; BYK Gardner). After baseline color measurements, 5 specimens of each resin were immersed in different staining solutions for 15 days: G1 - distilled water (control), G2 - coffee, G3 - cola soft drink. Afterwards, new color measurement was performed and the specimens were repolished and submitted to new color reading. Color stability was determined by the difference (ΔE) between the coordinates L*, a*, and b* obtained from the specimens before and after immersion into the solutions and after repolishing. RESULTS: There was no statistically signifcant difference (ANOVA, Tukey's test; p>0.05) among the ΔE values for the different types of composites after staining or repolishing. For all composite resins, coffee promoted more color change (ΔE>3.3) than distilled water and the cola soft drink. After repolishing, the ΔE values of the specimens immersed in coffee decreased to clinically acceptable values (ΔE<3.3), but remained signifcantly higher than those of the other groups. CONCLUSIONS: No signifcant difference was found among composite resins or between color values before and after repolishing of specimens immersed in distilled water and cola. Immersing specimens in coffee caused greater color change in all types of composite resins tested in this study and repolishing contributed to decrease staining to clinically acceptable ΔE values.