Penerapan Metode Distance Transform pada Linear Discriminant Analysis untuk Kemunculan Kulit pada Deteksi Kulit

Deteksi kulit memainkan peranan penting dalam berbagai aplikasi pengolah citra, mulai dari deteksi wajah, pelacakan wajah, penyaringan konten pornografi, berdasarkan sistem pencarian citra dan berbagai domain interaksi manusia dan komputer. Pendekatan informasi warna dapat mendeteksi warna kulit dengan baik menggunakan skin probability map (SPM) dengan aturan bayes. Namun SPM memiliki permasalahan dalam mendeteksi tekstur kulit. Linear discriminant analysis (LDA) merupakan algoritma ekstraksi fitur, dalam deteksi kulit digunakan untuk mengekstrak fitur tekstur kulit yang dapat menangani masalah SPM. Namun LDA memiliki permasalahan apabila digunakan untuk mengekstrak fitur tekstur kulit pada kernel yang berbeda. Distance transform (DT) merupakan algoritma untuk menghitung jarak citra biner pada setiap pikel gambar dan fitur poin terdekatnya, DT merupakan algoritma yang dapat mengatasi masalah pada LDA. Kombinasi algoritma SPM, LDA dan DT diusulkan untuk memperbaiki performa dari kemunculan kulit pada deteksi kulit. Dataset pada metode yang diusulkan menggunakan IBTD dataset. Hasil dari metode yang diusulkan bahwa metode yang diusulkan menunjukan peningkatan akurasi deteksi kesalahan yang signifikan pada SPM dan LDA

Melhorias na segmentação de pele humana em imagens digitais

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Segmentação de pele humana possui diversas aplicações nas áreas de visão computacional e reconhecimento de padrões, cujo propósito principal é distinguir regiões de pele e não pele em imagens. Apesar do elevado número de métodos disponíveis na literatura, a segmentação de pele com precisão ainda é uma tarefa desafiadora. Muitos métodos contam somente com a informação de cor, o que não discrimina completamente as regiões da imagem devido a variações nas condições de iluminação e à ambiguidade entre a cor da pele e do plano de fundo. Dessa forma, há ainda a demanda em melhorar a segmentação. Este trabalho apresenta três contribuições com respeito a essa necessidade. A primeira é um método autocontido para segmentação adaptativa de pele que faz uso de análise espacial para produzir regiões nas quais a cor da pele é estimada e, dessa forma, ajusta o padrão da cor para uma imagem em particular. A segunda é a introdução da detecção de saliência para, combinada com detectores de pele baseados em cor, realizar a remoção do plano de fundo, o que elimina muitas regiões de não pele. A terceira é uma melhoria baseada em textura utilizando superpixels para capturar energia de regiões na imagem filtrada, que é então utilizada para caracterizar regiões de não pele e assim eliminar a ambiguidade da cor adicionando um segundo voto. Resultados experimentais obtidos em bases de dados públicas comprovam uma melhoria significativa nos métodos propostos para segmentação de pele humana em comparação com abordagens disponíveis na literaturaAbstract: Human skin segmentation has several applications on computer vision and pattern recognition fields, whose main purpose is to distinguish skin and non-skin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. Therefore, there is still demand to improve the segmentation process. Three main contributions toward this need are presented in this work. The first is a self-contained method for adaptive skin segmentation that makes use of spatial analysis to produce regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. The second is the combination of saliency detection with color skin segmentation, which performs a background removal to eliminate non-skin regions. The third is a texture-based improvement using superpixels to capture energy of regions in the filtered image, employed to characterize non-skin regions and thus eliminate color ambiguity adding a second vote. Experimental results on public data sets demonstrate a significant improvement of the proposed methods for human skin segmentation over state-of-the-art approachesMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2

Static hand gesture segmentation for images with complex background; detection and tracking of dynamic hand gesture

This thesis presents color hand gesture segmentation for static images with complex background along with tracking and detection of hand gesture from video sequence. This thesis consists of two works: 1) Static. 2) Dynamic. In the first part, aim is to automatically segment the hand gesture from a given image under different luminance conditions and complex backgrounds. The luminance value affects the color component of an image which leads to increase the noise level in the segmented image. This paper proposes a combined model of two color spaces i.e., HSI, YCbCr and morphological operations with labeling to improve the segmentation performance of color hand gesture from complex backgrounds in terms of completeness and correctness. The proposed color model separates the chrominance and luminance components of the image. The performance of the proposed method is demonstrated through simulation and the experime ntal results reveal that proposed method provides better performance accuracy compared to the HSI and YCbCr methods individually in terms of correctness and completeness. In the second part, aim is to automatic detection and tracking of hand gesture from v ideo sequence under different backgrounds. It involves three steps: 1). Hand tracking 2). Hand detection 3). Hand identification.Here all the simulations are done in MATLAB 10 environment

Digital Image Analysis of Vitiligo for Monitoring of Vitiligo Treatment

Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules that result from damage to and destruction of epidermal melanocytes. Visually, the vitiligous areas are paler in contrast to normal skin or completely white due to the lack of pigment melanin. The course of vitiligo is unpredictable where the vitiligous skin lesions may remain stable for years before worsening. Vitiligo treatments have two objectives, to arrest disease progression and to re-pigment the vitiligous skin lesions. To monitor the efficacy of the treatment, dermatologists observe the disease directly, or indirectly using digital photos. Currently there is no objective method to determine the efficacy of the vitiligo treatment. Physician's Global Assessment (PGA) scale is the current scoring system used by dermatologists to evaluate the treatment. The scale is based on the degree of repigmentation within lesions over time. This quantitative tool however may not be help to detect slight changes due to treatment as it would still be largely dependent on the human eye and judgment to produce the scorings. In addition, PGA score is also subjective, as it varies with dermatologists. The progression of vitiligo treatment can be very slow and can take more than 6 months. It is observed that dermatologists find it visually hard to determine the areas of skin repigmentation due to this slow progress and as a result the observations are made after a longer time frame. The objective of this research is to develop a tool that enables dermatologists to determine and quantify areas of repigmentation objectively over a shorter time frame during treatment. The approaches towards achieving this objective are based on digital image processing techniques. Skin color is due to the combination of skin histological parameters, namely pigment melanin and haemoglobin. However in digital imaging, color is produced by combining three different spectral bands, namely red, green, and blue (RGB). It is believed that the spatial distribution of melanin and haemoglobin in skin image could be separated. It is found that skin color distribution lies on a two-dimensional melanin-haemoglobin color subspace. In order to determine repigmentation (due to pigment melanin) it is necessary to perform a conversion from RGB skin image to this two-dimensional color subspace. Using principal component analysis (PCA) as a dimensional reduction tool, the two-dimensional subspace can be represented by its first and second principal components. Independent component analysis is employed to convert the twodimensional subspace into a skin image that represents skin areas due to melanin and haemoglobin only. In the skin image that represents skin areas due to melanin, vitiligous skin lesions are identified as skin areas that lack melanin. Segmentation is performed to separate the healthy skin and the vitiligous lesions. The difference in the vitiligous surface areas between skin images before and after treatment will be expressed as a percentage of repigmentation in each vitiligo lesion. This percentage will represent the repigmentation progression of a particular body region. Results of preliminary and pre-clinical trial study show that our vitiligo monitoring system has been able to determine repigmentation progression objectively and thus treatment efficacy on a shorter time cycle. An intensive clinical trial is currently undertaken in Hospital Kuala Lumpur using our developed system. VI

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Enhancing person annotation for personal photo management using content and context based technologies

Rapid technological growth and the decreasing cost of photo capture means that we are all taking more digital photographs than ever before. However, lack of technology for automatically organising personal photo archives has resulted in many users left with poorly annotated photos, causing them great frustration when such photo collections are to be browsed or searched at a later time. As a result, there has recently been significant research interest in technologies for supporting effective annotation. This thesis addresses an important sub-problem of the broad annotation problem, namely "person annotation" associated with personal digital photo management. Solutions to this problem are provided using content analysis tools in combination with context data within the experimental photo management framework, called “MediAssist”. Readily available image metadata, such as location and date/time, are captured from digital cameras with in-built GPS functionality, and thus provide knowledge about when and where the photos were taken. Such information is then used to identify the "real-world" events corresponding to certain activities in the photo capture process. The problem of enabling effective person annotation is formulated in such a way that both "within-event" and "cross-event" relationships of persons' appearances are captured. The research reported in the thesis is built upon a firm foundation of content-based analysis technologies, namely face detection, face recognition, and body-patch matching together with data fusion. Two annotation models are investigated in this thesis, namely progressive and non-progressive. The effectiveness of each model is evaluated against varying proportions of initial annotation, and the type of initial annotation based on individual and combined face, body-patch and person-context information sources. The results reported in the thesis strongly validate the use of multiple information sources for person annotation whilst emphasising the advantage of event-based photo analysis in real-life photo management systems