Segmentacão de imagens de câncer de pele utilizando superpixels

Nowadays, there is a wide variety of computational techniques for the processing of digital images that can be applied to a wide range of fields, such as industry, commerce, education and the field of medicine. Highlighting with great importance, the field of medicine, which through the analysis of digital images, some diseases that are difficult to detect and identify can be diagnosed and treated properly. Among the diagnosed diseases, the skin lesions are the most disturbing representative of cutaneous melanoma. Because melanoma presents difficult identification when compared to other skin lesions or nevus (moles), technologies based on digital imaging techniques may be used to characterize the disease. In this work a digital image segmentation method is applied for the detection of melanoma skin cancer, using the superpixel technique, based on the Simple Linear Iterative Clustering - SLIC algorithm. The superpixel technique presents, as a basic characteristic, the grouping of pixels in regions significantly similar, in which the characteristics of the study images will be analyzed, which will facilitate the detection of skin lesions.Atualmente, verifica-se uma ampla variedade de técnicas computacionais destinadas ao processamento de imagens digitais que podem ser aplicadas as mais diversas áreas de atuação como na indústria, comércio, educação e medicina. Esta última destaca-se devido `a importância, pois através da análise de imagens digitais, algumas doenças de difícil detecçao e identificação podem ser diagnosticadas e tratadas adequadamente. Dentre as doenças que podem ser detectadas através da análise de imagens digitais, destacam-se as lesões de pele, tendo como seu representante mais preocupante o melanoma cutâneo. Pelo fato do melanoma apresentar difícil identificação ao ser comparado com outras lesões de pele ou nevos (pintas), podem ser usadas tecnologias baseadas em técnicas de processamento de imagens digitais para caracterizar a doença. Neste trabalho é aplicado um método de segmentação de imagens digitais para a detecção do câncer de pele melanoma, utilizando a técnica de superpixel, baseado no algoritmo Simple Linear Iterative Clustering - SLIC. A técnica de superpixel apresenta, como característica básica, o agrupamento de pixels em regiões significativamente semelhantes, em que serão analisadas as características das imagens de estudo, o que facilitar´a a detecçãao de lesões na pele

Superpixels: An Evaluation of the State-of-the-Art

Superpixels group perceptually similar pixels to create visually meaningful entities while heavily reducing the number of primitives for subsequent processing steps. As of these properties, superpixel algorithms have received much attention since their naming in 2003. By today, publicly available superpixel algorithms have turned into standard tools in low-level vision. As such, and due to their quick adoption in a wide range of applications, appropriate benchmarks are crucial for algorithm selection and comparison. Until now, the rapidly growing number of algorithms as well as varying experimental setups hindered the development of a unifying benchmark. We present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms utilizing a benchmark focussing on fair comparison and designed to provide new insights relevant for applications. To this end, we explicitly discuss parameter optimization and the importance of strictly enforcing connectivity. Furthermore, by extending well-known metrics, we are able to summarize algorithm performance independent of the number of generated superpixels, thereby overcoming a major limitation of available benchmarks. Furthermore, we discuss runtime, robustness against noise, blur and affine transformations, implementation details as well as aspects of visual quality. Finally, we present an overall ranking of superpixel algorithms which redefines the state-of-the-art and enables researchers to easily select appropriate algorithms and the corresponding implementations which themselves are made publicly available as part of our benchmark at davidstutz.de/projects/superpixel-benchmark/

Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided

A Novel Approach to Detect Copy Move Forgery using Deep Learning

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images

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Generation of Digital Surface Models (DSMs) from stereo satellite (spaceborne) images is classically performed by Ground Control Points (GCPs) which require site visits and precise measurement equipment. However, collection of GCPs is not always possible and such requirement limits the usage of spaceborne imagery. This study aims at developing a fast, fully automatic, GCP-free workflow for DSM generation. The problems caused by GCP-free workflow are overcome using freely-available, low resolution static DSMs (LR-DSM). LR-DSM is registered to the reference satellite image and the registered LR-DSM is used for i) correspondence generation and ii) initial estimate generation for 3-D reconstruction. Novel methods are developed for bias removal for LR-DSM registration and bias equalization for projection functions of satellite imaging. The LR-DSM registration is also shown to be useful for computing the parameters of simple, piecewise empirical projective models. Recent computer vision approaches on stereo correspondence generation and dense depth estimation are tested and adopted for spaceborne DSM generation. The study also presents a complete, fully automatic scheme for GCPfree DSM generation and demonstrates that GCP-free DSM generation is possible and can be performed in much faster time on computers. The resulting DSM can be used in various remote sensing applications including building extraction, disaster monitoring and change detection.Ph.D. - Doctoral Progra

Um método robusto para modelagem 3D de ambientes internos usando dados RGB-D

Orientadora : Daniel Rodrigues dos SantosTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa: Curitiba, 12/11/2015Inclui referências : f. 106-112Resumo: O objetivo deste trabalho é propor um método robusto para modelagem 3D de ambientes internos usando dados RGB-D. Basicamente, a modelagem 3D de ambientes está dividida em quatro tarefas, a saber: a escolha do sensor de imageamento; o problema do registro de nuvem de pontos 3D adquiridos pelo sensor de imageamento em diferentes pontos de vista; o problema da detecção de lugares anteriormente visitados (loop closure); e o problema da análise de consistência global. Atualmente, o Kinect é o sensor RGB-D mais empregado na aquisição de dados para modelagem de ambientes internos, uma vez que é leve, flexível e de fácil manuseio. A etapa de registro consiste em determinar os parâmetros de transformação relativa entre pares de nuvens de pontos e, neste trabalho, é dividida em duas partes: a primeira parte consiste em executar o registro inicial dos dados 3D usando pontos visuais e o modelo de corpo rígido 3D; na segunda parte, os parâmetros iniciais são refinados empregando um modelo matemático baseado numa abordagem paralaxe-a-plano, o que torna o método robusto. Para minimizar os efeitos da propagação de erros provocados na etapa de registro dos pares de nuvens de pontos 3D, o método proposto detecta lugares anteriormente visitados usando uma imagem de (frame-chave). Basicamente, é feita uma busca por imagens com grau de similaridade com a imagem de referência e, por fim, é obtida uma nova restrição espacial. A etapa de consistência global cria um grafo dirigido e ponderado, sendo cada vértice do grafo representado pelos parâmetros de transformação obtidos na etapa de registro dos dados, enquanto suas arestas representam as restrições espaciais definidas pelos parâmetros de transformação obtidos entre os lugares revisitados. A otimização deste grafo é feito através do método GraphSLAM. Experimentos foram realizados em cinco ambientes internos e o método proposto propiciou uma acurácia relativa em torno de 6,85 cm. . Palavras-chave:sensor RGB-D; modelagem 3D; Otimização da trajetória baseado em grafos; registro de pares de nuvens de pontos; análise de consistência global.Abstract: The objective of this paper is to propose a robust method for 3D modeling indoors using RGB-D data. Basically, the 3D modeling environment is divided into four problems, namely: the choice of the imaging sensor; the cloud Registration problem of 3D points acquired by the imaging sensor in different views; the problem of detection places previously visited (loop closure); and the problem of global consistency analysis. Currently, Kinect is the RGB-D sensor more employed in data acquisition for modeling indoor environments, since they are lightweight, flexible and easy to use. The registration step is to determine the transformation parameters relating between pairs of point cloud and in this paper is divided into two parts: the first part is to run the initial registration of 3D data using visual points and rigid body model 3D; in the second part, the initial parameters are refined using a mathematical model based on a parallax-the-plan approach, which makes the robust method. To minimize the effects of propagation of errors caused in the 3D point cloud pairs registration step, the proposed method detects previously visited places using a reference image (key-frame). Basically, a search for images with degree of correlation is made with the reference image, and finally, a new spatial constraint is obtained. The overall consistency of step creates a directed and weighted graph, each nodes in the graph represented by the transformation parameters obtained in the data registration step, whereas its edges represent the spatial constraints defined by the transformation parameters obtained between Revisited places. The optimization of the graph is made by GraphSLAM method. Experiments were carried out in five indoor and the proposed method provided a relative accuracy around 6,85 cm.. Keywords: RGB-D sensor; mapping 3D; GraphSLAM; pairs registration of point clouds; consistency global analysis

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition