1,135 research outputs found

    Hypothesis-based image segmentation for object learning and recognition

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    Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition

    Brain Tumor Detection and Classification from MRI Images

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    A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In this research, we proposed a transfer learning based approach in conjunction with discriminative learning rates to perform the classification of brain tumors. The data set used is a 3064 T MRI images dataset that contains T1 flair MRI images. We achieved a classification accuracy of 98.83%, 96.26%, and 95.18% for training, validation, and test sets and an F1 score of 0.96 on the T1 Flair MRI dataset

    Automatic Detection of Critical Dermoscopy Features for Malignant Melanoma Diagnosis

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    Improved methods for computer-aided analysis of identifying features of skin lesions from digital images of the lesions are provided. Improved preprocessing of the image that 1) eliminates artifacts that occlude or distort skin lesion features and 2) identifies groups of pixels within the skin lesion that represent features and/or facilitate the quantification of features are provided including improved digital hair removal algorithms. Improved methods for analyzing lesion features are also provided

    A Methodology for Extracting Human Bodies from Still Images

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    Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

    Segmentation of images by color features: a survey

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    En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

    Melhorias na segmentação de pele humana em imagens digitais

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    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

    Machine Learning for Instance Segmentation

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    Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level. A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms. In this thesis, we develop new algorithms for this task. In the first part we provide, for the first time in this field, a method for training a neural network to predict optimal input data for a watershed algorithm. We demonstrate its superior performance compared to other segmentation methods of its category. In the second part, we develop an efficient watershed-based algorithm for weighted graph partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time. We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge. Our algorithm is currently used by the leaders of two connectomics challenges. Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision
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