Combining local regularity estimation and total variation optimization for scale-free texture segmentation

Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures

Aprendizado ativo baseado em atributos contextuais de superpixel para classificação de imagem de sensoriamento remoto

Orientadores: Alexandre Xavier Falcão, Jefersson Alex dos SantosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Recentemente, técnicas de aprendizado de máquina têm sido propostas para criar mapas temáticos a partir de imagens de sensoriamento remoto. Estas técnicas podem ser divididas em métodos de classificação baseados em pixels ou regiões. Este trabalho concentra-se na segunda abordagem, uma vez que estamos interessados em imagens com milhões de pixels e a segmentação da imagem em regiões (superpixels) pode reduzir consideravelmente o número de amostras a serem classificadas. Porém, mesmo utilizando superpixels, o número de amostras ainda é grande para anotá-las manualmente e treinar o classificador. As técnicas de aprendizado ativo propostas resolvem este problema começando pela seleção de um conjunto pequeno de amostras selecionadas aleatoriamente. Tais amostras são anotadas manualmente e utilizadas para treinar a primeira instância do classificador. Em cada iteração do ciclo de aprendizagem, o classificador atribui rótulos e seleciona as amostras mais informativas para a correção/confirmação pelo usuário, aumentando o tamanho do conjunto de treinamento. A instância do classificador é melhorada no final de cada iteração pelo seu treinamento e utilizada na iteração seguinte até que o usuário esteja satisfeito com o classificador. Observamos que a maior parte dos métodos reclassificam o conjunto inteiro de dados em cada iteração do ciclo de aprendizagem, tornando este processo inviável para interação com o usuário. Portanto, enderaçamos dois problemas importantes em classificação baseada em regiões de imagens de sensoriamento remoto: (a) a descrição efetiva de superpixels e (b) a redução do tempo requerido para seleção de amostras em aprendizado ativo. Primeiro, propusemos um descritor contextual de superpixels baseado na técnica de sacola de palavras, que melhora o resultado de descritores de cor e textura amplamente utilizados. Posteriormente, propusemos um método supervisionado de redução do conjunto de dados que é baseado em um método do estado da arte em aprendizado ativo chamado Multi-Class Level Uncertainty (MCLU). Nosso método mostrou-se tão eficaz quanto o MCLU e ao mesmo tempo consideravelmente mais eficiente. Adicionalmente, melhoramos seu desempenho por meio da aplicação de um processo de relaxação no mapa de classificação, utilizando Campos Aleatórios de MarkovAbstract: In recent years, machine learning techniques have been proposed to create classification maps from remote sensing images. These techniques can be divided into pixel- and region-based image classification methods. This work concentrates on the second approach, since we are interested in images with millions of pixels and the segmentation of the image into regions (superpixels) can considerably reduce the number of samples for classification. However, even using superpixels the number of samples is still large for manual annotation of samples to train the classifier. Active learning techniques have been proposed to address the problem by starting from a small set of randomly selected samples, which are manually labeled and used to train a first instance of the classifier. At each learning iteration, the classifier assigns labels and selects the most informative samples for user correction/confirmation, increasing the size of the training set. An improved instance of the classifier is created by training, after each iteration, and used in the next iteration until the user is satisfied with the classifier. We observed that most methods reclassify the entire pool of unlabeled samples at every learning iteration, making the process unfeasible for user interaction. Therefore, we address two important problems in region-based classification of remote sensing images: (a) the effective superpixel description and (b) the reduction of the time required for sample selection in active learning. First, we propose a contextual superpixel descriptor, based on bag of visual words, that outperforms widely used color and texture descriptors. Second, we propose a supervised method for dataset reduction that is based on a state-of-art active learning technique, called Multi-Class Level Uncertainty (MCLU). Our method has shown to be as effective as MCLU, while being considerably more efficient. Additionally, we further improve its performance by applying a relaxation process on the classification map by using Markov Random FieldsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Line Based Multi-Range Asymmetric Conditional Random Field For Terrestrial Laser Scanning Data Classification

Terrestrial Laser Scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, highly dense three-dimensional point cloud of object surfaces by laser range finding. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. However, classifying massive TLS data faces many challenges, such as complex urban scene, partial data acquisition from occlusion. To make an automatic, accurate and robust TLS data classification, we present a line-based multi-range asymmetric Conditional Random Field algorithm. The first contribution is to propose a line-base TLS data classification method. In this thesis, we are interested in seven classes: building, roof, pedestrian road (PR), tree, low man-made object (LMO), vehicle road (VR), and low vegetation (LV). The line-based classification is implemented in each scan profile, which follows the line profiling nature of laser scanning mechanism.Ten conventional local classifiers are tested, including popular generative and discriminative classifiers, and experimental results validate that the line-based method can achieve satisfying classification performance. However, local classifiers implement labeling task on individual line independently of its neighborhood, the inference of which often suffers from similar local appearance across different object classes. The second contribution is to propose a multi-range asymmetric Conditional Random Field (maCRF) model, which uses object context as post-classification to improve the performance of a local generative classifier. The maCRF incorporates appearance, local smoothness constraint, and global scene layout regularity together into a probabilistic graphical model. The local smoothness enforces that lines in a local area to have the same class label, while scene layout favours an asymmetric regularity of spatial arrangement between different object classes within long-range, which is considered both in vertical (above-bellow relation) and horizontal (front-behind) directions. The asymmetric regularity allows capturing directional spatial arrangement between pairwise objects (e.g. it allows ground is lower than building, not vice-versa). The third contribution is to extend the maCRF model by adding across scan profile context, which is called Across scan profile Multi-range Asymmetric Conditional Random Field (amaCRF) model. Due to the sweeping nature of laser scanning, the sequentially acquired TLS data has strong spatial dependency, and the across scan profile context can provide more contextual information. The final contribution is to propose a sequential classification strategy. Along the sweeping direction of laser scanning, amaCRF models were sequentially constructed. By dynamically updating posterior probability of common scan profiles, contextual information propagates through adjacent scan profiles

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net -is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of similar to 0.88, a 95HD of similar to 47 voxels and an AVD of similar to 0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice similar to 0.76, 95HD similar to 59, AVD similar to 1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies