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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain ...

Currently, Mathematical Morphology (MM) has become a powerful tool in Digital Image Processing (DIP). It allows processing images to enhance fuzzy areas, segment objects, detect edges and analyze structures. The techniqu...

"SAR (synthetic aperture radar) and PolSAR (polarimetric synthetic aperture radar) images fulfill a fundamental role in Earth observation, due to their advantages over optical images. However, the presence of speckle noi...

Subjects / keywords: WILKINSON, FRANK, ED; ACADEMIC PRESS; 331.12 D997d

Segmentation in biological images is essential for the determination of biological parameters that allow the construction of models of several biological problems. This helps to establish clear relationships between thos...

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general colo...

Segmentation is often a critical step in image analysis. Microscope image components show great variability of shapes, sizes, intensities and textures. An inaccurate segmentation conditions the ulterior quantification a...

In this paper we improve the face detection and segmentation technique proposed in [1]. In order to obtain the shape of the face, we use a region based approach and find the face as a set of regions from a generic segmen...

Magnetic Resonance Imaging is one of the most important medical imaging techniques for the investigating diseases of the human brain. A novel method for automatic segmentation Magnetic resonance brain image framework is ...

In this work we study the extraction of semantic objects from images together with a metric that ranks them according to their perceptual significance. To obtain an initial segmentation we use elements of mathematical mo...

This paper presents a database containing 'ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations ...

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local ...

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with...

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propos...

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semant...

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segme...

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression o...

The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framew...

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessaril...

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmenta...

Synthetic Aperture Radar (SAR) images are corrupted by a signal-dependent non-additive noise called speckle. Many statistical models have been proposed to describe this noise, aiming at the development of specialized tec...

This work presents preliminary results of a method for semi-automatic detection of fat and hematopoietic cells as well as trabecular surfaces in bone marrow biopsies, in order to calculate the percentage of each type of ...

Subjects / keywords: Computer science; Segmentation; Artificial intelligence; Annotation; Volume (thermodynamics); Set (abstract data type); Training set; Pattern recognition (psychology); Test data; Computer vision; Physics; Quantum mechani...

Subjects / keywords: Image segmentation; Range segmentation; Segmentation; Pattern recognition (psychology); Artificial intelligence; Graph; Segmentation-based object categorization; Computer science; Minimum spanning tree-based segmentation...