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Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them,... more
Hidden Markov models are very robust and have been widely used in a wide range of application fields; however, they can prove some limitations for data restoration under some complex situations. These latter include cases when the data to... more
Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them,... more
Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network... more
A novel hierarchical approach for image retrieval is proposed in this paper. First, a color label histogram is used to effectively filter out the images that are not similar to the query image in color. The color label matrix is built by... more
In this paper, an effective content-based visual image retrieval system is presented. This system consists of two main components: visual content extraction and indexing, and query engine. Each image in the image database is represented... more
Texture is one of the most important features for object detection and recognition. In many applications, it is derived from the responses of texture filters. In this paper, we evaluate the potential of seven texture filter banks for the... more
Texture is one of the most important features for object detection and recognition. In many applications, it is derived from the responses of texture filters. In this paper, we evaluate the potential of seven texture filter banks for the... more
This paper presents a general 2D object characterization and matching scheme based on the information provided by color and shape. We will focus on matching objects from generic images with complex scenes. In order to identify the region... more
Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them,... more
Method for robust demarcation of the basal cell carcinoma (BCC) is presented employing novel dependent component analysis (DCA)-based approach to unsupervised segmentation of the red-green-blue (RGB) fluorescent image of the BCC. It... more
This study presents a robust approach for characterization of multi-layered forests using airborne laser scanning (ALS) data. Fuel mapping or biomass estimation requires knowing the diversity and boundaries of the forest patches, as well... more
A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object... more
This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first... more
This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first... more
In this paper, we present an unsupervised, automated technique for brain tissue segmentation based on multivariate magnetic resonance (MR) and spectroscopy images, for patients with gliomas. The algorithm uses spectroscopy data for coarse... more
New techniques for more accurate unsupervised segmentation of lung tissues from Low Dose Computed Tomography (LDCT) are proposed. In this paper we describe LDCT images and desired maps of regions (lung and the other chest tissues) by a... more
A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases... more
Many important applications of image analysis deal with multi-modal images such that each object of interest relates to an individual mode in the marginal signal distribution collected over the image. Segmentation of such seemingly simple... more
This work deals with unsupervised sonar image segmentation. We present a new estimation and segmentation procedure on images provided by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow... more
Segmentation of natural images using Scale-Space representations: A linear and a non-linear approach
In general purpose computer vision systems, unsupervised image analysis is mandatory in order to achieve an automatic operation. In this paper a different approach to image segmentation for natural scenes is presented. Scale-Space... more
Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations
In this paper, we introduce a framework that merges classical ideas borrowed from scale-space and multi-resolution segmentation with non-linear partial differential equations. A non-linear scale-space stack is constructed by means of an... more
In this paper we propose the Adaptive Scene Dependent Filters (ASDF) to enhance the online learning capabilities of an object recognition system in real-world scenes. The ASDF method proposed extends the idea of unsupervised segmentation... more
A method for unsupervised segmentation by incremental clustering is introduced. Inspired by incremental approach and correlation clustering, clusters are added and refined during segmentation process. Correlation clustering is to keep... more
Digital matting consists in extracting a foreground element from a background image. Besides the image, usual matting methods need to be initialized with two disjoint regions : the set of foreground only pixels and the set of background... more
A methodology is proposed for contrast enhanced unsupervised segmentation of a liver from a twodimensional multi-phase CT image. The multi-phase CT image is represented by a linear mixture model, whereupon each single-phase CT image is... more
and the editors of this volume; I hope that I have succeeded in correcting the errors they pointed out to me. Reader, bear in mind that all the ideas presented here have been simplified to improve comprehensibility. As always, if you are... more
This paper empirically compares nine families of image dissimilarity measures that are based on distributions of color and texture features summarizing over 1000 CPU hours of computational experiments. Ground truth is collected via a... more
In this paper, we propose a novel approach to perform scene segmentation of TV series. Using the output of our existing speaker diarization system, any temporal segment of the video can be described as a binary feature vector. A... more
The objective of this paper is to describe the comparative efficacy of three signal processing-based feature extraction methodologies for classifying normal oral mucosa, early and advanced stages of Oral Submucous Fibrosis (OSF) by... more
We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve... more
We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve... more
We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve... more
We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve... more
We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neural networks. Memory is included to resolve... more
TV stream structuring consists in detecting precisely the first and the last frames of all the programs and the breaks (commercials, trailers, station identification, bumpers) of a given stream and then in annotating all these segments... more
Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them,... more
In this work a novel texture model particularly suited for unsupervised image segmentation is proposed. Any texture is represented at region level by means of a finite-state hierarchical model resulting from the superposition of several... more
We present the ACQDIV corpus database and aggregation pipeline, a tool developed as part of the European Research Council (ERC) funded project ACQDIV, which aims to identify the universal cognitive processes that allow children to acquire... more
あらまし 音素セグメンテーションは,音声認識や音声合成における基本的な問題である。しかしながら,言語情報 や音響モデルに関する知識を全く用いない教師なし音素セグメンテーションは,非常に難解な問題として挙げられる。 その本質的問題は「どうのように最適な分割を定義する か」である。本論文では,最適な分割を確率的な枠組みで定 式化する。統計分析と情報理論を用いて、最適化対象として三つの目標関数を提案する:Mean Square Error (MSE), Log... more
This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability.... more
This paper presents a new method to restore a particular type of degradation related to ancient document images. This degradation, referred to as "bleed-through", is due to the paper porosity, the chemical quality of the ink, or the... more
We propose an unsupervised segmentation algorithm for magnetic resonance images (MRI) endowed with a parametric intensity inhomogeneity (IIH) correction schema and the on-line estimation of the image model intensity class means. The paper... more
This paper presents a new method to restore a particular type of degradation related to ancient document images. This degradation, referred to as "bleed-through", is due to the paper porosity, the chemical quality of the ink, or the... more
In this paper, we present an original unsupervised segmentation scheme which splits a grey level image into different sets of connected pixels whose grey levels are homogeneous. This approach is based on an analysis of a triangular table... more