Probabilistic Relaxation Labeling Research Articles

Spectral Estimation of Multiple Light Sources based on Highlight Detection

The authors discuss the spectral estimation of multiple light sources from image data in a complex illumination environment. An approach is proposed to effectively estimate illuminant spectra and the corresponding light sources based on highlight areas that appear on dielectric object surfaces. First, the authors develop a highlight detection method using two types of convolution filters with Gaussian distributions, center-surround and low-pass filters. This method is available even for white surfaces, and it is independent of object color and of viewing and incidence angles. Second, they present an algorithm for estimating the illuminant spectra from extracted highlight areas. Each specular highlight area has a spectral composition corresponding to only one light source among multiple light sources. The spectral image data are projected onto a two-dimensional subspace, where a linear cluster in pixel distribution is detected for each highlight area. Third, the relative positional relationship between highlight areas among different object surfaces is used to identify the light sources on each surface. The authors develop an algorithm based on probabilistic relaxation labeling. The light source for each highlight and the corresponding spectral-power distribution are determined from the iterative labeling process. Finally, the feasibility of the proposed approach is examined in an experiment using a real complex environment, where dielectric objects are illuminated by multiple light sources of light-emitting diode, fluorescence, and incandescence.

Spectral Contextual Classification of Hyperspectral Imagery With Probabilistic Relaxation Labeling.

In this paper, a spectral-spatial classification framework based on probabilistic relaxation labeling using compatibility coefficients is proposed for hyperspectral images. It is a two-stage classifier that uses maximum a posteriori (MAP) estimation to maximize posterior probabilities of classification map obtained in first stage to incorporate spatial information for better classification accuracy. Two different forms of compatibility coefficients based on correlation and mutual information are used for MAP estimation. The initial probability estimates are obtained from probabilistic support vector machine (SVM) classifier. The combination of SVM with MAP estimation is investigated and compared with benchmark Markov random field and extended morphological profile-based approaches and some other recent methods. The experimental results are presented for three airborne hyperspectral images. The results reveal that incorporation of contextual information with both forms of compatibility coefficients statistically significantly improved SVM results. The compatibility coefficients based on correlation produced the best results among the relaxation methods outperforming many existing methods.

Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration

The anisotropic scale space (ASS) is often used to enhance the performance of a scale-invariant feature transform (SIFT) algorithm in the registration of synthetic aperture radar (SAR) images. The existing ASS-based methods usually suffer from unstable keypoints and false matches, since the anisotropic diffusion filtering has limitations in reducing the speckle noise from SAR images while building the ASS image representation. We proposed a speckle reducing SIFT match method to obtain stable keypoints and acquire precise matches for the SAR image registration. First, the keypoints are detected in a speckle reducing anisotropic scale space constructed by the speckle reducing anisotropic diffusion, so that speckle noise is greatly reduced and prominent structures of the images are preserved, consequently the stable keypoints can be derived. Next, the probabilistic relaxation labeling approach is employed to establish the matches of the keypoints then the correct match rate of the keypoints is significantly increased. Experiments conducted on simulated speckled images and real SAR images demonstrate the effectiveness of the proposed method.

Point-pattern matching based on point pair local topology and probabilistic relaxation labeling

This paper presents a robust point-pattern matching (PPM) algorithm, in which the invariant feature and probabilistic relaxation labeling are combined to improve the assignment accuracy and efficiency. A local feature descriptor, namely, point pair local topology (PPLT), is proposed first. The feature descriptor is defined by histogram which is constructed using the weighting of distance measures and angle measures based on local point pair. We use the matching scores of point pair local topology descriptor’s statistic test to define new compatibility coefficients. Then, the robust support functions are constructed based on the obtained compatibility coefficients. Finally, according to the relaxed iterations of matching probability matrix and the mapping constraints required by the bijective correspondence, the correct matching results are obtained. A number of comparison and evaluation experiments on both synthetic point sets and real-world data demonstrate that the proposed algorithm performs better in the presence of outliers and positional jitter. In addition, it achieves the superior performance under similarity and even nonrigid transformation among point sets in the meantime compared with state-of-the-art approaches.

Parallel probabilistic relaxation labelling based on Markov random fields for spectral-spatial hyperspectral image classification

ABSTRACTThe large volume of data and computational complexity of algorithms limit the application of hyperspectral image classification to real-time operations. This work addresses the use of different parallel processing techniques to speed up the Markov random field (MRF)-based method to perform spectral-spatial classification of hyperspectral imagery. The Metropolis relaxation labelling approach is modified to take advantage of multi-core central processing units (CPUs) and to adapt it to massively parallel processing systems like graphics processing units (GPUs). The experiments on different hyperspectral data sets revealed that the implementation approach has a huge impact on the execution time of the algorithm. The results demonstrated that the modified MRF algorithm produced classification accuracy similar to conventional methods with greatly improved computational performance. With modern multi-core CPUs, good computational speed-up can be achieved even without additional hardware support. The CPU-GPU hybrid framework rendered the otherwise computationally expensive approach suitable for time-constrained applications.

A Probabilistic Relaxation Labeling (PRL) Based Method for C. elegans Cell Tracking in Microscopic Image Sequences

Automatic cell tracking for 3D time-lapse images plays an important role in studying of live cell, which allows investigation of biological processes in vivo at the single cell resolution. In this paper, we propose a method for cell tracking during C. elegans embryogenesis based on probabilistic relaxation labeling (PRL). Instead of relying on the absolute cell position, we make use of the cell-to-cell relative position with two compatibility coefficients to achieve cell tracking. The tracking results obtained with this method are highly accurate with temporal resolution of one minute. In addition, our method can also been realized on multi-core CPUs, therefore providing an effective tool for analysis of large-scale data consisting of 3D time-lapse live cell images.

Automatic understanding of sketch maps using context-aware classification

Sketching is a natural and easy way for humans to express visual information in everyday life. Despite a number of approaches to understand online sketch maps, the automatic understanding of offline, hand-drawn sketch maps still poses a problem. This paper presents a new approach for novel sketch map understanding. To our knowledge, this is the first comprehensive work dealing with this task in an offline way. This paper presents a system for automatic understanding of sketch maps and the underlying algorithms for all steps. Major parts are a region-growing segmentation for sketch map objects, a classification for isolated objects, and a context-aware classification. The context-aware classification uses probabilistic relaxation labeling to integrate dependencies between objects into the recognition. We show how these algorithms can deal with the major problems of sketch map understanding, such as vagueness in interpretation. Our experiments demonstrate the importance of context-aware classification for sketch map understanding. In addition, a new database of annotated sketch maps was developed and is made publicly available. This can be used for training and evaluation of sketch map understanding algorithms.

A Flexible and Customizable Architecture for the Relaxation Labeling Algorithm

This brief presents a flexible and customizable architecture for the probabilistic relaxation labeling (PRL) algorithm. The algorithm has been restructured by using a hardware-friendly process that is executed on the proposed architecture. This enables the design to handle different numbers of objects and labels flexibly. Moreover, in the design, the proposed PRL unit can be easily duplicated for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> times according to the available resources on the field-programmable gate array (FPGA). In this brief, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> can be scalable up to 10 by using a Virtex-6 FPGA XC6VLX240T platform. Compared with existing architectures that are not suitable for a large number of objects, the proposed architecture reduces the time complexity from <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> × <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> ) to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> ) with the same <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> × <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) space complexity, where <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> are the numbers of objects and labels, respectively. The experimental results show that the execution time of our design is about 15 times less for five objects and about 35 times less for a 128 × 64 image block than the software implementation running on a Quad-core Intel 32-nm machine.

PRL‐dock: Protein‐ligand docking based on hydrogen bond matching and probabilistic relaxation labeling

Protein-ligand docking is widely applied to structure-based virtual screening for drug discovery. This article presents a novel docking technique, PRL-Dock, based on hydrogen bond matching and probabilistic relaxation labeling. It deals with multiple hydrogen bonds and can match many acceptors and donors simultaneously. In the matching process, the initial probability of matching an acceptor with a donor is estimated by an efficient scoring function and the compatibility coefficients are assigned according to the coexisting condition of two hydrogen bonds. After hydrogen bond matching, the geometric complementarity of the interacting donor and acceptor sites is taken into account for displacement of the ligand. It is reduced to an optimization problem to calculate the optimal translation and rotation matrixes that minimize the root mean square deviation between two sets of points, which can be solved using the Kabsch algorithm. In addition to the van der Waals interaction, the contribution of intermolecular hydrogen bonds in a complex is included in the scoring function to evaluate the docking quality. A modified Lennard-Jones 12-6 dispersion-repulsion term is used to estimate the van der Waals interaction to make the scoring function fairly "soft" so that ligands are not heavily penalized for small errors in the binding geometry. The calculation of this scoring function is very convenient. The evaluation is carried out on 278 rigid complexes and 93 flexible ones where there is at least one intermolecular hydrogen bond. The experiment results of docking accuracy and prediction of binding affinity demonstrate that the proposed method is highly effective.

Object-Based Image Analysis of High-Resolution Satellite Images Using Modified Cloud Basis Function Neural Network and Probabilistic Relaxation Labeling Process

Object-based image analysis is quickly gaining acceptance among remote sensing community, and object-based image classification methods are increasingly being used for classification of land use/cover units from high-resolution satellite images with results closer to human interpretation compared to per-pixel classifiers. The problem of nonlinear separability of classes in a feature space consisting of spectral/spatial/textural features is addressed by kernel-based nonlinear mapping of the feature vectors. This facilitates use of linear discriminant functions for classification as used in artificial neural networks (ANNs). In this paper, performance of a recently introduced kernel called cloud basis function (CBF) is investigated with some modification for classification. The CBF has demonstrated superior performance to the tune of about 4% higher classification accuracy compared to conventional radial basis function used in ANN. The results are further improved by using probabilistic relaxation labeling as a postprocessing step. This paper has potential applications in urban planning and urban studies.

TRACKING MULTIPLE PERSONS BASED ON ATTRIBUTED RELATIONAL GRAPH

The appearance model is very effective in tracking multiple persons. The main difficulty in tracking persons is to represent appearance reliably and effectively, especially in the presence of occlusions. In this paper, an effective Attributed Relational Graph (ARG) based tracking algorithm is presented to track multiple persons even under occlusions. The appearance of each person is expressed by an ARG model which not only combines color feature with spatial information but also illustrates the relations among body parts. The similarity of ARG models is computed to build a matching matrix in consecutive frames. Four tracking situations are determined according to the matching matrix. In addition, to track persons under occlusions, probabilistic relaxation labeling in the ARG models of body parts is deduced to label occluded persons optimally. Experimental validation of the proposed tracking method is verified and presented on indoor and outdoor sequences.

A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data

Biclustering is an important method in DNA microarray analysis which can be applied when only a subset of genes is co-expressed in a subset of conditions. Unlike standard clustering analyses, biclustering methodology can perform simultaneous classification on two dimensions of genes and conditions in a microarray data matrix. However, the performance of biclustering algorithms is affected by the inherent noise in data, types of biclusters and computational complexity. In this paper, we present a geometric biclustering method based on the Hough transform and the relaxation labeling technique. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometric interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our algorithm, the Hough transform is employed for hyperplane detection in sub-spaces to reduce the computational complexity. Then sub-biclusters are combined into larger ones under the probabilistic relaxation labeling framework. Our simulation studies demonstrate the robustness of the algorithm against noise and outliers. In addition, our method is able to extract biologically meaningful biclusters from real microarray gene expression data.

Probabilistic relaxation labelling using the Fokker–Planck equation

In this paper we develop a new formulation of probabilistic relaxation labelling using the theory of diffusion processes on graphs. Our aim is to tackle the problem of labelling objects consistently and unambiguously using information concerning label consistency and initial label probabilities. We abstract this problem using a support graph with each graph node an object-label assignment. Initial object-label probabilities then evolve across the graph under the governance of the Fokker–Planck equation in terms of an infinitesimal generator matrix computed from the edge weights of the support graph. In this way we effectively kernelise probabilistic relaxation. Encouraging results are obtained in applying the new relaxation process in the applications of scene labelling, data classification, and feature correspondence matching.

Robust multi-view correspondence of noisy 2D points using relaxation

An efficient ghost removal algorithm for reliably extracting correspondence information for 2D points from several views using epipolar constraints is proposed. The point matching relations are modelled using a weighted k-partite graph and a globally convergent iterative algorithm is used to extract reliable maximum cliques from the graph using probabilistic relaxation labelling. Experimental results show the effectiveness of the proposed method.

Bone labelling on micro-magnetic resonance images

The study of trabecular bone is receiving increasing interest within the medical community working in the field of skeletal diseases, such as osteoporosis. Quantification of trabecular bone structure usually requires as a starting point a correct segmentation of the trabecular network. We have developed a probabilistic relaxation labelling technique, which uses local features of the trabecular bone images to improve segmentation. Tests on synthetic images show that bone labelling performs a more accurate segmentation than conventional techniques such as thresholding, especially by preserving the connectivity of the trabecular network. Tests on acquired data show that porosity values obtained after segmentation are in good agreement with experimental values obtained by weighing the bone samples.

Bone labelling on micro-magnetic resonance images

The study of trabecular bone is receiving increasing interest within the medical community working in the field of skeletal diseases, such as osteoporosis. Quantification of trabecular bone structure usually requires as a starting point a correct segmentation of the trabecular network. We have developed a probabilistic relaxation labelling technique, which uses local features of the trabecular bone images to improve segmentation. Tests on synthetic images show that bone labelling performs a more accurate segmentation than conventional techniques such as thresholding, especially by preserving the connectivity of the trabecular network. Tests on acquired data show that porosity values obtained after segmentation are in good agreement with experimental values obtained by weighing the bone samples.

A new probabilistic relaxation scheme and its application to edge detection

This paper presents a new scheme for probabilistic relaxation labeling that consists of an update function and a dictionary construction method. The nonlinear update function is derived from Markov random field theory and Bayes' formula. The method combines evidence from neighboring label assignments and eliminates label ambiguity efficiently. This result is important for a variety of image processing tasks, such as image restoration, edge enhancement, edge detection, pixel classification, and image segmentation. The authors successfully applied this method to edge detection. The relaxation step of the proposed edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial role in refining edge outputs. The experiments show that our algorithm converges quickly and is robust in noisy environments.

Junction detection using probabilistic relaxation

Abstract A novel approach to junction detection using an explicit line finder model and contextual rules is presented. Contextual rules expressing properties of 3D-images (surface orientation discontinuities) limit the number of line intersections interpreted as junctions. A probabilistic relaxation labelling scheme is used to combine the a priori world knowledge represented by contextual rules and the information contained in observed lines. Junctions corresponding to a vertex (V-junctions) and an occlusion (T-junction) of 3D objects are detected and stored in a junciton graph. The information in the junction graph is used to extract higher level features. Results of the most promising method, the polyhedral object face recovery, are briefly discussed. The performance of the junction detection process is demonstrated on images from indoor, outdoor, and industrial environments.

A flexible parallel architecture for relaxation labeling algorithms

The design of a flexible parallel architecture for both the discrete relaxation labeling (DRL) algorithm and the probabilistic relaxation labeling (PRL) algorithm is addressed. Through the analysis of parallelism in the computational models of both algorithms, the parallel execution of the algorithms on a flexible parallel architecture is presented. Three basic types of parallel operations are performed in the architecture: simultaneous, pipeline, and systolic. An illustrative example is used to show how the DRL algorithm can be executed on the parallel architecture. In doing so, the processing element (PE) organization and the combiner organization of the architecture are described. The same architecture with programmable functional units is shown to be able to execute the PRL algorithm, too. The performance comparisons between the proposed architecture and some other existing ones are also given.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Texture segmentation using Voronoi polygons

Textures are defined in terms of primitives called tokens. A texture segmentation algorithm based on the Voronoi tessellation is discussed. The algorithm first builds the Voronoi tessellation of the tokens that make up the textured image. It then computes a feature vector for each Voronoi polygon. These feature vectors are used in a probabilistic relaxation labeling on the tokens, to identify the interior and the border regions of the textures. Some experimental results are shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>