ICM Algorithm Research Articles

MC-SSM: Nonparametric Semantic Image Segmentation With the ICM Algorithm

In the last few years, there has been considerable interest in scene parsing. This task consists of assigning a predefined class label to each pixel (or pre-segmented region) in an image. To best address the complexity challenge of this task, first, we propose a new geometric retrieval strategy to select nearest neighbors from a database containing fully segmented and annotated images. Then, we introduce a novel and simple energy-minimization model. The proposed cost function of this model combines efficiently different global nonparametric semantic likelihood energy terms. These terms are computed from the (pre-)segmented regions of the (query) image and their structural properties (location, texture, color, context, and shape). Different from the traditional approaches, we use a simple and local optimization procedure derived from the iterative conditional modes algorithm to optimize our energy-based model. Experimental results on two challenging datasets: 1) microsoft research Cambridge dataset and 2) Stanford background dataset demonstrate the feasibility and the success of the proposed approach. Compared to existing annotation methods that require training classifiers for each object and learning many parameters, our method is easy to implement, has a few parameters, and combines different criteria.

G0-WISHART DISTRIBUTION BASED CLASSIFICATION FROM POLARIMETRIC SAR IMAGES

Abstract. Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

Saddlepoint approximations for rank‐invariant permutation tests and confidence intervals with interval‐censoring

AbstractInterval‐censored data occur when subjects are assessed by using regular follow‐up. In such instances, we consider rank‐invariant permutation tests to test the significance of a treatment versus a control. For a wide class of such tests, which includes the Peto & Peto class, we present saddlepoint approximations for the exact permutation mid‐P‐values which achieve extremely small relative errors. The speed and stability of these saddlepoint computations make them practicable for inverting the permutation tests and we compute nominal confidence intervals for the treatment effect. Such confidence intervals are of substantial clinical importance since, more than simply stating the level of statistical significance, they quantify the significant benefit of the treatment by providing a confidence interval for the percentage increase in mean (or median) treatment survival time as compared to control. Our methodology makes heavy use of nonparametric MLEs (NPMLEs) for survival functions and some limitations of existing algorithms, such as the hybrid ICM algorithm, are noted and accommodated. The Canadian Journal of Statistics 42: 308–324; 2014 © 2014 Statistical Society of Canada

Textile Printing Pattern Image Segmentation Based on Algorithm of MRF

In order to improve the accuracy of textile image segmentation, this paper presents a method of applying the MRF algorithm to the fabric image segmentation. Segmentation results could be obtained by three steps: First step is pretreating the original images. Second step is establishing the MRF model by way of the ICM algorithm. The last step is combining the bayesian theory and MAP. Several printing images are applied and perfect results are received in this experiment.

Discrimination of agricultural crops in a tropical semi-arid region of Brazil based on L-band polarimetric airborne SAR data

Recent articles are indicating that polarimetric data provide significantly more information than conventional or multi-polarized images, particularly due to the additional phase information. The objective of this paper is to evaluate the multi-polarized and fully polarimetric L-band airborne SAR-R99B data, in terms of their capability to distinguish among different agricultural crops in the western part of Bahia State, Brazil. Emphasis was given to coffee, cotton and pasture crops which were at well developed growing stages. Discrimination among crops was carried out using graphical analysis of mean backscatter values. Crop classification was performed for single and multiple polarizations, and fully polarimetric images with a classifier that uses the contextual Iterated Conditional Modes–ICM algorithm. The investigation confirmed the potential of L-band multi-polarized and polarimetric airborne SAR-R99B data to distinguish and classify agricultural crops in the tropical condition of the test-site. In addition, it clearly indicated the gradual and considerable improvement that was achieved going from single to three polarizations and from multi-polarized to fully polarimetric images.

The linear attenuation coefficients as features of multiple energy CT image classification

We present in this paper an analysis of the linear attenuation coefficients as useful features of single and multiple energy CT images with the use of statistical pattern classification tools. We analyzed four CT images through two pointwise classifiers (the first classifier is based on the maximum-likelihood criterion and the second classifier is based on the k-means clustering algorithm) and one contextual Bayesian classifier (ICM algorithm – Iterated Conditional Modes) using an a priori Potts–Strauss model. A feature extraction procedure using the Jeffries–Matusita (J–M) distance and the Karhunen–Loève transformation was also performed. Both the classification and the feature selection procedures were found to be in agreement with the predicted discrimination given by the separation of the linear attenuation coefficient curves for different materials.

Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis

We consider the problems of image segmentation and classification, and image restoration when the true image is made up of a small number of (unordered) colors. Our emphasis is on both performance and speed; speed has become increasingly important for analyzing large images and multispectral images with many bands, processing large image databases, real-time or near realtime image analysis, and the online analysis of video. Bayesian image analysis provides an elegant solution to these problems, but it is computationally expensive, and the solutions it provides may be sensitive to unrealistic global properties of the models on which it is based. The ICM algorithm is faster and based on the local properties of the models underlying Bayesian image analysis; parameter estimation is performed iteratively via pseudolikelihood. Mathematical morphology is faster again and is widely considered to perform well, but lacks a statistical basis; method selection (analogous to parameter estimation) is done in a rather ad hoc manner. We propose Bayesian morphology, a synthesis of these methods that attempts to combine the speed of mathematical morphology with the principled statistical basis of ICM. The key observation is that when the original image is discrete (or if an initial segmentation has been carried out), then, assuming a Potts model for the true scene and channel transmission noise, (1) the ICM algorithm is equivalent to a form of mathematical morphology and (2) the segmentation is insensitive to the precise values of the model parameters. Unlike in standard Bayesian images analysis and ICM, it is feasible to do maximum likelihood estimation of the parameters in this setting. For gray-level or multispectral images, we propose an initial segmentation based on the EM algorithm for a mixture model of the marginal distribution of the pixels. The resulting algorithm is much faster than ICM, with gains that increase for more bands and larger images, and has good performance in experiments and for real examples.

Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis

Abstract We consider the problems of image segmentation and classification, and image restoration when the true image is made up of a small number of (unordered) colors. Our emphasis is on both performance and speed; speed has become increasingly important for analyzing large images and multispectral images with many bands, processing large image databases, real-time or near realtime image analysis, and the online analysis of video. Bayesian image analysis provides an elegant solution to these problems, but it is computationally expensive, and the solutions it provides may be sensitive to unrealistic global properties of the models on which it is based. The ICM algorithm is faster and based on the local properties of the models underlying Bayesian image analysis; parameter estimation is performed iteratively via pseudolikelihood. Mathematical morphology is faster again and is widely considered to perform well, but lacks a statistical basis; method selection (analogous to parameter estimation) is done in a rather ad hoc manner. We propose Bayesian morphology, a synthesis of these methods that attempts to combine the speed of mathematical morphology with the principled statistical basis of ICM. The key observation is that when the original image is discrete (or if an initial segmentation has been carried out), then, assuming a Potts model for the true scene and channel transmission noise, (1) the ICM algorithm is equivalent to a form of mathematical morphology and (2) the segmentation is insensitive to the precise values of the model parameters. Unlike in standard Bayesian images analysis and ICM, it is feasible to do maximum likelihood estimation of the parameters in this setting. For gray-level or multispectral images, we propose an initial segmentation based on the EM algorithm for a mixture model of the marginal distribution of the pixels. The resulting algorithm is much faster than ICM, with gains that increase for more bands and larger images, and has good performance in experiments and for real examples.

Three‐dimensional regularized binary image reconstruction from three two‐dimensional projections using a randomized ICM algorithm

Three‐dimensional regularized binary image reconstruction from three two‐dimensional projections using a randomized ICM algorithm

Three-dimensional regularized binary image reconstruction from three two-dimensional projections using a randomized ICM algorithm

In this article, we address the problem of fully three-dimensional (3D) binary image reconstruction from three projections. The regularization approach relies on the use of a 3D Ising model which is a particular Gibbs prior. The problem is then equivalent to the minimization of a nonconvex energy function on discrete values. After an evaluation of standard optimization methods such as simulated annealing or its deterministic version ICM, we propose a new algorithm called parallel randomized ICM. It improves the reconstruction quality compared to ICM solutions while keeping reasonable reconstruction time. Its performances are evaluated from simulated projections for different 3D test images. Its application to real data is presented. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 135–146, 1998

Bayesian filtering and supervised classification in image remote sensing

In the framework of image remote sensing, Markov random fields are used to model the distribution of points both in the 2-dimensional geometrical layout of the image and in the spectral grid. The problems of image filtering and supervised classification are investigated. The mixture model of noise developed here and appropriate Gibbs densities yield a same approach and a same efficient ICM algorithm both for filtering and classifying.

An evaluation of the icm algorithm for image reconstruction

We examine the properties of Iterated Conditional Modes (ICM) estimation for a number of synthetic binary images using simulation.