A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M-estimators of location is also established. Algorithms for two low-level vision tasks, discontinuity preserving smoothing and image segmentation are described as applications. In these algorithms the only user set parameter is the resolution of the analysis, and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). While this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. In this paper, we propose a new approach to Bayesian image segmentation which directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF), and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm which is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. We also develop a computationally effcient method for unsupervised estimation of m...

Recently, a framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. In this paper, we show that this model class is also quite rich. In particular, we describe how 1-D Markov processes and 2-D Markov random fields (MRF’s) can be represented within this framework. The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to computationally efficient algorithms for statistical inference. On the other hand, 2-D MRF’s are well known to be very difficult to analyze due to their noncausal structure, and thus their use typically leads to computationally intensive algorithms for smoothing and parameter identification. In contrast, our multiscale representations are based on scale-recursive models and thus lead naturally to scale-recursive algorithms, which can be substantially more efficient computationally than those associated with MRF models. In 1-D, the multiscale representation is a generalization of the midpoint deflection construction of Brownian motion. The representation of 2-D MRF’s is based on a further generalization to a “midline ” deflection construction. The exact representations of 2-D MRF’s are used to motivate a class of multiscale approximate MRF models based on one-dimensional wavelet transforms. We demonstrate the use of these latter models in the context of texture representation and, in particular, we show how they can be used as approximations for or alternatives to well-known MRF texture models.

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

. Most segmentation algorithms are composed of several procedures: split and merge, small region elimination, boundary smoothing, : : : , each depending on several parameters. The introduction of an energy to minimize leads to a drastic reduction of these parameters. We prove that the most simple segmentation tool, the "region merging" algorithm, made according to the simplest energy, is enough to compute a local energy minimum belonging to a compact class and to achieve the job of most of the tools mentioned above. We explain why "merging" in a variational framework leads to a fast multiscale, multichannel algorithm, with a pyramidal structure. The obtained algorithm is O(n ln n), where n is the number of pixels of the picture. We apply this fast algorithm to make grey level and texture segmentation and we show experimental results. Key words. variational methods, nonnumerical algorithm, image processing, texture discrimination AMS(MOS) subject classifications. 68Q20,68U10, 1. Int...

This paper presents the Plannett system, which combines artificial neural networks to achieve expertlevel accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JARtool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date. Introduction Between 1991 and 1994, the Magellan space probe collected more than 30,000 synthetic aperture radar (SAR) images of the surface of the planet Venus, a greater amount of data than all previous planetary missions combined (Smyth et al. 1995). To a...

We describe a fully-automatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular the impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even singleecho MR images are well classified into grey matter, white matter, cerebrospinal fluid, scalpbone, and background. A simulated annealing and an iterated conditional modes implementation are presented. Index Terms Magnetic Resonance Imaging, Segmentation, Markov Random Fields I. INTRODUCTION Excellent soft-tissue contrast and high spatial resolution make magnetic resonance imaging the method for anatomical imaging in brain research. Segmentation of the MR imag...

This paper proposes a content-based temporal video segmentation system that integrates syntactic (domain-independent) and semantic (domain-dependent) features for automatic management of video data. Temporal video segmentation includes scene change detection and shot classification. The proposed scene change detection method consists of two steps: detection and tracking of semantic objects of interest specified by the user, and an unsupervised method for detection of cuts, and edit effects. Object detection and tracking is achieved using a region matching scheme, where the region of interest is defined by the boundary of the object. A new unsupervised scene change detection method based on 2-class clustering is introduced to eliminate the data dependency of threshold selection. The proposed shot classification approach relies on semantic image features and exploits domain-dependent visual properties such as shape, color, and spatial configuration of tracked semantic objects. The syste...

Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond to actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take and computer resources to handle. Nowadays, color imagery has definitely supplanted monochromatic information and computation power is no longer a limitation in processing large volumes of data. The attention has accordingly been focused in recent years on algorithms for segmentation of color images and various techniques, ofted borrowed from the background of gray-level image segmentation, have been proposed. This paper provides a review of methods advanced in the past few years for segmentation of color images.

Temporal video segmentation is the "rst step towards automatic annotation of digital video for browsing and retrieval. This article gives an overview of existing techniques for video segmentation that operate on both uncompressed and compressed video stream. The performance, relative merits and limitations of each of the approaches are comprehensively discussed and contrasted. The gradual development of the techniques and how the uncompressed domain methods were tailored and applied into compressed domain are considered. In addition to the algorithms for shot boundaries detection, the related topic of camera operation recognition is also reviewed. � 2001 Elsevier Science B.V. All rights reserved.