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44
Hybrid Image Segmentation Using Watersheds and Fast Region Merging
 IEEE transactions on Image Processing
, 1998
"... Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate est ..."
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Cited by 88 (1 self)
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Abstract—A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and regionbased techniques through the morphological algorithm of watersheds. An edgepreserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottomup) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the socalled nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, onepixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with twodimensional/threedimensional (2D/3D) magnetic resonance images are presented. Index Terms — Image segmentation, nearest neighbor region merging, noise reduction, watershed transform. I.
Color LAR codec: a color image representation and compression scheme based on local resolution adjustment and selfextracting region representation
 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
, 2007
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A Fast Algorithm for MDLBased Multiband Image Segmentation
 Proc. IEEE Computer Vision and Pattern Recognition Conf
, 1994
"... We consider the problem of image segmentation and describe an algorithm that is based on the Minimum Description Length (MDL) principle, is fast, is applicable to multiband images, and guarantees closed regions. We construct an objective €unction that, when minimized, yields a partitioning of the ..."
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Cited by 15 (1 self)
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We consider the problem of image segmentation and describe an algorithm that is based on the Minimum Description Length (MDL) principle, is fast, is applicable to multiband images, and guarantees closed regions. We construct an objective €unction that, when minimized, yields a partitioning of the image into regions where the pixel values in each band of each region are described by a polynomial surface plus noise. The polynomial orders and their coefficients are determined by the algorithm. The minimization is difficult because (1) it involves a search over a very large space and (2) there is extensive computation required at each stage of the search. To address the first of these problems we use a regionmerging minimization algorithm. To address the second we use an incremental polynomial regression that uses computations from the previous stage to compute results in the current stage, resulting in a significant speed up over the nonincremental technique. The segmentation result obtained is suboptimal in general but of high quality. Results on real images are shown.
Energy Partitions and Image Segmentation
, 2004
"... We address the issue of lowlevel segmentation for realvalued images. The proposed approach relies on the formulation of the problem in terms of an energy partition of the image domain. In this framework, an energy is defined by measuring a pseudometric distance to a source point. Thus, the choic ..."
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Cited by 11 (1 self)
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We address the issue of lowlevel segmentation for realvalued images. The proposed approach relies on the formulation of the problem in terms of an energy partition of the image domain. In this framework, an energy is defined by measuring a pseudometric distance to a source point. Thus, the choice of an energy and a set of sources determines a tessellation of the domain. Each energy acts on the image at a different level of analysis; through the study of two types of energies, two stages of the segmentation process are addressed. The first energy considered, the path variation, belongs to the class of energies determined by minimal paths. Its application as a presegmentation method is proposed. In the second part, where the energy is induced by a ultrametric, the construction of hierarchical representations of the image is discussed.
Objectbased detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote
 Sensing
, 2006
"... In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated throu ..."
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Cited by 11 (1 self)
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In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the aid of ancillary topographic data. Image objects as minimum classification units were generated through the Fractal Net Evolution Approach (FNEA) segmentation using eCognition software. For each object, 52 features were calculated including spectral features, textures, topographic features, and geometric features. After statistically ranking the importance of these features with the classification and regression tree algorithm (CART), the most effective features for classification were used to classify the vegetation. Due to the uneven sample size for each class, we chose a nonparametric (nearest neighbor) classifier. We built a hierarchical classification scheme and selected features for each of the broadest categories to carry out the detailed classification, which significantly improved the accuracy. Pixelbased maximum likelihood classification (MLC) with comparable features was used as a benchmark in evaluating our approach. The objectbased classification approach overcame the problem of saltandpepper effects found in classification results from traditional pixelbased approaches. The method takes advantage of the rich amount of local spatial information present in the irregularly shaped objects in an image. This classification approach was successfully tested at Point Reyes National Seashore in Northern California to create a comprehensive vegetation inventory. Computerassisted classification of high spatial resolution remotely sensed imagery has good potential to substitute or augment the present groundbased inventory of National Park lands.
A Multiscale Hypothesis Testing Approach to Anomaly Detection and Localization from Noisy Tomograhic Data
 IEEE Transactions on Image Processing
, 1998
"... Abstract—In this paper, we investigate the problems of anomaly detection and localization from noisy tomographic data. These are characteristic of a class of problems that cannot be optimally solved because they involve hypothesis testing over hypothesis spaces with extremely large cardinality. Our ..."
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Cited by 10 (1 self)
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Abstract—In this paper, we investigate the problems of anomaly detection and localization from noisy tomographic data. These are characteristic of a class of problems that cannot be optimally solved because they involve hypothesis testing over hypothesis spaces with extremely large cardinality. Our multiscale hypothesis testing approach addresses the key issues associated with this class of problems. A multiscale hypothesis test is a hierarchical sequence of composite hypothesis tests that discards large portions of the hypothesis space with minimal computational burden and zooms in on the likely true hypothesis. For the anomaly detection and localization problems, hypothesis zooming corresponds to spatial zooming—anomalies are successively localized to finer and finer spatial scales. The key challenges we address include how to hierarchically divide a large hypothesis space and how to process the data at each stage of the hierarchy to decide which parts of the hypothesis space deserve more attention. To answer the former we draw on [1] and [7]–[10]. For the latter, we pose and solve a nonlinear optimization problem for a decision statistic that maximally disambiguates composite hypotheses. With no more computational complexity, our optimized statistic shows substantial improvement over conventional approaches. We provide examples that demonstrate this and quantify how much performance is sacrificed by the use of a suboptimal method as compared to that achievable if the optimal approach were computationally feasible. Index Terms—Anomaly detection, composite hypothesis testing, hypothesis zooming, nonlinear optimization, quadratic programming, tomography. I.
Multiscale, statistical anomaly detection analysis and algorithms for linearized inverse scattering problems
 Multidimensional Systems and Signal Processing
, 1997
"... Abstract. In this paper we explore the utility of multiscale and statistical techniques for detecting and characterizing the structure of localized anomalies in a medium based upon observations of scattered energy obtained at the boundaries of the region of interest. Wavelet transform techniques are ..."
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Cited by 9 (5 self)
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Abstract. In this paper we explore the utility of multiscale and statistical techniques for detecting and characterizing the structure of localized anomalies in a medium based upon observations of scattered energy obtained at the boundaries of the region of interest. Wavelet transform techniques are used to provide an efficient and physically meaningful method for modeling the nonanomalous structure of the medium under investigation. We employ decisiontheoretic methods both to analyze a variety of difficulties associated with the anomaly detection problem and as the basis for an algorithm to perform anomaly detection and estimation. These methods allow for a quantitative evaluation of the manner in which the performance of the algorithms is impacted by the amplitudes, spatial sizes, and positions of anomalous areas in the overall region of interest. Given the insight provided by this work, we formulate and analyze an algorithm for determining the number, location, and magnitudes associated with a set of anomaly structures. This approach is based upon the use of a Generalized, Mary Likelihood Ratio Test to successively subdivide the region as a means of localizing anomalous areas in both space and scale. Examples of our multiscale inversion algorithm are presented using the Born approximation of an electrical conductivity problem formulated so as to illustrate many of the features associated with similar detection problems arising in fields such as geophysical prospecting, ultrasonic imaging, and medical imaging. Key Words: 1.
Performance evaluation of image segmentation and texture extraction methods in scene analysis
 EX [gN(x)] − EX[g(x)] ≤ EX [gN(x) − g(x)] ≤ 1 � α α {gN(x)→g(x)}gN(x) − g(x)dx � �� � � α x=0 →0(dominated convergence) � α + 1 α {gN(x)�g(x)}gN(x) − g(x)dx � �� � ≤2M×PX (gN(x)�g(x))=0 � α gN(x)dx → g(x)dx x=0 12th December 2003 DRAFT 07 BER 10
, 2000
"... This thesis is available for Library use on the understanding that it is copyright material and that no quotation from this thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identified and that no material has previou ..."
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Cited by 6 (1 self)
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This thesis is available for Library use on the understanding that it is copyright material and that no quotation from this thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identified and that no material has previously been submitted and approved for the award of this degree by this or any other university.
Isophotes Selection and ReactionDiffusion Model for Object Boundaries Estimation
 IJCV
, 2002
"... This paper investigates generic regionbased segmentation schemes using areaminimization constraint and background modeling, and develops a computationally efficient framework based on level lines selection coupled with biased anisotropic diffusion. A common approach to image segmentation is to con ..."
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Cited by 6 (0 self)
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This paper investigates generic regionbased segmentation schemes using areaminimization constraint and background modeling, and develops a computationally efficient framework based on level lines selection coupled with biased anisotropic diffusion. A common approach to image segmentation is to construct a cost function whose minima yield the segmented image. This is generally achieved by competition of two terms in the cost function, one that punishes deviations from the original image and another that acts as a regularization term. We propose a variational framework for characterizing global minimizers of a particular segmentation energy that can generates irregular object boundaries in image segmentation. Our motivation comes from the observation that energy functionals are traditionally complex, for which it is usually difficult to precise global minimizers corresponding to "best" segmentations. In this paper, we prove that the set of curves that minimizes the basic energy model under concern is a subset of level lines or isophotes, i.e. the boundaries of image level sets. The connections of our approach with regiongrowing techniques, snakes and geodesic active contours are also discussed. Moreover, it is absolutely necessary to regularize isophotes delimiting object boundaries and to determine piecewise smooth or constant approximations of the image data inside the objects boundaries for vizualization and pattern recognition purposes. Thus, we have constructed a reactiondiffusion process based on the PeronaMalik anisotropic diffusion equation. In particular, a reaction term has been added to force the solution to remain close to the data inside object boundaries and to be constant in noninformative regions, that is the background region. In the overall appro...
Quality Measures for Image Segmentation Using Generated Images
 Proceedings Image and Signal Processing for Remote Sensing II (Washington: S.P.I.E
, 1995
"... To provide a quantitative measure of the quality of a segmentation of an image a "true" segmentation must be known and the differences between the two segmentations must be transformed into one or more quality values. A method is described to generate a realistic satellite image and its true segment ..."
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Cited by 5 (3 self)
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To provide a quantitative measure of the quality of a segmentation of an image a "true" segmentation must be known and the differences between the two segmentations must be transformed into one or more quality values. A method is described to generate a realistic satellite image and its true segmentation to subpixel level using ground truth data and a real image. Quality measures are described which evaluate two kinds of errors: the splitting of a real field into more than one segment and the merging of pixels from different fields into a segment. Results for various segmentation methods are discussed. 1 INTRODUCTION Segmentation is an important step in the processing of images. There are however few quantitative methods (see 11 for an overview) for determining the quality of a segmentation of an image. Usually only inspection by a human expert is used to provide a qualitative evaluation of the result of a segmentation process. In 4 and 3 statistical quality measures are used b...