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66
Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies
, 1999
"... Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scalespace behavior of intensity extrema, edges, intensity ridges, and greylevel blobs. In this paper, w ..."
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Cited by 45 (0 self)
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Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scalespace behavior of intensity extrema, edges, intensity ridges, and greylevel blobs. In this paper, we investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scalebased hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scalespace. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.
Wavelets for a Vision
, 1996
"... Early on, computer vision researchers have realized that multiscale transforms are important to analyze the information content of images. The wavelet theory gives a stable mathematical foundation to understand the properties of such multiscale algorithms. This tutorial describes major applications ..."
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Cited by 43 (0 self)
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Early on, computer vision researchers have realized that multiscale transforms are important to analyze the information content of images. The wavelet theory gives a stable mathematical foundation to understand the properties of such multiscale algorithms. This tutorial describes major applications to multiresolution search, multiscale edge detection and texture discrimination.
Geometric Heat Equation and Nonlinear Diffusion of Shapes and Images
 Computer Vision and Image Understanding
, 1993
"... We propose a geometric smoothing method based on local curvature in shapes and images which is governed by the geometric heat equation and is a special case of the reactiondiffusion framework proposed by [28]. For shapes, the approach is analogous to the classical heat equation smoothing, but with ..."
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Cited by 40 (5 self)
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We propose a geometric smoothing method based on local curvature in shapes and images which is governed by the geometric heat equation and is a special case of the reactiondiffusion framework proposed by [28]. For shapes, the approach is analogous to the classical heat equation smoothing, but with a renormalization by arclength at each infinitesimal step. For images, the smoothing is similar to anisotropic diffusion in that, since the component of diffusion in the direction of the brightness gradient is nil, edge location and sharpness are left intact. We present several properties of curvature deformation smoothing of shape: it preserves inclusion order, annihilates extrema and inflection points without creating new ones, decreases total curvature, satisfies the semigroup property allowing for local iterative computations, etc. Curvature deformation smoothing of an image is based on viewing it as a collection of isointensity level sets, each of which is smoothed by curvature and the...
A Categorization of Multiscaledecompositionbased Image Fusion Schemes with a Performance Study for a Digital Camera Application
 Proceedings of the IEEE
, 1999
"... The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for human and machine perception or further image processing tasks. In this paper, a generic image fusion framework based on multiscale ..."
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Cited by 39 (2 self)
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The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for human and machine perception or further image processing tasks. In this paper, a generic image fusion framework based on multiscale decomposition is studied. This framework provides freedom to choose different multiscale decomposition methods and different fusion rules. The framework includes all of the existing multiscaledecompositionbased fusion approaches we found in the literature which did not assume a statistical model for the source images. Different image fusion approaches are investigated based on this framework. Some evaluation measures are suggested and applied to compare the performance of these fusion schemes for a digital camera application. The comparisons indicate that our framework includes some new approaches which outperform the existing approaches for the cases we consider. 1 Introduction There ha...
Image Processing with Multiscale Stochastic Models
, 1993
"... In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a twosweep algorithm for estimation. A ..."
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Cited by 29 (3 self)
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In this thesis, we develop image processing algorithms and applications for a particular class of multiscale stochastic models. First, we provide background on the model class, including a discussion of its relationship to wavelet transforms and the details of a twosweep algorithm for estimation. A multiscale model for the error process associated with this algorithm is derived. Next, we illustrate how the multiscale models can be used in the context of regularizing illposed inverse problems and demonstrate the substantial computational savings that such an approach offers. Several novel features of the approach are developed including a technique for choosing the optimal resolution at which to recover the object of interest. Next, we show that this class of models contains other widely used classes of statistical models including 1D Markov processes and 2D Markov random fields, and we propose a class of multiscale models for approximately representing Gaussian Markov random fields...
Scalespace derived from Bsplines
 IEEE Trans. Pattern Anal. Machine Intell
, 1998
"... Abstract—It is wellknown that the linear scalespace theory in computer vision is mainly based on the Gaussian kernel. The purpose of the paper is to propose a scalespace theory based on Bspline kernels. Our aim is twofold. On one hand, we present a general framework and show how Bsplines provid ..."
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Cited by 23 (8 self)
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Abstract—It is wellknown that the linear scalespace theory in computer vision is mainly based on the Gaussian kernel. The purpose of the paper is to propose a scalespace theory based on Bspline kernels. Our aim is twofold. On one hand, we present a general framework and show how Bsplines provide a flexible tool to design various scalespace representations: continuous scalespace, dyadic scalespace frame, and compact scalespace representation. In particular, we focus on the design of continuous scalespace and dyadic scalespace frame representation. A general algorithm is presented for fast implementation of continuous scalespace at rational scales. In the dyadic case, efficient frame algorithms are derived using Bspline techniques to analyze the geometry of an image. Moreover, the image can be synthesized from its multiscale local partial derivatives. Also, the relationship between several scalespace approaches is explored. In particular, the evolution of wavelet theory from traditional scalespace filtering can be well understood in terms of Bsplines. On the other hand, the behavior of edge models, the properties of completeness, causality, and other properties in such a scalespace representation are examined in the framework of Bsplines. It is shown that, besides the good properties inherited from the Gaussian kernel, the Bspline derived scalespace exhibits many advantages for modeling visual mechanism with regard to the efficiency, compactness, orientation feature, and parallel structure. Index Terms—Image modeling, Bspline, wavelet, scalespace, scaling theorem, fingerprint theorem.
Variational Image Segmentation Using Boundary Functions
 IEEE Transactions on Image Processing
, 1996
"... A general variational framework for image approximation and segmentation is introduced. By using a continuous "lineprocess" to represent edge boundaries, it is possible to formulate a variational theory of image segmentation and approximation in which the boundary function has a simple explicit for ..."
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Cited by 17 (3 self)
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A general variational framework for image approximation and segmentation is introduced. By using a continuous "lineprocess" to represent edge boundaries, it is possible to formulate a variational theory of image segmentation and approximation in which the boundary function has a simple explicit form in terms of the approximation function. At the same time, this variational framework is general enough to include the most commonly used objective functions. Application is made to MumfordShah type functionals as well as those considered by Geman and others. Employing arbitrary LLL ppp norms to measure smoothness and approximation allows the user to alternate between a least squares approach and one based on total variation, depending on the needs of a particular image. Since the optimal boundary function that minimizes the associated objective functional for a given approximation function can be found explicitly, the objective functional can be expressed in a reduced form that depends ...
A waveletbased method for action potential detection from extracellular neural signal recording with low signaltonoise ratio
 IEEE Trans Biomed Eng
"... Abstract—We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signaltonoise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this pro ..."
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Cited by 13 (1 self)
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Abstract—We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signaltonoise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this problem and, thus, in previous studies on experimental neurophysiology, only action potentials with sufficiently large amplitude have been detected and analyzed. In order to lower the level of SNR required for successful detection, we propose an action potential detector based on a prudent combination of wavelet coefficients of multiple scales and demonstrate its performance for neural signal recording with varying degrees of similarity between signal and noise. The experimental data include recordings from the rat somatosensory cortex, the giant medial nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed method was tested for various SNR values and degrees of spectral similarity. The method was superior to the Teager energy operator and even comparable to or better than the optimal linear detector. A detection ratio higher than 80 % at a false alarm ratio lower than 10 % was achieved, under an SNR of 2.35 for the rat cortex data where the spectral similarity was very high. Index Terms—Action potential detection, extracellular neural signal recording, signaltonoise ratio, Teager energy operator, wavelet transform. I.
The Computational Study of Vision
 Foundations of Cognitive Science
, 1988
"... Through vision, we derive a rich understanding... This article reviews some computational studies of vision, focusing on edge detection, binocular stereo, motion analysis, intermediate vision and object recognition. ..."
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Cited by 12 (1 self)
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Through vision, we derive a rich understanding... This article reviews some computational studies of vision, focusing on edge detection, binocular stereo, motion analysis, intermediate vision and object recognition.
Binarization of document images using Hadamard multiresolution analysis
 ICDAR’99: Intl. Conf. Document Anal. Recog
, 1999
"... In this article, we propose a new method that combines the use of a global threshold and a windowbased scheme for computing local thresholds. The latter scheme compares the contrast of gray values within a neighborhood whose size varies with the scale of the objects being examined. To compute the s ..."
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Cited by 8 (2 self)
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In this article, we propose a new method that combines the use of a global threshold and a windowbased scheme for computing local thresholds. The latter scheme compares the contrast of gray values within a neighborhood whose size varies with the scale of the objects being examined. To compute the scale quantity, a new wavelet model entitled Hadamard multiresolution analysis is also proposed. When the windowbased scheme is applied to the areas where global threshold is likely to fail, we obtain uniformly better binary results than using a global threshold only. Significant improvements to OCR performance can also be achieved by our binary results.