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Segmentation process
"... Abstract — In medical Image analysis segmentation plays an important role. Due to the noise presence in the image and the nature of the varying intensity makes segmentation process a crucial one. Some of the approaches used over this problem are region growing, watershed segmentation, global thresho ..."
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Abstract — In medical Image analysis segmentation plays an important role. Due to the noise presence in the image and the nature of the varying intensity makes segmentation process a crucial one. Some of the approaches used over this problem are region growing, watershed segmentation, global
Unsupervised texture segmentation using Gabor filters
 Pattern Recognition
"... We presenf a texture segmentation algorithm inspired by the multichannel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatialfrequency domain. We propose a s ..."
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Cited by 611 (20 self)
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We presenf a texture segmentation algorithm inspired by the multichannel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatialfrequency domain. We propose a
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 618 (14 self)
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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic
ATTENTION, INTENTIONS, AND THE STRUCTURE OF DISCOURSE
, 1986
"... In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse. In this theory, discourse structure is composed of three separate but interrelated components: the structure of the sequence of utterances (called the linguistic structure), a ..."
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Cited by 1251 (49 self)
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recognizing how the utterances of the discourse aggregate into segments, recognizing the intentions expressed in the discourse and the relationships among intentions, and tracking the discourse through the operation of the mechanisms associated with attentional state. This processing description specifies
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
 International Journal of Computer Vision
, 2001
"... In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a se ..."
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Cited by 1278 (80 self)
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In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a
Matching words and pictures
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We present a new approach for modeling multimodal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (autoannotation ..."
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Cited by 652 (39 self)
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We present a new approach for modeling multimodal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto
Recognitionbycomponents: A theory of human image understanding
 Psychological Review
, 1987
"... The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recog ..."
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Cited by 1221 (23 self)
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The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory
The Contourlet Transform: An Efficient Directional Multiresolution Image Representation
 IEEE TRANSACTIONS ON IMAGE PROCESSING
"... The limitations of commonly used separable extensions of onedimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a “true” twodimensional transform that can capture the intrinsic geometrical structure t ..."
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Cited by 509 (20 self)
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flexible multiresolution, local, and directional image expansion using contour segments, and thus it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires an order N operations for Npixel images. Furthermore, we establish a precise
The diminishing segment process
"... Let Ξ0 = [−1, 1], and define the segments Ξn recursively in the following manner: for every n = 0, 1,..., let Ξn+1 = Ξn ∩ [an+1 − 1, an+1 + 1], where the point an+1 is chosen randomly on the segment Ξn with uniform distribution. For the radius ρn of Ξn we prove that n(ρn − 1/2) converges in distribu ..."
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Cited by 1 (0 self)
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Let Ξ0 = [−1, 1], and define the segments Ξn recursively in the following manner: for every n = 0, 1,..., let Ξn+1 = Ξn ∩ [an+1 − 1, an+1 + 1], where the point an+1 is chosen randomly on the segment Ξn with uniform distribution. For the radius ρn of Ξn we prove that n(ρn − 1/2) converges
Results 1  10
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