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32
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 459 (19 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 systematic filter selection scheme which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy ” in a window around each pixel. An unsupervised squareemr clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial adjacency information in the clustering process is also proposed. We report experiments on images with natural textures as well as artificial textures with identical 2nd and 3rdorder statistics.
Preattentive texture discrimination with early vision mechanisms
 Journal of the Optical Society of America A
, 1990
"... mechanisms ..."
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its ..."
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Cited by 207 (3 self)
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Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for kmeans clustering is Lloyd's algorithm. In this paper we present a simple and efficient implementation of Lloyd's kmeans clustering algorithm, which we call the filtering algorithm. This algorithm is very easy to implement. It differs from most other approaches in that it precomputes a kdtree data structure for the data points rather than the center points. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a datasensitive analysis of the algorithm's running time. Second, we have implemented the algorithm and performed a number of empirical studies, both on synthetically generated data and on real...
Object Detection Using Gabor Filters
 Pattern Recognition
, 1997
"... This paper pertains to the detection of objects located in complex backgrounds. A featurebased segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: A given image is passed ..."
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Cited by 40 (1 self)
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This paper pertains to the detection of objects located in complex backgrounds. A featurebased segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: A given image is passed through a bank of evensymmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy ("Gabor features"), and their spatial locations, are inputted to a squarederror clustering algorithm. This clustering algorithm yields a segmentation of the original image  it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual...
Multiresolution Histograms and their Use for Recognition
 IEEE transactions on Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—The histogram of image intensities is used extensively for recognition and for retrieval of images and video from visual databases. A single image histogram, however, suffers from the inability to encode spatial image variation. An obvious way to extend this feature is to compute the histog ..."
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Cited by 39 (0 self)
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Abstract—The histogram of image intensities is used extensively for recognition and for retrieval of images and video from visual databases. A single image histogram, however, suffers from the inability to encode spatial image variation. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image to form a multiresolution histogram. The multiresolution histogram shares many desirable properties with the plain histogram including that they are both fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the multiresolution histogram directly encodes spatial information. We describe a simple yet novel matching algorithm based on the multiresolution histogram that uses the differences between histograms of consecutive image resolutions. We evaluate it against five widely used image features. We show that with our simple feature we achieve or exceed the performance obtained with more complicated features. Further, we show our algorithm to be the most efficient and robust.
A ContiguityEnhanced KMeans Clustering Algorithm for Unsupervised Multispectral Image Segmentation
, 1997
"... The recent and continuing construction of multi and hyperspectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security intere ..."
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Cited by 27 (3 self)
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The recent and continuing construction of multi and hyperspectral imagers will provide detailed data cubes with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The reduction of this voluminous data to useful intermediate forms is necessary both for downlinking all those bits and for interpreting them. Smart onboard hardware is required, as well as sophisticated earthbound processing. A segmented image (in which the multispectral data in each pixel is classified into one of a small number of categories) is one kind of intermediate form which provides some measure of data compression. Traditional image segmentation algorithms treat pixels independently and cluster the pixels according only to their spectral information. This neglects the implicit spatial information that is available in the image. We will suggest a simple approach  a varian...
COLOUR TEXTURE ANALYSIS
"... This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using lowlevel image descriptors (such a ..."
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Cited by 24 (2 self)
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This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using lowlevel image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a microlevel using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection.
Texture Segmentation Using GaussianMarkov Random Fields and Neural Oscillator Networks
, 2001
"... We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a GaussianMarkov random fields (GMRF) model. Unlike a GMRFbased approach, our method does not employ model parameters as fe ..."
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Cited by 23 (3 self)
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We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a GaussianMarkov random fields (GMRF) model. Unlike a GMRFbased approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a twodimensional (2D) array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method. Index TermsDynamical systems, Gaussian Markov random fields, LEGION, neural networks, relaxation oscillators, texture segmentation. I.
The Analysis of a Simple kMeans Clustering Algorithm
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its nea ..."
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Cited by 19 (1 self)
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Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for kmeans clustering is Lloyd's algorithm. In this paper we present a simple and efficient implementation of Lloyd's kmeans clustering algorithm, which we call the filtering algorithm. This algorithm is very easy to implement. It differs from most other approaches in that it precomputes a kdtree data structure for the data points rather than the center points. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a datasensitive analysis of the algorithm's running time. Second, we have implemented the algorithm and performed a number of empirical studies, both on synthetically generated data and on real data from...