The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Marzuki Khalid - One of the best experts on this subject based on the ideXlab platform.
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using Gabor Filters as image multiplier for tropical wood species recognition system
International Conference on Computer Modelling and Simulation, 2010Co-Authors: Rubiyah Yusof, Nenny Ruthfalydia Rosli, Marzuki KhalidAbstract:One of the main problems in wood species recognition systems is the lack of discriminative features of the texture images. In order to overcome this, we use Gabor filter in the pre-processing stage of the wood texture image to multiply the number of features for a single image, thus providing more information for feature extractor to capture. The textural wood features are extracted using two feature extraction methods which are co-occurrence matrix approach, known as grey level co-occurrence matrix (GLCM) and also Gabor Filters to generate more variation of features and to improve the accuracy rate. The combined features extracted from GLCM and Gabor Filters are sent to the classifier module. A multi-layer neural network based on the popular back propagation (MLBP) algorithm is used for classification. The results show that increasing the number of features by using Gabor Filters as image multiplier and the combination of features from Gabor Filters and GLCM feature extractors improved the accuracy rate of the wood species recognition system.
Rubiyah Yusof - One of the best experts on this subject based on the ideXlab platform.
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tropical wood species recognition system based on Gabor filter as image multiplier
Signal-Image Technology and Internet-Based Systems, 2013Co-Authors: Rubiyah Yusof, Nenny Ruthfalydia RosliAbstract:The main problem in wood species recognition system is the lack of discriminative features of the texture images. Some of the wood species have similar patterns with others and some have different patterns even though they are of the same species. Moreover, the growth rings for tropical wood changes slightly due seasonal changes in climate. One of the ways to improve the system is by providing more features representation of each species. In this work, Gabor filter is proposed to generate multiple processed images from a single image so that more features can be extracted and trained by the neural network. After the raw image has been sharpened and contrast enhancement has been applied at the preprocessing stage, the image will be convolved with Gabor Filters. The output of the convolution generates Gabor images which are images extracted based on frequency and spatial information of the original images. These Gabor images will be used by grey level co-occurrence matrix (GLCM) for feature extraction. A multi-layer neural network based on popular back-propagation (MLBP) algorithm is used for classification. The result shows that increasing the number of features by means of Gabor Filters as well as the right combination of Gabor Filters increases the accuracy rate of the system.
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using Gabor Filters as image multiplier for tropical wood species recognition system
International Conference on Computer Modelling and Simulation, 2010Co-Authors: Rubiyah Yusof, Nenny Ruthfalydia Rosli, Marzuki KhalidAbstract:One of the main problems in wood species recognition systems is the lack of discriminative features of the texture images. In order to overcome this, we use Gabor filter in the pre-processing stage of the wood texture image to multiply the number of features for a single image, thus providing more information for feature extractor to capture. The textural wood features are extracted using two feature extraction methods which are co-occurrence matrix approach, known as grey level co-occurrence matrix (GLCM) and also Gabor Filters to generate more variation of features and to improve the accuracy rate. The combined features extracted from GLCM and Gabor Filters are sent to the classifier module. A multi-layer neural network based on the popular back propagation (MLBP) algorithm is used for classification. The results show that increasing the number of features by using Gabor Filters as image multiplier and the combination of features from Gabor Filters and GLCM feature extractors improved the accuracy rate of the wood species recognition system.
Nenny Ruthfalydia Rosli - One of the best experts on this subject based on the ideXlab platform.
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tropical wood species recognition system based on Gabor filter as image multiplier
Signal-Image Technology and Internet-Based Systems, 2013Co-Authors: Rubiyah Yusof, Nenny Ruthfalydia RosliAbstract:The main problem in wood species recognition system is the lack of discriminative features of the texture images. Some of the wood species have similar patterns with others and some have different patterns even though they are of the same species. Moreover, the growth rings for tropical wood changes slightly due seasonal changes in climate. One of the ways to improve the system is by providing more features representation of each species. In this work, Gabor filter is proposed to generate multiple processed images from a single image so that more features can be extracted and trained by the neural network. After the raw image has been sharpened and contrast enhancement has been applied at the preprocessing stage, the image will be convolved with Gabor Filters. The output of the convolution generates Gabor images which are images extracted based on frequency and spatial information of the original images. These Gabor images will be used by grey level co-occurrence matrix (GLCM) for feature extraction. A multi-layer neural network based on popular back-propagation (MLBP) algorithm is used for classification. The result shows that increasing the number of features by means of Gabor Filters as well as the right combination of Gabor Filters increases the accuracy rate of the system.
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using Gabor Filters as image multiplier for tropical wood species recognition system
International Conference on Computer Modelling and Simulation, 2010Co-Authors: Rubiyah Yusof, Nenny Ruthfalydia Rosli, Marzuki KhalidAbstract:One of the main problems in wood species recognition systems is the lack of discriminative features of the texture images. In order to overcome this, we use Gabor filter in the pre-processing stage of the wood texture image to multiply the number of features for a single image, thus providing more information for feature extractor to capture. The textural wood features are extracted using two feature extraction methods which are co-occurrence matrix approach, known as grey level co-occurrence matrix (GLCM) and also Gabor Filters to generate more variation of features and to improve the accuracy rate. The combined features extracted from GLCM and Gabor Filters are sent to the classifier module. A multi-layer neural network based on the popular back propagation (MLBP) algorithm is used for classification. The results show that increasing the number of features by using Gabor Filters as image multiplier and the combination of features from Gabor Filters and GLCM feature extractors improved the accuracy rate of the wood species recognition system.
Gerald Sommer - One of the best experts on this subject based on the ideXlab platform.
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Gabor wavelet networks for object representation
Lecture Notes in Computer Science, 2000Co-Authors: Volker Kruger, Gerald SommerAbstract:In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor Filters ensured that geometrical and textural object features are encoded. The feasibility of the Gabor Filters as a model for local object features ensures a considerable data reduction while at the same time allowing any desired precision of the object representation ranging from a sparse to a photo-realistic representation. In the second part of the paper we will present an approach for the estimation of a head pose that is based on the Gabor wavelet networks.
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efficient head pose estimation with Gabor wavelet networks
British Machine Vision Conference, 2000Co-Authors: Volker Kruger, Gerald SommerAbstract:In this article we want to introduce first the Gabor wavelet network as a model based approach for an effective and efficient object representation. The Gabor wavelet network has several advantages such as invariance to some degree with respect to translation, rotation and dilation. Furthermore, the use of Gabor Filters ensured that geometrical and textural object features are encoded. The feasibility of the Gabor Filters as a model for local object features ensures a considerable data reduction while at the same time allowing any desired precision of the object representation ranging from a sparse to a photo-realistic representation. In the second part of the paper we will present an approach for the estimation of a head pose that is based on the Gabor wavelet networks.
Anil K. Jain - One of the best experts on this subject based on the ideXlab platform.
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object detection using Gabor Filters
Pattern Recognition, 1997Co-Authors: Anil K. Jain, Nalini K Ratha, Sridhar LakshmananAbstract:Abstract This paper pertains to the detection of objects located in complex backgrounds. A feature-based 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 even-symmetric 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 squared-error 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 and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of “Gabor features” is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, fingerprint analysis, and image database query.
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address block location on envelopes using Gabor Filters
Pattern Recognition, 1992Co-Authors: Anil K. Jain, Sushil BhattacharjeeAbstract:Abstract The large volume of mail and the increased cost of handling it has made postal automation an important domain for pattern recognition and computer vision research. A substantial amount of work is being done to design an automatic mail sorting system which can read and interpret the destination address on a mail piece and direct it to the appropriate bin. Robust optical character recognition (OCR) systems are now available which can read printed characters with great accuracy (> 99%). But, in order to read the destination address, the region in the image containing the address must first be located. Even though several approaches to address block location have been proposed in the literature, it remains a difficult problem. A simple method is presented for automatically identifying regions in envelope images which are candidates for being the destination address. The envelope image is considered to contain different textured regions, one of which corresponds to the text-content in the image. Thus, a texture-based segmentation method is used to identify the regions of text in the image. The method for texture discrimination is based on Gabor Filters which have been successfully used earlier for a variety of texture classification and segmentation tasks. It is shown that only a small number of even-symmetric Gabor Filters are needed in this application. The success of the texture-based segmentation algorithm for identifying address blocks is demonstrated on a number of test images. These results also demonstrate the invariance of the method to the orientation of text in the envelope image and the variations in the size and font of the text.
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text segmentation using Gabor Filters for automatic document processing
Machine Vision Applications, 1992Co-Authors: Anil K. Jain, Sushil BhattacharjeeAbstract:There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor Filters are used to extract texture features for each of these regions. These Filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same Filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique.
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Unsupervised texture segmentation using Gabor Filters
Pattern Recognition, 1991Co-Authors: Anil K. Jain, Farshid FarrokhniaAbstract:This paper presents a texture segmentation algorithm inspired by the multi-channel 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 spatial-frequency domain, and a systematic filter selection scheme is proposed, 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. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the "true" number of texture categories. © 1991.