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Physical Sciences and Mathematics Commons™

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Selected Works

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2012

Associate Professor Golshah Naghdy

Articles 1 - 18 of 18

Full-Text Articles in Physical Sciences and Mathematics

Signature-Based Document Retrieval, A. Chalechale, G. Naghdy, Alfred Mertins Nov 2012

Signature-Based Document Retrieval, A. Chalechale, G. Naghdy, Alfred Mertins

Associate Professor Golshah Naghdy

This paper presents a new approach for document image decomposition and retrieval based on connected component analysis and geometric properties of the labeled regions. The database contains document images with Arabic/Persian text combined with English text, headlines, ruling lines, trademark and signature. In particular, Arabic/Persian signature extraction is investigated using special characteristics of the signature that is fairly different from English signatures. A set of efficient, invariant and compact features is extracted for validation purposes using angular-radial partitioning of the signature region. Experimental results show the robustness of the proposed method.

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Edge Image Description Using Angular Radial Partitioning, A. Chalechale, Alfred Mertins, G. Naghdy Nov 2012

Edge Image Description Using Angular Radial Partitioning, A. Chalechale, Alfred Mertins, G. Naghdy

Associate Professor Golshah Naghdy

The authors present a novel approach for image representation based on geometric distribution of edge pixels. Object segmentation is not needed, therefore the input image may consist of several complex objects. For an efficient description of an arbitrary edge image, the edge map is divided into M/spl times/N angular radial partitions and local features are extracted for these partitions. The entire image is then described as a set of spatially distributed invariant feature descriptors using the magnitude of the Fourier transform. The approach is scale- and rotation-invariant and tolerates small translations and erosions. The extracted features are characterised by their …

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Chain-Based Extraction Of Line Segments To Describe Images, A. Chalechale, G. Naghdy, Prashan Premaratne, H. Moghaddasi Nov 2012

Chain-Based Extraction Of Line Segments To Describe Images, A. Chalechale, G. Naghdy, Prashan Premaratne, H. Moghaddasi

Associate Professor Golshah Naghdy

This work presents a novel fast method for line segment extraction, based on a chain code representation of edge maps. It has a parallel nature and can be employed on parallel machines. In the first phase it breaks the macro chains into several micro chains after applying shifting, smoothing and differentiating. The micro chains are then approximated by straight line segments. In the second phase, based on the length and the error criteria, the line segments are grouped into much longer lines. The experimental results show a significant improvement in the number of line segments extracted while their accumulative length …

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Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona Nov 2012

Wavelet-Based Feature-Adaptive Adaptive Resonance Theory Neural Network For Texture Identification, Jiazhao Wang, Golshah Naghdy, Philip Ogunbona

Associate Professor Golshah Naghdy

A new method of texture classification comprising two processing stages, namely a low-level evolutionary feature extraction based on Gabor wavelets and a high-level neural network based pattern recognition, is proposed. The design of these stages is motivated by the processes involved in the human visual system: low-level receptors responsible for early vision processing and the high-level cognition. Gabor wavelets are used as extractors of ‘‘lowlevel’’ features that feed the feature-adaptive adaptive resonance theory (ART) neural network acting as a high-level ‘‘cognitive system.’’ The novelty of the model developed in this paper lies in the use of a self-organizing input layer …

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Scalable Multiresolution Image Segmentation And Its Application In Video Object Extraction Algorithm, F. Akhlaghian Tab, G. Naghdy, Alfred Mertins Nov 2012

Scalable Multiresolution Image Segmentation And Its Application In Video Object Extraction Algorithm, F. Akhlaghian Tab, G. Naghdy, Alfred Mertins

Associate Professor Golshah Naghdy

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modelling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it suitable for the scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multiresolution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, …

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Automatic Image Annotation For Semantic Image Retrieval, Wenbin Shao, G. Naghdy, Son Lam Phung Nov 2012

Automatic Image Annotation For Semantic Image Retrieval, Wenbin Shao, G. Naghdy, Son Lam Phung

Associate Professor Golshah Naghdy

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor …

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Automatic Annotation Of Digital Images Using Colour Structure And Edge Direction, Wenbin Shao, G. Naghdy, Son Lam Phung Nov 2012

Automatic Annotation Of Digital Images Using Colour Structure And Edge Direction, Wenbin Shao, G. Naghdy, Son Lam Phung

Associate Professor Golshah Naghdy

The focus of this paper is on automatic annotation for semantic image retrieval. This work is aimed at identifying visual descriptors that are most relevant, effective and suitable for semantic annotation tasks. We propose an image annotation system based on support vector machines and a combination of descriptors that includes a gradient direction histogram and several MPEG-7 visual descriptors. The system is tested on a large database of 7200 cityscape and landscape images. The results indicate that when descriptors are used individually, the proposed gradient direction histogram performs best. However, when descriptors are combined, the accuracy is improved. The presented …

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Document Image Analysis And Verification Using Cursive Signature, A. Chalechale, G. Naghdy, Prashan Premaratne, Alfred Mertins Nov 2012

Document Image Analysis And Verification Using Cursive Signature, A. Chalechale, G. Naghdy, Prashan Premaratne, Alfred Mertins

Associate Professor Golshah Naghdy

A new approach for document image analysis and verification is presented. The approach utilizes connected component analysis and geometric properties of labelled regions for region of interest extraction. Document images containing Persian/Arabic text combined with English text, headlines, ruling lines, trade mark and cursive signature are used as a test data. Persian/Arabic signature extraction is investigated as a case study. The proposed method uses special characteristics of such signatures for extraction and verification procedures. A set of efficient, invariant and compact features is extracted utilizing spatial partitioning of the signature region. Comparative results exhibit high extraction and verification rates.

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New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona Nov 2012

New Wavelet Based Art Network For Texture Classification, Jiazhao Wang, Golshah Naghdy, Philip O. Ogunbona

Associate Professor Golshah Naghdy

A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory (Fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the Fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using …

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3d Geometric Modelling Of Hand-Woven Textile, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy Nov 2012

3d Geometric Modelling Of Hand-Woven Textile, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy

Associate Professor Golshah Naghdy

Geometric modeling and haptic rendering of textile has attracted significant interest over the last decade. A haptic representation is created by adding the physical properties of an object to its geometric configuration. While research has been conducted into geometric modeling of fabric, current systems require time-consuming manual recognition of textile specifications and data entry. The development of a generic approach for construction of the 3D geometric model of a woven textile is pursued in this work. The geometric model would be superimposed by a haptic model in the future work. The focus at this stage is on hand-woven textile artifacts …

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Scalable Multiresolution Color Image Segmentation With Smoothness Constraint, F. Akhlaghian Tab, G. Naghdy, Alfred Mertins Nov 2012

Scalable Multiresolution Color Image Segmentation With Smoothness Constraint, F. Akhlaghian Tab, G. Naghdy, Alfred Mertins

Associate Professor Golshah Naghdy

This paper presents a multiresolution image segmentation method based on the discrete wavelet transform and Markov random field (MRF) modeling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it applicable for scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multire solution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Examining the corresponding pixels at different resolutions simultaneously enables the algorithm to directly segment the images in the YUV or similar …

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Arabic/Persian Cursive Signature Recognition And Verification Using Line Segment Distribution, A. Chalechale, G. Naghdy, Prashan Premaratne Nov 2012

Arabic/Persian Cursive Signature Recognition And Verification Using Line Segment Distribution, A. Chalechale, G. Naghdy, Prashan Premaratne

Associate Professor Golshah Naghdy

This work proposes a fast method for line segment extraction based on chain code differentiation. It is applied to cursive signature recognition of Arabic/Persian. The evaluation method is introduced to obtain a quantitative value for the recognition rate. The comparative results show the existing differences among the methods in recognition, building time and searching time criteria. The two methods used for comparison are invariant moments and CBLSE method.

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Sketch-Based Image Retrieval Using Angular Partitioning, A. Chalechale, G. Naghdy, Alfred Mertins Nov 2012

Sketch-Based Image Retrieval Using Angular Partitioning, A. Chalechale, G. Naghdy, Alfred Mertins

Associate Professor Golshah Naghdy

This paper presents a novel approach for sketch-based image retrieval based on low-level features. The approach enables measuring the similarity between a full color image and a simple black and white sketched query and needs no cost intensive image segmentation. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Abstract images are obtained using strong edges of the model image and thinned outline of the sketched image. Angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using Fourier transform. The extracted features are scale and …

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Image Analysis Using Line Segments Extraction By Chain Code Differentiation, A. Chalechale, G. Naghdy, Prashan Premaratne Nov 2012

Image Analysis Using Line Segments Extraction By Chain Code Differentiation, A. Chalechale, G. Naghdy, Prashan Premaratne

Associate Professor Golshah Naghdy

This paper proposes a new fast method for line segment extraction from edge maps. It has a parallel nature and can be used on parallel machines easily. The method uses the chain codes in the edge map, namely macrochains, for line segment detection. In the first phase, it breaks the macrochains into several microchains by employing the extreme points of the first derivative of shifted-smoothed chain code function. Straight-line segments approximate the resulting microchains. In the second phase, the line segments are grouped together based on their proximity (collinearity and nearness) to make longer segments. The final set could be …

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Sketch-Based Image Matching Using Angular Partitioning, A. Chalechale, G. Naghdy, Alfred Mertins Nov 2012

Sketch-Based Image Matching Using Angular Partitioning, A. Chalechale, G. Naghdy, Alfred Mertins

Associate Professor Golshah Naghdy

This work presents a novel method for image similarity measure, where a hand-drawn rough black and white sketch is compared with an existing data base of full color images (art works and photographs). The proposed system creates ambient intelligence in terms of the evaluation of nonprecise, easy to input sketched information. The system can then provide the user with options of either retrieving similar images in the database or ranking the quality of the sketch against a given standard, i.e., the original image model. Alternatively, the inherent pattern-matching capability of the system can be utilized to allow detection of distortion …

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Texture Analysis Using Gabor Wavelets, Golshah Naghdy, Jianli Wang, Philip Ogunbona Nov 2012

Texture Analysis Using Gabor Wavelets, Golshah Naghdy, Jianli Wang, Philip Ogunbona

Associate Professor Golshah Naghdy

Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for …

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A Multi-Class Image Classification System Using Salient Features And Support Vector Machines, Wenbin Shao, Son Lam Phung, G. Naghdy Nov 2012

A Multi-Class Image Classification System Using Salient Features And Support Vector Machines, Wenbin Shao, Son Lam Phung, G. Naghdy

Associate Professor Golshah Naghdy

This paper addresses the problem of automatic image annotation for semantic retrieval of images. We propose an image classification system that is capable of recognizing several image categories. The system is based on the support vector machine and a set of image features that includes MPEG-7 visual descriptors and a custom feature. The system is evaluated on a large dataset consisting of 14400 images in four categories - landscape, cityscape, vehicle and portrait. We find that the proposed edge direction histogram and the MPEG-7 edge histogram perform better than other features in this application. Experiment results indicate that the pair- …

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Image Database Retrieval Using Sketched Queries, A. Chalechale, G. Naghdy, Prashan Premaratne Nov 2012

Image Database Retrieval Using Sketched Queries, A. Chalechale, G. Naghdy, Prashan Premaratne

Associate Professor Golshah Naghdy

This paper presents a novel approach for sketch-based image retrieval based on low-level features. It enables the measuring of the similarity among full color multi-component images within a database (models) and simple black and white user sketched queries. It needs no cost intensive image segmentation. Strong edges of the model image and morphologically thinned version of the query image are used for image abstraction. Angular-radial decomposition of pixels in the abstract images is used to extract new compact and affine invariant features. Comparative results, employing an art database (ArT BANK), show significant improvement in average normalized modified retrieval rank (ANMRR) …

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