Special Issue on Image Processing and Analysis

1

Sketch Recognition using Domain Classification

Author 1: Vasudha Vashisht Author 2: Tanupriya Choudhury Author 3: Dr. T. V. Prasad

Conceptualizing away the sketch processing details in a user interface will enable general users and domain experts to create more complex sketches. There are many domains for which sketch recognition systems are being developed. But, they entail image-processing skill if they are to handle the details of each domain, and also they are lengthy to build. The implemented system’s goal is to enable user interface designers and domain experts who may not have proficiency in sketch recognition to be able to construct these sketch systems. This sketch recognition system takes in rough sketches from user drawn with the help of mouse as its input. It then recognizes the sketch using segmentation and domain classification; the properties of the user drawn sketch and segments are searched heuristically in the domains and each figures of each domain, and finally it shows its domain, the figure name and properties. It also draws the sketch smoothly. The work is resulted through extensive research and study of many existing image processing and pattern matching algorithms.

Sketch recognition segmentation domain classification.

Abstract doi.org/10.14569/SpecialIssue.2011.010101 PDF

2

A new Optimization-Based Image Segmentation method By Particle Swarm Optimization

Author 1: Fahd M.A Mohsen Author 2: Mohiy M. Hadhoud Author 3: Khalid Amin

This paper proposes a new multilevel thresholding method segmenting images based on particle swarm optimization (PSO). In the proposed method, the thresholding problem is treated as an optimization problem, and solved by using the principle of PSO. The algorithm of PSO is used to find the best values of thresholds that can give us an appropriate partition for a target image according to a fitness function. in this paper, a new quantitative evaluation function is proposed based on the information theory. The new evaluation function is used as an objective function for the algorithm of PSO in the proposed method. Because quantitative evaluation functions deal with segmented images as a set of regions, the target image is divided into a set of regions and not to a set of classes during the different stages of our method (where a region is a group of connected pixels having the same range of gray levels). The proposed method has been tested on different images, and the experimental results demonstrate its effectiveness.

Thresholding-based segmentation Particle swarm optimization Quantitative image segmentation evaluation.

Abstract doi.org/10.14569/SpecialIssue.2011.010102 PDF

3

Image segmentation by adaptive distance based on EM algorithm

Author 1: Mohamed Ali Mahjoub Author 2: Karim Kalti

This paper introduces a Bayesian image segmentation algorithm based on finite mixtures. An EM algorithm is developed to estimate parameters of the Gaussian mixtures. The finite mixture is a flexible and powerful probabilistic modeling tool. It can be used to provide a model-based clustering in the field of pattern recognition. However, the application of finite mixtures to image segmentation presents some difficulties; especially it’s sensible to noise. In this paper we propose a variant of this method which aims to resolve this problem. Our approach proceeds by the characterization of pixels by two features: the first one describes the intrinsic properties of the pixel and the second characterizes the neighborhood of pixel. Then the classification is made on the base on adaptive distance which privileges the one or the other features according to the spatial position of the pixel in the image. The obtained results have shown a significant improvement of our approach compared to the standard version of EM algorithm.

EM algorithm image segmentation adaptive distance.

Abstract doi.org/10.14569/SpecialIssue.2011.010103 PDF

4

Modeling of neural image compression using GA and BP: a comparative approach

Author 1: G G Rajput Author 2: Vrinda Shivashetty Author 3: Manoj Kumar singh

It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate; the decompressed image gets really fuzzy or indistinguishable. To overcome problems associated with conventional methods, artificial neural networks based method can be used. Genetic algorithm is a very powerful method for solving real life problems and this has been proven by applying to number of different applications. There is lots of interest to involve the GA with ANN for various reasons at various levels. Trapping in the local minima is one of the well-known problems of gradient decent based learning in ANN. The problem can be addressed using GA algorithm. But no work has been done to evaluate the performance of both learning methods from the image compression point of view. In this paper, we investigate the performance of ANN with GA in the application of image compression for obtaining optimal set of weights. Direct method of compression has been applied with neural network to get the additive advantage for security of compressed data. The experiments reveal that the standard BP with proper parameters provide good generalize capability for compression and is much faster compared to earlier work in the literature, based on cumulative distribution function. Further, the results obtained shows that general concept about GA, it performs better over gradient decent based learning, is not applicable for image compression.

Image compression genetic algorithm neural network back propagation.

Abstract doi.org/10.14569/SpecialIssue.2011.010104 PDF

5

Clustering and Bayesian network for image of faces classification

Author 1: Khlifia Jayech Author 2: Mohamed Ali Mahjoub

In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including color and texture information to build a vector of labels for each image. Finally, we apply five variants of Bayesian networks classifiers ()aïve Bayes, Global Tree Augmented )aïve Bayes (GTA)), Global Forest Augmented )aïve Bayes (GFA)), Tree Augmented )aïve Bayes for each class (TA)), and Forest Augmented )aïve Bayes for each class (FA)) to classify the image of faces using the vector of labels. In order to validate the feasibility and effectively, we compare the results of GFA) to FA) and to the others classifiers ()B, GTA), TA)). The results demonstrate FA) outperforms than GFA), )B, GTA) and TA) in the overall classification accuracy.

face recognition clustering Bayesian network aïve Bayes TA FA .

Abstract doi.org/10.14569/SpecialIssue.2011.010105 PDF

6

Skew correction for Chinese character using Hough transform

Author 1: Tian Jipeng, Author 2: G.Hemantha Kumar Author 3: H.K. Chethan

Chinese Handwritten character recognition is an emerging field in Computer Vision and Pattern Recognition. Documents acquired through Scanner, Mobile or Camera devices are often prone to Skew and Correction of skew for such document is a major task and important factor in optical character recognition. The goal of the work is to correct skew for the documents. In this paper we have proposed a novel method for skew correction using Hough transform. The proposed approach with high precision can detect skew with large angle (-90 to +90) the experimental result reveal that the proposed method is efficient compared to well-known existing methods. The experimental results show the efficacy compared to the result of well-known existing methods.

Handwritten Chinese character Computer Vision Pattern Recognition skew detection Hough transforms.

Abstract doi.org/10.14569/SpecialIssue.2011.010106 PDF

7

Recombinant Skeleton Using Junction Points in Skeleton Based Images

Author 1: Komala Lakshmi Author 2: Dr.M.Punithavalli

We perform the task of combining two skeleton images and to produce the recombinant skeleton. We propose the recombinant skeleton algorithm to produce the recombinant skeletons. The existing skeleton representation has been taken and the merge vertex detection algorithm was used before applying the recombinant skeleton algorithm. We can design and apply this recombinant skeleton in motion detection, image matching, tracking, panorama stitching, 3D modeling and object recognition. We can generate or manufacture the true real time object from the recombinant skeleton produced. The proposed method utilize local search algorithm for junction validation. Our frame work suggests the range of possibility in getting the recombinant skeleton. The boundary is essential for any transformation hence the bamboo skeleton algorithm is deployed for computing the boundary and for storing the skeleton together with the boundary. Thus our representation is skeleton with border or outline. From this new skeleton representation the proposed recombinant is achieved.

Recombinant skeleton bamboo skeleton valance skeleton point (VSP) core skeleton point(CSP) junction skeleton points (JSP).

Abstract doi.org/10.14569/SpecialIssue.2011.010107 PDF

8

ID Numbers Recognition by Local Similarity Voting

Author 1: Shen Lu Author 2: Yanyun Qu Author 3: Yanyun Cheng Author 4: Yi Xie

This paper aims to recognize ID numbers from three types of valid identification documents in China: the first-generation ID card, the second-generation ID card and the driver license of motor vehicle. We have proposed an approach using local similarity voting to automatically recognize ID numbers. Firstly, we extract the candidate region which contains ID numbers and then locate the numbers and characters. Secondly, we recognize the numbers by an improved template matching method based on the local similarity voting. Finally, we verify the ID numbers and characters. We have applied the proposed approach to a set of about 100 images which are shot by conventional digital cameras. The experimental results have demonstrated that this approach is efficient and is robust to the change of illumination and rotation. The recognition accuracy is up to 98%.

template matching algorithm ID number recognition OCR

Abstract doi.org/10.14569/SpecialIssue.2011.010108 PDF

9

Component Localization in Face Alignment

Author 1: Yanyun Qu Author 2: Tianzhu Fang Author 3: Yanyun Cheng Author 4: Han Liu

Face alignment is a significant problem in the processing of face image, and Active Shape Model (ASM) is a popular technology for this problem. However, the initiation of the alignment strongly affects the performance of ASM. If the initiation of alignment is bad, the iteration of ASM optimization will be stuck in a local minima, and the alignment will fail. In this paper, we propose a novel approach to improve ASM by building the classifiers of the face components. We design the SVM classifiers for eyes, mouth and nose, and we use Speeded Up Robust Features(SURF) and Local Binary Pattern(LBP) feature to describe the components which are discriminative for the components than Haar-like features. The face components are firstly located by the classifiers and they indicate the initiation of the alignment. Our approach can make the iterations of ASM optimization converge fast and with the less errors. We evaluate our approach on the frontal views of upright faces of IMM dataset. The experimental results have shown that our approach outperforms the original ASM in terms of efficiency and accuracy.

face alignment ASM component localization LBP SURF

Abstract doi.org/10.14569/SpecialIssue.2011.010109 PDF

10

Fine Facet Digital Watermark (FFDW) Mining From The Color Image Using Neural Networks

Author 1: N Chenthalir Indra Author 2: Dr. E . Ramaraj

On hand watermark methods employ selective Neural Network techniques for watermark embedding efficiently. Similarity Based Superior Self Organizing Maps (SBS_SOM) a neural network algorithm for watermark generation. Host image is learned by the SBS_SOM neurons and the very fine RGB feature values are mined as digital watermark. Discrete Wavelet Transform (DWT) is used for watermark entrench. Similarity Ratio and PSNR values prove the temperament of the Fine Facet Digital Watermark (FFDW). The Proposed system affords inclusive digital watermarking system.

Similarity based Superior SOM Discrete Wavelet Transform Digital watermark embedding PSNR.

Abstract doi.org/10.14569/SpecialIssue.2011.010110 PDF

11

Automatic Image Registration Using Mexican Hat Wavelet, Invariant Moment, and Radon Transform

Author 1: Jignesh N Sarvaiya Author 2: Dr. Suprava Patnaik

Image registration is an important and fundamental task in image processing used to match two different images. Given two or more different images to be registered, image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A feature-based approach to automated imageto- image registration is presented. The characteristic of this approach is that it combines Mexican-Hat Wavelet, Invariant Moments and Radon Transform. Feature Points from both images are extracted using Mexican-Hat Wavelet and controlpoint correspondence is achieved with invariant moments. After detecting corresponding control points from reference and sensed images, to recover scaling and rotation a line and triangle is form in both images respectively and applied radon transform to register images.

Image Registration Mexican-hat wavelet Invariant Moments Radon Transform.

Abstract doi.org/10.14569/SpecialIssue.2011.010111 PDF

12

Human Face Detection under Complex Lighting Conditions

Author 1: Golam Moazzam Author 2: Ms. Rubayat Parveen Author 3: Md. Al-Amin Bhuiyan

This paper presents a novel method for detecting human faces in an image with complex backgrounds. The approach is based on visual information of the face from the template image and is commenced with the estimation of the face area in the given image. As the genetic algorithm is a computationally expensive process, the searching space for possible face regions is limited to possible facial features such as eyes, nose, mouth, and eyebrows so that the required timing is greatly reduced. In addition, the lighting effects and orientation of the faces are considered and solved in this method. Experimental results demonstrate that this face detector provides promising results for the images of individuals which contain quite a high degree of variability in expression, pose, and facial details.

Face Detection Genetic Searching Fitness Function Cross-over Mutation Roulette Wheel Selection.

Abstract doi.org/10.14569/SpecialIssue.2011.010112 PDF

13

Ear Recognition using Dual Tree Complex Wavelet Transform

Author 1: Rajesh M Bodade Author 2: Sanjay N Talbar

Since last 10 years, various methods have been used for ear recognition. This paper describes the automatic localization of an ear and it’s segmentation from the side poses of face images. In this paper, authors have proposed a novel approach of feature extraction of iris image using 2D Dual Tree Complex Wavelet Transform (2D-DT-CWT) which provides six sub-bands in 06 different orientations, as against three orientations in DWT. DT-CWT being complex, exhibits the property of shift invariance. Ear feature vectors are obtained by computing mean, standard deviation, energy and entropy of these six sub-bands of DT-CWT and three sub-bands of DWT. Canberra distance and Euclidian distance are used for matching. This method is implemented and tested on two image databases, UND database of 219 subjects from the University of Notre Dame and own database created at MCTE, of 40 subjects which is also used for online ear testing of system for access control at MCTE. False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) and Receiver’s Operating Curve (ROC) are compiled at various thresholds. The accuracy of recognition is achieved above 97 %.

Ear recognition ear detection ear biometrics DT-CWT complex wavelet transform Biometrics Pattern Recognition Security Image Processing Bioinformatics Computer vision.

Abstract doi.org/10.14569/SpecialIssue.2011.010113 PDF

14

SUCCESeR: Simple and Useful Multi Color Concepts for Effective Search and Retrieval

Author 1: Satishkumar L Varma Author 2: Sanjay N. Talbar

The image quality depends on level of intensities used in images. Image is consists of various types of objects. Objects in the images are distinguishable because of various intensity levels used. The concentration of intensity levels so called energy can be extracted from image using discrete cosine transform (DCT). In this paper we apply DCT 8x8 block coefficients separately on three different color planes of three different color models namely RGB, HSV and YCbCr. The different elements of ten DCT coefficient matrices are used to form feature vectors. The different feature vectors are formed using these ten elements. These feature vectors are used to index all images in the database. The system was tested with Coral Image database containing 1000 natural images having 10 different classes of images. The image retrieval using these indices is giving comparatively better results.

color model discrete cosine transform image indexing image retrieval.

Abstract doi.org/10.14569/SpecialIssue.2011.010114 PDF

15

Automatic License Plate Localization Using Intrinsic Rules Saliency

Author 1: Chirag N Paunwala Author 2: Dr. Suprava Patnaik

This paper addresses an intrinsic rule-based license plate localization (LPL) algorithm. It first selects candidate regions, and then filters negative regions with statistical constraints. Key contribution is assigning image inferred weights to the rules leading to adaptability in selecting saliency feature, which then overrules other features and the collective measure, decides the estimation. Saliency of rules is inherent to the frame under consideration hence all inevitable negative effects present in the frame are nullified, incorporating great deal of flexibility and more generalization. Situations considered for simulation, to claim that the algorithm is better generalized are, variations in illumination, skewness, aspect ratio and hence the LP font size, vehicle size, pose, partial occlusion of vehicles and presence of multiple plates. Proposed method allows parallel computation of rules, hence suitable for real time application. The mixed data set has 697 images of almost all varieties. We achieve a Miss Rate (MR) = 4% and False Detection Rate (FDR) = 5.95% in average. Also we have implemented skew correction of the above detected LPs necessary for efficient character detection.This paper addresses an intrinsic rule-based license plate localization (LPL) algorithm. It first selects candidate regions, and then filters negative regions with statistical constraints. Key contribution is assigning image inferred weights to the rules leading to adaptability in selecting saliency feature, which then overrules other features and the collective measure, decides the estimation. Saliency of rules is inherent to the frame under consideration hence all inevitable negative effects present in the frame are nullified, incorporating great deal of flexibility and more generalization. Situations considered for simulation, to claim that the algorithm is better generalized are, variations in illumination, skewness, aspect ratio and hence the LP font size, vehicle size, pose, partial occlusion of vehicles and presence of multiple plates. Proposed method allows parallel computation of rules, hence suitable for real time application. The mixed data set has 697 images of almost all varieties. We achieve a Miss Rate (MR) = 4% and False Detection Rate (FDR) = 5.95% in average. Also we have implemented skew correction of the above detected LPs necessary for efficient character detection.

License plate localization Salient rules Connected Region Analysis statistical inconsistency skew correction.

Abstract doi.org/10.14569/SpecialIssue.2011.010115 PDF

16

Performance Comparison of SVM and K-NN for Oriya Character Recognition

Author 1: Sanghamitra Mohanty Author 2: Himadri Nandini Das Bebartta

Image classification is one of the most important branch of Artificial intelligence; its application seems to be in a promising direction in the development of character recognition in Optical Character Recognition (OCR). Character recognition (CR) has been extensively studied in the last half century and progressed to the level, sufficient to produce technology driven applications. "ow the rapidly growing computational power enables the implementation of the present CR methodologies and also creates an increasing demand on many emerging application domains, which require more advanced methodologies. Researchers for the recognition of Indic Languages and scripts are comparatively less with other languages. There are lots of different machine learning algorithms used for image classification nowadays. In this paper, we discuss the characteristics of some classification methods such as Support Vector Machines (SVM) and K-"earest "eighborhood (K-"") that have been applied to Oriya characters. We will discuss the performance of each algorithm for character classification based on drawing their learning curve, selecting parameters and comparing their correct rate on different categories of Oriya characters. It has been observed that Support Vector Machines outperforms among both the classifiers.

Recognition Features earest eighbors Support Vectors.

Abstract doi.org/10.14569/SpecialIssue.2011.010116 PDF

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