ICTACT Journal on Image and Video Processing

A. A. Haseena Thasneem ¹, R. Mehaboobathunnisa ¹, M. Mohammed Sathik ¹, S. Arumugam ²

Affiliations
1 Department of Computer Science, Sadakathullah Appa College, IN
2 Nandha Engineering College, IN

Abstract

This paper compares different algorithms for the segmentation of skin lesions in dermoscopic images. The basic segmentation algorithms compared are Thresholding techniques (Global and Adaptive), Region based techniques (K-means, Fuzzy C means, Expectation Maximization and Statistical Region Merging), Contour models (Active Contour Model and Chan - Vese Model) and Spectral Clustering. Accuracy, sensitivity, specificity, Border error, Hammoude distance, Hausdorff distance, MSE, PSNR and elapsed time metrices were used to evaluate various segmentation techniques.

Keywords

Thresholding, Expectation Maximization, Contour Models, Dermoscopy, Spectral Clustering.

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K. Krishneswari ¹, S. Arumugam ²

Affiliations
1 Department of Computer Science and Engineering, Tamilnadu College of Engineering, IN
2 Nandha Educational Institutions, IN

Abstract

Palmprint recognition has attracted various researchers in recent years due to its richness in amount of features. In feature extraction, the single feature has become bottleneck in producing high performance. To solve this we propose an intramodal feature fusion for palmprint authentication. The proposed system extracts multiple features like Texture (Gabor), and Line features from the preprocessed palmprint images. The feature vectors obtained from different approaches are incompatible and also the features from same image may be redundant. Therefore, we propose a Particle Swarm Optimization (PSO) based technique to perform feature fusion on extracted features. Being an iterative technique that randomly optimizes the fused feature space, it overcomes the problems of feature fusion. Finally the feature vector is further reduced using Principal Component Analysis (PCA) and matched with stored template using NN classifier. The proposed approach is validated for their efficiency on PolyU palmprint database of 200 users. The experimental results illustrates that the feature level fusion improves the recognition accuracy significantly.

Keywords

Biometrics, Palmprint, Feature Fusion, PSO, Intramodal.

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J. Vijayakumar ¹, S. Arumugam ²

Affiliations
1 Department of Electronics and Communication Engineering, Nandha College of Technology, IN
2 Nandha Engineering College, IN

Abstract

Betelvine plants are infected variety of diseases in the complete plantation without any premature warning of the diseases. The aim of this paper is to detection of foot rot disease in the vellaikodi variety of betelvine plants using digital image processing techniques. The digital images of the uninfected or normal betelvine leaves and the digital images of the infected in foot rot diseased betelvine leaves at different stages are collected from different Betelvine plants using a high resolution digital camera and collected betelvine images are stored with JPEG format. The digital images of the betelvine leaves analyses are done using the image processing toolbox in MATLAB which gives the normal patterns of the digital images. Using RGB encoding process, the RGB components of the betelvine leaves are separated. The mean and median values for all sample leaves are computed and calculated values are stored in the system. The mean and median values of test leaves are computed and compared with the stored values. As the result of this comparison, it is identified whether test leaves are affected by foot rot disease or not. Finally this analysis helps to recognize the foot rot disease can be identified before it spreads to entire crop.

Keywords

Betelvine, Foot Rot Disease and Phytophthora Parasitica.

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