ICTACT Journal on Image and Video Processing

S. Arivazhagan ¹, R. Newlin Shebiah ¹, J. Salome Magdalene ¹, G. Sushmitha ¹

Affiliations
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, IN

Abstract

Derailments take place when a train runs off its rails and are seriously hazardous to human safety. Most of the Railway Track defects which lead to derailment are detected manually by trained human operators walking along the track. To overcome this difficulty, an Automatic Railway Track Derailment Inspection System using Machine Vision Algorithm to detect the cracks in the railway track is proposed here. The input image is decomposed by Gabor filter and texture features were extracted using Segmentation based Fractal Texture Analysis (SFTA) and the features are classified as defect and defect free classes using AdaBoost Classifier. The proposed algorithm is tested on a set of real time samples collected and the classification rate obtained was satisfactory.

Keywords

Crack Detection, Gabor Wavelets, Texture Analysis, AdaBoost Classifier.

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S. Arivazhagan ¹, L. Ganesan ², C. N. Savithri ¹

Affiliations
1 Departmentof Electronics and Communication Engineering, Mepco Schlenk Engineering College, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Alagappa Chettiar College of Engineering and Technology, Tamil Nadu, IN

Abstract

Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Double Density Wavelet Transform (DDWT) and Dual Tree Complex Wavelet Transform (DTCWT) and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

Keywords

Texture, Multi-Resolution Transforms, Statistical and Co-Occurrence Features.

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S. Arivazhagan ¹, W. Sylvia Lilly Jebarani ¹, S. Bagavath ¹

Affiliations
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Tamil Nadu, IN

Abstract

Steganographic algorithms in the recent past have been producing stego images with perceptual invisibility, better secrecy and certain robustness against attacks like cropping, filtering etc. Recovering a good quality secret from a good quality stego image may not always be possible. The method proposed in this paper works in transform domain and attempts to extract the secret almost as same as the embedded one maintaining minimal changes to the cover image by using techniques like median maintenance, offset and quantization.

Keywords

Arnold Transform, Median maintenance, Discrete Wavelet Transform, Improved LSB, Offset, Quantization.

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