ICTACT Journal on Image and Video Processing

S. Jenicka ¹, A. Suruliandi ²

Affiliations
1 Department of Computer Science and Engineering, Einstein College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN

Abstract

Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely sensed image, Adaptive Neuro Fuzzy Inference System (ANFIS) has been used. The strength of ANFIS classifier is that it combines the merits of fuzzy logic and neural network. Hence in this article, land cover classification of remotely sensed image has been performed using Gabor wavelet and ANFIS classifier. The classification accuracy of the classified image obtained is found to be 92.8%.

Keywords

ANFIS, Gabor Filters, Texture Analysis, Land Cover Classification, Big Data.

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A. Suruliandi ¹, G. Murugeswari ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN

Abstract

Digital image processing techniques are useful in abnormality detection in mammogram images. Recently, texture based image segmentation of mammogram images has become popular due to its better precision and accuracy. Local Binary Pattern has been a recently proposed texture descriptor which attracted the research community rigorously towards texture based analysis of digital images. Many texture descriptors have been developed as variants of Local Binary Pattern, because of its success. In this work, the performance of Local Binary Pattern descriptor and its variants namely Local Ternary pattern, Extended Local Ternary Pattern, Local Texture Pattern and Local Line Binary Pattern are evaluated for mammogram image segmentation using a supervised KNN algorithm. Performance metrics such as accuracy, error rate, sensitivity, specificity, Under Estimation Fraction and Over Estimation Fraction are used for comparison purpose. The results show that Local Binary Pattern outperforms other descriptors in terms of abnormality detection in mammogram images.

Keywords

Mammogram Image Segmentation, Texture Segmentation, Local Binary Pattern, Local Ternary Pattern, Extended Local Ternary Pattern, Local Texture Pattern and Local Line Binary Pattern.

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R. Reena Rose ¹, A. Suruliandi ², K. Meena ³

Affiliations
1 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
3 Department of Electronics and Communication Engineering, J. P. College of Engineering, IN

Abstract

Texture descriptors have an important role in recognizing face images. However, almost all the existing local texture descriptors use nearest neighbors to encode a texture pattern around a pixel. But in face images, most of the pixels have similar characteristics with that of its nearest neighbors because the skin covers large area in a face and the skin tone at neighboring regions are same. Therefore this paper presents a general framework called Local Texture Description Framework that uses only eight pixels which are at certain distance apart either circular or elliptical from the referenced pixel. Local texture description can be done using the foundation of any existing local texture descriptors. In this paper, the performance of the proposed framework is verified with three existing local texture descriptors Local Binary Pattern (LBP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) for the five issues viz. facial expression, partial occlusion, illumination variation, pose variation and general recognition. Five benchmark databases JAFFE, Essex, Indian faces, AT & T and Georgia Tech are used for the experiments. Experimental results demonstrate that even with less number of patterns, the proposed framework could achieve higher recognition accuracy than that of their base models.

Keywords

Face Recognition, Local Texture Description Framework, Nearest Neighborhood Classification, Chi-Square Distance Metric.

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A. Suruliandi ¹, R. Reena Rose ², K. Meena ³

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
3 Department of Computer Science and Engineering, Sardar Raja College of Engineering, IN

Abstract

Texture feature plays a predominant role in recognizing face images. However different persons can have similar texture features that may degrade the system performance. Hence in this paper, the problem of face similarity is addressed by proposing a solution which combines textural and geometrical features. An algorithm is proposed to combine these two features. Five texture descriptors and few geometrical features are considered to validate the proposed system. Performance evaluations of these features are carried out independently and jointly for three different issues such as expression variation, illumination variation and partial occlusion with objects. It is observed that the combination of textural and geometrical features enhance the accuracy of face recognition. Experimental results on Japanese Female Facial Expression (JAFFE) and ESSEX databases indicate that the texture descriptor Local Binary Pattern achieves better recognition accuracy for all the issues considered.

Keywords

Face Recognition, Texture Features, Geometric Features, Nearest Neighborhood Classification, Chi-Square Distance Metric.

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K. Meena ¹, A. Suruliandi ², R. Reena Rose ³

Affiliations
1 Department of Electronics and Communication Engineering, J. P. College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
3 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN

Abstract

Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE), Gamma Intensity Correction (GIC), Normalization chain and Modified Homomorphic Filtering (MHF) are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.

Keywords

Face Recognition, Texture Analysis, Texture Features.

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