Fabric defect detection using the wavelet transform in an ARM processor

Small devices used in our day life are constructed with powerful architectures that can be used for industrial applications when requiring portability and communication facilities. We present in this paper an example of the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance than using processors with float point arithmetic calculations

A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents

International audienceRecently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set

Propose shot boundary detection methods by using visual hybrid features

Shot boundary detection is the fundamental technique that plays an important role in a variety of video processing tasks such as summarization, retrieval, object tracking, and so on. This technique involves segmenting a video sequence into shots, each of which is a sequence of interrelated temporal frames. This paper introduces two methods, where the first is for detecting the cut shot boundary via employing visual hybrid features, while the second method is to compare between them. This enhances the effectiveness of the performance of detecting the shot by selecting the strongest features. The first method was performed by utilizing hybrid features, which included statistics histogram of hue-saturation-value color space and grey level co-occurrence matrix. The second method was performed by utilizing hybrid features that include discrete wavelet transform and grey level co-occurrence matrix. The frame size decreased. This process had the advantage of reducing the computation time. Also used local adaptive thresholds, which enhanced the method’s performance. The tested videos were obtained from the BBC archive, which included BBC Learning English and BBC News. Experimental results have indicated that the second method has achieved (97.618%) accuracy performance, which was higher than the first and other methods using evaluation metrics

Discrete Wavelet Transform (DWT) – Gray Level Co-occurrence Matric (GLCM) – Based Fingerprint Recognition Method

Fingerprint recognition system has become among the most popular system used either in civilian law or personal security system. Mostly, fingerprint recognition is based on minutiae that is corresponding to features of the image and thus the similarities are evaluated. In this paper, another technique is used to overcome the normal issue of time consumption. Thus, discrete wavelet transform (DWT) and grey level co-occurrence metrics (GLCM) is proposed to have shorter time consumption. Throughout this paper, the project is to evaluate similarities of fingerprint images in terms of false acceptance rate (FAR), false rejection rate (FRR), and total success rate (TSR). The fingerprint images consist of 15 subjects with about four different images each

Reliable and Efficient coding Technique for Compression of Medical Images based on Region of Interest using Directional Filter Banks

Medical images carry huge and vital information. It is necessary to compress the medical images without losing its vital-ness. The proposed algorithm presents a new coding technique based on  image compression using contourlet transform used in different modalities of medical imaging. Recent reports on natural image compression have shown superior performance of contourlet transform, a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. As far as medical images are concerned the diagnosis part (ROI) is of much important compared to other regions. Therefore those portions are segmented from the whole image using  fuzzy C-means(FCM) clustering technique. Contourlet transform is then applied to ROI portion which performs Laplacian Pyramid(LP) and Directional Filter Banks. The region of less significance are compressed using Discrete Wavelet Transform and finally modified embedded zerotree wavelet algorithm is applied which uses six symbols instead of four symbols used in Shapiro’s EZW to the resultant image which shows better PSNR and high compression ratio.Â

Fingerprint Recognition using Gray Level Co-Occurrence Matrices (GLCM) and Discrete Wavelet Transform (DWT)

This paper is thoroughly investigated regarding the fingerprint recognition techniques. This is because the world of security had become more essential. Thus, fingerprint recognition is one of the security enforcement and needed to be developed essentially. This project is focused on the effectiveness of the Gray-Level Co-occurrence Matrices (GLCM) and Discrete Wavelet Transform (DWT) techniques for fingerprint recognition. As in the chapter one, this paper discusses regarding the background of the GLCM and the DWT as well as the reason of this project was initiated. Other than that, this paper also discuss regarding the problem that had been faced previously in order to recognise fingerprint optimally. This paper also discusses the objectives and the limitation of this project in this chapter. On the next chapter, history regarding the GLCM as well as DWT had been widely discuss that made the fingerprint recognition system becomes more popular nowadays. The definition of term, equation and equation related to the GLCM and DWT also had been explained. Moreover, some previous related study will also be discussed. On the third chapter, this paper reviews the method that will be approached for the project for the entire eight months’ timeframe. As for the last chapter, several initial conclusions had been made regarding the fingerprint recognition techniques