FAST DISCRETE CURVELET TRANSFORM BASED ANISOTROPIC FEATURE EXTRACTION FOR IRIS RECOGNITION

Video steganography mainly deals with secret data transmission in a carrier video file without being visually noticeable by intruders. Video steganography is preferred over image steganography because a video carries more space in comparison to an image. The main concept of information hiding consists of a cover media, which is a greyscale or a color video, a secret data, which is an image or text, and a stego key. Here a secure video steganography method has been proposed which uses Curvelet Transform for secret data embedding, Elliptic Curve Cryptography for stego key encryption and a threshold algorithm for the determination of the amount of secret data to be encoded per frame. A video is a collection of various frames. The frames are selected randomly from the cover video and the frame number of the respective frames has been indexed in the stego key to find the secret data embedding location. Here, the selection of frames in a sequential manner has been avoided to improve security. For enhanced security, the stego key is also encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES). Fast Discrete Curvelet Transform (FDCT) has been applied to the frames of the cover video and the curvelet coefficients have been modified to obscure the secret data to produce the stego video.

Ground-roll is an interferential wave existing widely in seismic data, and it is an important step in seismic data processing to suppress ground-roll. Wavelet Transform lacks of detecting the singularities of the signal with straight line or curve, and the method can separate the different waves at the same time because of Curvelet transform simultaneously has the characteristics of frequency and direction. The experiment results show that the Curvelet transform has better effects in ground-roll suppression.

Image fusion based on the Fourier and wavelet transform methods retain rich multispectral details but less spatial details from source images. Wavelets perform well only at linear features but not at non linear discontinuities because they do not use the geometric properties of structures. Curvelet transforms overcome such difficulties in feature representation. In this paper, we define a novel fusion rule via high pass modulation using Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms (FDCT) domain. For experimental study of this method Indian Remote Sensing (IRS) Resourcesat-1 LISS IV satellite sensor image of spatial resolution of 5.8m is used as low resolution (LR) multispectral image and Cartosat-1 Panchromatic (Pan) of spatial resolution 2.5m is used as high resolution (HR) Pan image. This fusion rule generates HR multispectral image at 2.5m spatial resolution. This method is quantitatively compared with Wavelet, Principal component analysis (PCA), High pass filtering(HPF), Modified Intensity-Hue-Saturation (M.IHS) and Grams-Schmidth fusion methods. Proposed method spatially outperform the other methods and retains rich multispectral details.

Digital watermarking technology means that the hidden information is embedded in digital multimedia data by way of signal processing. In this paper, we firstly analyze the Fast Discrete Curvelet Transforms (FDCT) and Discrete Wavelet Transforms (DWT) which are usually used in image processing and figure out that DWT can not approximate optimally the image which contains the line singularity. And then a new encrypted embedded algorithm is presented based on FDCT. Based on bit-plane of gray image, the algorithm makes use of the advantage of FDCT and the watermark is encrypted by Chaotic Map and Arnold Transforms (AT) which is imbedded into the proper coefficient of FDCT. The result of our experiment shows that this algorithm is robust and transparent compared with DWT.

This article presents an efficient method for power quality events (PQEs) detection and classification based on Curvelet transform (CT) and optimized extreme learning machine (OELM). Initially, various PQEs signal data are extracted even under noisy and simultaneously occurred multi-event conditions to reflect the real-time conditions. Relevant features of all these extracted signals are computed by using a multi-resolution and multidirectional fast discrete CT (FDCT) approach. These features are used to classify the events accurately by the proposed OELM. In this classification strategy, a modified differential evolution (MDE) is introduced to optimize the conventional ELM to enhance its precision rate. The proposed novel approach is tested and analyzed under various noisy conditions with 20 dB, 30 dB, and 50 dB with single and multi PQEs conditions. Comparative results with other recently proposed approaches by various authors reveal that the proposed CT- and OELM-based classifier titled CT-OELM can be considered as a competitive choice for implementing in the real-time monitoring system.

Batik is one of the cultural heritages in Indonesia. Batik has many types spread around Indonesia. Related to the diversity of batik, an effort to develop a database to preserve batik information is required. Searching batik information from the database by using keywords such as the province name where a batik came from, sometimes is difficult. In some cases, people only has a batik image without knowing any additional information, such as motif name and it's origin. Attaching a modul to classify batik image automatically into the database will be very useful, so that people can search more information about batik by inputting a batik image. This research proposes batik image classification using Fast Discrete Curvelet Transform (FDCT) and Hue Saturation Value (HSV) space as the representation of texture and color features, and K Nearest Neighbour (KNN) as the classifier. The experiment give a good result, which is showed by the worst classification error rate 3.33% for combined features vector.

Image denoising always is one of important research topics in the image processing field. In this paper, fast discrete curvelet transform (FDCT) and undecimated wavelet transform (UDWT) are proposed for image denoising. A noisy image is first denoised by FDCT and UDWT separately. The whole image space is then divided into edge region and non-edge regions. After that, wavelet transform is performed on the images denoised by FDCT and UDWT respectively. Finally, the resultant image is fused through using both of edge region wavelet cofficients of the image denoised by FDCT and non-edge region wavelet cofficients of the image denoised by UDWT. The proposed method is validated through numerical experiments conducted on standard test images. The experimental results show that the proposed algorithm outperforms wavelet-based and curvelet-based image denoising methods and preserve linear features well.

Detecting the changes within a single image is significant for applications like planogram compliance where objects of same type are arranged in a sequence. This article discusses on detection of such objects which differ in color, texture and shape from other objects in the given image. In this proposed algorithm, the given image is converted to HSV color space and it is partitioned into regions; Fast Discrete Curvelet Transform (FDCT) coefficients are extracted. In order to identify changed regions in the given image, salient points are detected using the Features from Accelerated Segment Test (FAST) corner detection algorithm. It has been observed that the value channel content of the HSV image is much significant in extracting useful features for change detection. Experimental results show an efficacy of 87% in detecting changes in a single row of a shelf image having same items arranged in a linear fashion.Keywords(FAST) corner detectionHSV color spaceChange detectionFDCTImage processing

Breast cancer diagnosis in digital mammogram using multiscale curvelet transform

This paper aims at developing an automatic pathological brain detection system (PBDS) to assist radiologists in identifying brain diseases correctly in less time. Magnetic resonance imaging (MRI) has the potential to provide better information about the brain soft tissues and hence MR images have been incorporated in the proposed system. Fifty largest coefficients are selected from each sub-band of a level-5 fast discrete curvelet transform (FDCT) to serve as a feature set for each image. To reduce the size of the feature set, principal component analysis (PCA) has been harnessed. Subsequently, least squares SVM (LS-SVM) with three different kernels are utilized to segregate the images as healthy or pathological. The proposed system has been validated on three benchmark datasets and a 10 ×k-fold stratified cross validation (SCV) test has been performed. It indicates that the proposed system “FDCT + PCA + LS-SVM + RBF” achieves better performance than not only two other systems having linear and polynomial kernel but also 22 existing methods. In addition, the suggested system requires only six features which are computationally economical for a practical use.

In order to improve the performance of iris recognition, a novel method for iris recognition based on block theory and self-adaptive feature selection is proposed in this paper. Firstly, the normalized iris image is decomposed by convolving with multi-scale and multi-orientation Gabor filters, and then separated into several blocks, the block feature vector which includes mean and variance of Gabor coefficients inside each block can be obtained through statistical techniques, the iris feature vector of the whole iris image is then constructed by conjugating the block feature vector in row column order, finally the two-classifier of iris image are established based on the most distinguishable features, and the multi-classifiers of iris image are established by voting mechanism, and the performance is test by CASIA iris database. The results show that, compared with the traditional iris recognition methods, the proposed method has improved the iris recognition rate.

Noise removal is essential in medical imaging applications in order to enhance and recover anatomical details that may be hidden in the data. The issues of Poisson noise occurrence in medical imaging due to the arrival of photon nature of light produced at the time of capturing of image have always been a concern. This paper addresses a novel approach for the removal of Poisson noise embedded in the biomedical images based on multi-resolution transform-based denoising and fusion approach. In this technique, first, the images are denoised separately, by applying Discrete Wavelet Transform (DWT)/Stationary Wavelet Transform (SWT) and Fast Discrete Curvelet Transform (FDCT) integrated with Rudin-Osher-Fatemi (ROF) model. Next, the distinct features extracted from the denoised images are fused, resulting in an enhanced and denoised image. The proposed method was tested and compared with other denoising techniques on a set of biomedical images. Subjective and objective evaluations show better performance of this new approach compared to existing techniques.

Iris recognition has become a trend in the biometric discipline to identify an individual and is widely used compared to other biometrics and the traditional recognition system. Various factors, such as the obstruction of eyelids, eyelashes, shadows, specular reflection by eyeglasses, and other noises, can cause the occlusion of iris images. This study aims to propose a hybrid U-Net with ResNet segmentation method (HUREIS) in occlusion iris images for iris recognition. The HUREIS was compared with VGG-16 to determine its benchmark based on segmentation ac curacy and computational time. This study employed a MICHE I dataset that only considers occlusion images for eyelids and eyelashes for mobile devices. The findings showed that HUREIS performed the best dice score at 93.7% and Jaccard score at 88.3%, respectively. The performance of HUREIS is 5.4% better than VGG-16, with approximately 84% minimum computational time. The segmentation accuracy was compared with the state of-the-art methods based on the MICHE-I dataset and the U Net segmentation method to validate the effectiveness of the proposed iris segmentation method. Furthermore, this study evaluated the feature vector of HUREIS and VGG-16 to obtain the performance of iris classification using a support vector machine and decision tree C4.5 based on Euclidean distance and Cosine similarity. The Euclidean distance and cosine similarity were compared to determine the best image similarity score close to one. The results show that SVM for HUREIS achieved the optimal classification accuracy at 100% using Euclidean distance. Compared to decision tree C4.5, VGG-16 achieved 94.41% of accuracy using Euclidean distance.

Iris recognition is a means of biometric identification. A key part of the recognition system using iris is the extraction of prominent texture information or features in the iris. The identification delay in iris recognition can be reduced by reducing the feature vector generated from the feature extraction of iris images. A new form of local binary pattern (LBP) termed LBPX is proposed in this paper as an iris feature extraction method. For this, input eye images are processed and converted to normalized iris images employing circular Hough transformation and Daugman’s rubber sheet model. Next, LBPX is applied to the normalized images. In this LBPX stage, rotation-invariant LBP operation takes place. The performance of LBPX based recognition system adopting iris image is evaluated in terms of accuracy and feature vector length. This is done for three datasets CASIA-IRIS-V4, UBIRIS and IITD. Results indicate that LBPX can achieve acceptable accuracy values of 97.20%, 96% and 96.40% for CASIA-IRIS-V4, UBIRIS and IITD datasets, respectively. Furthermore, results show LBPX outperforms existing feature extraction methods in terms of reduced feature-length, ensuring faster iris recognition.

Iris Recognition for Personal Identification System