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Journal articles on the topic 'Log-Gabor wavelet'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

LI, Ning, and De XU. "2D Log-Gabor Wavelet Based Action Recognition." IEICE Transactions on Information and Systems E92-D, no. 11 (2009): 2275–78. http://dx.doi.org/10.1587/transinf.e92.d.2275.

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2

Li, Ji-liang, Xiang-zhong Fang, and Jun Hou. "Mean shift based log-Gabor wavelet image coding." Journal of Zhejiang University-SCIENCE A 8, no. 4 (April 2007): 620–24. http://dx.doi.org/10.1631/jzus.2007.a0620.

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3

Agarwal, Megha, and Rudra Prakash Maheshwari. "Content Based Image Retrieval Based on Log Gabor Wavelet Transform." Advanced Materials Research 403-408 (November 2011): 871–78. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.871.

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This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

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4

Li, Qi, Peng Ge, Hua Jun Feng, and Zhi Hai Xu. "Image Displacement Detection under Low Illumination Using Joint Transform Correlator with Wavelet Denoising." Applied Mechanics and Materials 128-129 (October 2011): 602–6. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.602.

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Since joint transform correlator (JTC) cannot directly detect the displacement between reference and target images without adequate exposure, an image displacement detection method using JTC based on log-Gabor wavelet denoising is proposed. The method uses a log-Gabor wavelet transform to denoise the reference and the target image obtained in the condition lack of enough exposure, preserving the phase information of them. Results show that the method can successfully accomplish the motion detection, RMSE of displacement measurement using JTC with wavelet denoising could be within 0.3 pixels under 1/80 of normal exposure. The method improved the detection ability of JTC in the condition of low illumination and low contrast, and has great application prospect under these circumstances.

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5

ZHOU, Zhi-ping, and Hui-jun WU. "Iris recognition algorithm combining wavelet transform and Log-Gabor filter." Journal of Computer Applications 29, no. 8 (October 9, 2009): 2095–97. http://dx.doi.org/10.3724/sp.j.1087.2009.02095.

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6

Senapati, Suman, Neeraj Bhende, and Goutam Saha. "Bayesian marginal statistics for speech enhancement using log Gabor wavelet." International Journal of Speech Technology 14, no. 3 (July 12, 2011): 193–210. http://dx.doi.org/10.1007/s10772-011-9096-2.

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7

Yang, Yong, Song Tong, Shuying Huang, and Pan Lin. "Log-Gabor Energy Based Multimodal Medical Image Fusion in NSCT Domain." Computational and Mathematical Methods in Medicine 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/835481.

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Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. The phase congruency that can provide a contrast and brightness-invariant representation is applied to fuse low-frequency coefficients, whereas the Log-Gabor energy that can efficiently determine the frequency coefficients from the clear and detail parts is employed to fuse the high-frequency coefficients. The proposed fusion method has been compared with the discrete wavelet transform (DWT), the fast discrete curvelet transform (FDCT), and the dual tree complex wavelet transform (DTCWT) based image fusion methods and other NSCT-based methods. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of multimodal medical images than other algorithms. Further, the applicability of the proposed method has been testified by carrying out a clinical example on a woman affected with recurrent tumor images.

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8

Senapati, Suman, Sandipan Chakroborty, and Goutam Saha. "Speech enhancement by joint statistical characterization in the Log Gabor Wavelet domain." Speech Communication 50, no. 6 (June 2008): 504–18. http://dx.doi.org/10.1016/j.specom.2008.03.004.

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9

Hsieh, Ching Tang, Chia Shing Hu, and Chun Wei Pan. "Automatic Facial Aging/Rejuvenating Synthesis Method." Applied Mechanics and Materials 284-287 (January 2013): 3035–39. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3035.

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At present, the synthesizing faces of different ages does not emphasize on feature alignment and rectification of twisted images. If these situations do happen, they might cause failure and inaccuracy on synthesizing images. In this paper, we propose a reversible human facial aging/rejuvenating synthesis system which is implemented by Active Shape Model (ASM) integrated with Log-Gabor Wavelet, which can be used to search for the dementia elderly. First, we use AdaBoost and ASM algorithm to extract the feature set of human face, and rectify them by the concept of facial geometric invariance. The invariant concepts are the distance between inner corners of both eyes and the distance between the nose and chin. Then, we find manually one target image which is similar to the test image from the database, and analyze age texture of this human image by Log-Gabor wavelet in order to retrieve decomposition maps. Finally, we can effectively simulate human facial images of people of different ages by controlling the number of decomposition map of images and objectively judge the results via the density of wrinkles.

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10

Farouk, R. M., Mohammed Elsayed, and Mohammed Aly. "Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm." International Journal of Computer Applications 141, no. 1 (May 17, 2016): 27–32. http://dx.doi.org/10.5120/ijca2016909209.

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11

Xinting Gao, F. Sattar, and R. Venkateswarlu. "Multiscale Corner Detection of Gray Level Images Based on Log-Gabor Wavelet Transform." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 7 (July 2007): 868–75. http://dx.doi.org/10.1109/tcsvt.2007.897473.

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12

Senapati, Suman. "Optimal speech enhancement under signal presence uncertainty using Log Gabor Wavelet and Bayesian Joint Statistics." International Journal of Speech Technology 16, no. 4 (April 12, 2013): 439–59. http://dx.doi.org/10.1007/s10772-013-9195-3.

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13

Ammour, Basma, Larbi Boubchir, Toufik Bouden, and Messaoud Ramdani. "Face–Iris Multimodal Biometric Identification System." Electronics 9, no. 1 (January 1, 2020): 85. http://dx.doi.org/10.3390/electronics9010085.

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Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

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14

Kalitzin, Stiliyan N., Prisca R. Bauer, Robert J. Lamberts, Demetrios N. Velis, Roland D. Thijs, and Fernando H. Lopes Da Silva. "Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse." International Journal of Neural Systems 26, no. 08 (October 4, 2016): 1650027. http://dx.doi.org/10.1142/s0129065716500271.

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Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time–frequency wavelet spectrum to detect log–linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as “gold standard”. We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

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15

Fischer, Sylvain, Filip Šroubek, Laurent Perrinet, Rafael Redondo, and Gabriel Cristóbal. "Self-Invertible 2D Log-Gabor Wavelets." International Journal of Computer Vision 75, no. 2 (January 13, 2007): 231–46. http://dx.doi.org/10.1007/s11263-006-0026-8.

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16

CHEN, XUERONG, and ZHONGLIANG JING. "INFRARED FACE RECOGNITION BASED ON LOG-GABOR WAVELETS." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 03 (May 2006): 351–60. http://dx.doi.org/10.1142/s0218001406004703.

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Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Recently, infrared (IR) imagery of human faces is considered as a promising alternative to visible imagery. IR face recognition is a biometric which offers the security of fingerprints with the convenience of face recognition. However, IR has its own limitations. The presence of eyeglasses has more influence on IR than visible imagery. In this paper, a method based on Log-Gabor wavelets for IR face recognition is proposed. The method first derives a Log-Gabor feature vector from IR face image, then obtains the independent Log-Gabor features by using independent component analysis (ICA). Experimental results show that the proposed method works well, even in challenging situations.

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17

S.Patil, Pravin, S. R. Kolhe, R. V. Patil, and P. M. Patil. "The Comparison of Iris Recognition using Principal Component Analysis, Log Gabor and Gabor Wavelets." International Journal of Computer Applications 43, no. 1 (April 30, 2012): 29–33. http://dx.doi.org/10.5120/6070-8229.

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18

NOWSHAD, SK, and UMAR FAROOQ. "ANALYSIS OF MAMMOGRAM FOR DETECTION OF BREAST CANCER USING WAVELET STATISTICAL FEATURES." International Journal of Image Processing and Vision Science, October 2012, 89–94. http://dx.doi.org/10.47893/ijipvs.2012.1016.

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Early detection of breast cancer increases the survival rate and increases the treatment options. One of the most powerful techniques for early detection of breast cancer is based on digital mammogram. A system can be developed for assisting the analysis of digital mammograms using log-Gabor wavelet statistical features. The proposed system involves three major steps called Pre-processing, Processing, and Feature extraction. In pre-processing, the digital mammogram can be de-noised using efficient decision-based algorithm. In processing stage, the suspicious Region of Interest (ROI) can be cropped and convolved with log-Gabor filter for four different orientations. Then gray level co-occurrence matrix (GLCM)can be constructed for log-Gabor filter output at four different orientations and from that first order statistical features and second order statistical features can be extracted to analyze whether the mammogram as normal or benign or malignant. The proposed method can allow the radiologist to focus rapidly on the relevant parts of the mammogram and it can increase the effectiveness and efficiency of radiology clinics.

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19

Mohanty, Sanjay Kumar, and Niladree Nandini Das. "Identification Based on Texturer Analysis And Gabor Wavelet Filter." International Journal of Computer and Communication Technology, January 2012, 29–32. http://dx.doi.org/10.47893/ijcct.2012.1110.

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This paper presents for human identification and here used a fusion mechanism that amalgamates both, Canny Edge Detection and a circulat hough transform to detect the iris boundaries in the eye's digital image. Applying the Gabor Wavelet filter isted of using 1D Log-Gabor filter in order to exact the deterministic pattern in a person's iris in the form of a featute vector. By comparing the quantized vectors using the hamming distance operator, we determine finally.

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20

Mohanty, Sanjay Kumar, and Prasant Kumar Pattnaik. "Authentication Based on Texture Analysis And SVM Classification." International Journal of Instrumentation Control and Automation, April 2011, 61–66. http://dx.doi.org/10.47893/ijica.2011.1012.

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This paper presents for identification and here used a fusion mechanism that amalgamates both, a Canny Edge detection and a Circular Hough Transform to detect the iris boundaries in the eye’s digital image. We then applied the Gabor Wavelet filter instead of using 1D Log-Gabor filter in order to exact the deterministic patterns in a person’s iris in the form of a feature vector. By comparing the quantized vectors using the Hamming Distance operator, we determine finally and for classification used Support vector Machine.

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21

"Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features." International Journal of Recent Technology and Engineering 8, no. 3 (September 30, 2019): 2761–67. http://dx.doi.org/10.35940/ijrte.c4808.098319.

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Iris recognition system has gained prominent focus because of its uniqueness, stability over time. But the recognition level of single biometric based recognition systems is greatly affected by environmental conditions, physiological deficiency. Multi-biometric systems diminish this problem with the fusion of features collected from various traits or samples of the same trait, a single trait by employing multiple algorithms or multiple instances. To gain the advantages of multi-biometric systems in iris recognition, a Multi-algorithmic iris recognition system has been proposed where Texture features from iris are extracted by using 2D-Log Gabor filter and Phase features are extracted by Haar Wavelet; And these features can be integrated at various levels like Decision, Rank, Score, feature, and pixel. Even though the feature level fusion contains rich information about biometric samples when compared to remaining fusion levels; it involves mapping complexity, high dimensional feature space. To gain advantage of feature level fusion in iris recognition and to overcome the problem of resulted high dimensional feature space, Genetic Algorithm (GA) based reduction scheme, Principal Component Analysis (PCA) reduction strategy and a hybrid reduction scheme which is a combination of PCA and GA have been applied to reduce the resulted feature space. The performance of these reduction strategies have evaluated on CASIA iris database, IIT Delhi iris database using Machine Learning approaches. The results have shown that the feature space has dramatically reduced while keeping recognition accuracy and also revealed that space and time requirements have significantly decreased after employing feature reduction schemes.

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