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Journal articles on the topic 'Slanlet transform'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Hamoud Al-Tamimi, Mohammed Sabbih. "Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4382. http://dx.doi.org/10.11591/ijece.v9i5.pp4382-4395.

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<p>In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this

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Mohammed, Sabbih Hamoud Al-Tamimi. "Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4382–95. https://doi.org/10.11591/ijece.v9i5.pp4382-4395.

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In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In th

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3

Selesnick, I. W. "The slantlet transform." IEEE Transactions on Signal Processing 47, no. 5 (1999): 1304–13. http://dx.doi.org/10.1109/78.757218.

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4

Xiang, Xiuqiao, and Baochang Shi. "Evolving generation and fast algorithms of slantlet transform and slantlet-Walsh transform." Applied Mathematics and Computation 269 (October 2015): 731–43. http://dx.doi.org/10.1016/j.amc.2015.07.094.

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Kekre, H. B., Tanuja Sarode, and Shachi Natu. "Performance Comparison of Wavelets Generated from Four Different Orthogonal Transforms for Watermarking With Various Attacks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 3 (2013): 1139–52. http://dx.doi.org/10.24297/ijct.v9i3.3340.

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This paper proposes a watermarking technique using different orthogonal wavelet transforms like Hartley wavelet, Kekrewavelet, Slant wavelet and Real Fourier wavelet transform generated from corresponding orthogonal transform. Theseorthogonal wavelet transforms have been generated using different sizes of component orthogonal transform matrices.For example 256*256 size orthogonal wavelet transform can be generated using 128*128 and 2*2 size componentorthogonal transform. It can also be generated using 64*64 and 4*4, 32*32 and 8*8, 16*16 and 16*16 size componentorthogonal transform matrices. In

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Abbas, Sulaiman M., Mohammed Nadhim Abbas, and Saifuldeen A. Mohammed. "SLANTLET TRANSFORM-BASED OFDM SCHEME." Journal of Engineering 12, no. 03 (2006): 1638–47. http://dx.doi.org/10.31026/j.eng.2006.03.04.

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Wireless digital communication is rapidly expanding resulting in a demand for systems that are reliable and have a high spectral efficiency. To fulfill these demands OFDM technology has drawn a lot of attention. In this paper a new technique is proposed to improve the performance of OFDM. The new technique is use the slantlet transform (SLT) instead Fast Fourier transform (FFT) in order to reduce the level of interference. This also will remove the need for Guard interval (GI) in the case of the FFT-OFDM and therefore improve the bandwidth efficiency of the OFDM. The SLT-OFDM is also better th

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Journal, Baghdad Science. "An Embedded Data Using Slantlet Transform." Baghdad Science Journal 8, no. 3 (2011): 840–48. http://dx.doi.org/10.21123/bsj.8.3.840-848.

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Data hiding is the process of encoding extra information in an image by making small modification to its pixels. To be practical, the hidden data must be perceptually invisible yet robust to common signal processing operations. This paper introduces a scheme for hiding a signature image that could be as much as 25% of the host image data and hence could be used both in digital watermarking as well as image/data hiding. The proposed algorithm uses orthogonal discrete wavelet transforms with two zero moments and with improved time localization called discrete slantlet transform for both host and

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Alwan, Iman Mohammed Ga’fer, and Enas Muzaffer Al-Ta’ee. "An Embedded Data Using Slantlet Transform." Baghdad Science Journal 8, no. 3 (2011): 840–48. http://dx.doi.org/10.21123/bsj.2011.8.3.840-848.

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Data hiding is the process of encoding extra information in an image by making small modification to its pixels. To be practical, the hidden data must be perceptually invisible yet robust to common signal processing operations. This paper introduces a scheme for hiding a signature image that could be as much as 25% of the host image data and hence could be used both in digital watermarking as well as image/data hiding. The proposed algorithm uses orthogonal discrete wavelet transforms with two zero moments and with improved time localization called discrete slantlet transform for both host and

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Al-Helali, Adnan Hadi M., Hamza A. Ali, and Buthainah Al-Dulaimi. "Slantlet Transform for Multispectral Image Fusion." Journal of Computer Science 5, no. 4 (2009): 263–69. http://dx.doi.org/10.3844/jcs.2009.263.269.

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Al-Helali. "Slantlet Transform for Multispectral Image Fusion." Journal of Computer Science 5, no. 4 (2009): 263–69. http://dx.doi.org/10.3844/jcssp.2009.263.269.

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11

Ghanim, Zainab N. "ECG SLANTLET TRANSFORM WITH FPGA DESIGN." Journal of Engineering 16, no. 04 (2010): 5877–91. http://dx.doi.org/10.31026/j.eng.2010.04.14.

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Journal, Baghdad Science. "SlantletTransformbased VideoDenoising." Baghdad Science Journal 8, no. 2 (2011): 595–601. http://dx.doi.org/10.21123/bsj.8.2.595-601.

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A technique for noise removal is proposed based on slantlet transform. The proposed algorithm tends to reduce the computational time by reducing the total number of frames through dividing the video film into sub films, finding master frames, applying the slantlet transform which is orthogonal discrete wavelet transform with two zero moments and with improved time localization. Thresholding technique is applied to the details coefficients of the slantlet transform .The denoised frame is repeated to retain the original frame sequence. The proposed method was applied by using MATLAB R2010a with

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13

Ga’fer, Iman M., and Afrah L. Mohammed. "SlantletTransformbased VideoDenoising." Baghdad Science Journal 8, no. 2 (2011): 595–601. http://dx.doi.org/10.21123/bsj.2011.8.2.595-601.

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A technique for noise removal is proposed based on slantlet transform. The proposed algorithm tends to reduce the computational time by reducing the total number of frames through dividing the video film into sub films, finding master frames, applying the slantlet transform which is orthogonal discrete wavelet transform with two zero moments and with improved time localization. Thresholding technique is applied to the details coefficients of the slantlet transform .The denoised frame is repeated to retain the original frame sequence. The proposed method was applied by using MATLAB R2010a with

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14

Abou-Loukh, Sadiq J., Tarik Zeyad, and Rasha Thabit. "ECG CLASSIFICATION USING SLANTLET TRANSFORM AND ARTIFICIAL NEURAL NETWORK." Journal of Engineering 16, no. 01 (2010): 4510–26. http://dx.doi.org/10.31026/j.eng.2010.01.09.

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Automatic detection and classification of cardiac arrhythmias is important for diagnosis of cardiac abnormality. This paper shows a method to accurately classify ECG arrhythmias through a combination of slantlet transform and artificial neural network (ANN). The ability of the slantlet transform to decompose signal at various resolutions allows accurate extraction of features from non-stationary signals like ECG. The low frequency coefficients, which contain the maximum information about the arrhythmia, were selected from the slantlet decomposition. These coefficients are fed to a Multi-Layer

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15

Kekre, Dr H. B., Dr Tanuja Sarode, and Prachi Natu. "STUDY OF INCREASE IN GLOBAL COMPONENTS IN HYBRID WAVELETS ON DATA COMPRESSION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 2 (2013): 1028–39. http://dx.doi.org/10.24297/ijct.v9i2.4173.

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This paper presents a hybrid wavelet transform technique which studies the effect of global components on the quality of image compression. Hybrid wavelet transform is generated using two different component orthogonal transforms. One orthogonal transform represents global featuresof image in betterway and another is used to represent local features. Walsh transform of size 8x8 is used as a base transform i.e. to represent global characteristics of image. Other transforms like DCT, Discrete Real Fourier Transform,DiscreteHartley transform (DHT), Discrete Sine Transform (DST), Discrete Kekre Tr

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Thabit, Rasha, Asmaa Hatem Jawad, Doaa Subhi, and Khamis A. Zidan. "Studying the Performance of Different Transforms in Face Image Authentication Technology." International Journal of Research and Scientific Innovation XI, no. VIII (2024): 631–46. http://dx.doi.org/10.51244/ijrsi.2024.1108051.

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The rapid spread of face image manipulation applications and the malevolent uses of the manipulated face images have been considered as an alarm for information security specialists. Inspired by the urgent need of face image manipulation reveal, various researches have been introduced to serve this purpose. The recent direction of developing face image authentication (FIA) technology has been directed towards combinations of various image processing techniques such as face area detection, image watermarking, tamper detection, tamper localization, and image recovery. In this paper, FIA scheme h

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17

Kumar, Sushil. "Image De-noising using Wavelet-Like Transform." International Journal of Advance Research and Innovation 5, no. 1 (2017): 46–50. http://dx.doi.org/10.51976/ijari.511707.

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This paper proposes a comparative study of image denoising method using BlockShrink algorithm between the Wavelet transform (DWT) and the Slantlet transform (SLT). Slantlet transform, which is also a wavelet-like transform and a better candidate for signal compression compared to the DWT based scheme and which can provide better time localization. BlockShrink is found to be a better method than other conventional image denoising methods. Through experimental results, it is found that DWT based BlockShrink thresholding is better option than BiShrink thresholding in terms of PSNR for the Gaussia

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18

Thabit, Rasha, and Bee Ee Khoo. "Robust reversible watermarking scheme using Slantlet transform matrix." Journal of Systems and Software 88 (February 2014): 74–86. http://dx.doi.org/10.1016/j.jss.2013.09.033.

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19

Bamal, Roopam, and Singara Singh Kasana. "Dual hybrid medical watermarking using walsh-slantlet transform." Multimedia Tools and Applications 78, no. 13 (2019): 17899–927. http://dx.doi.org/10.1007/s11042-018-6820-9.

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20

Khaleel, Asmaa. "Efficient PCG classification system based on Slantlet transform." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 8 (2023): 143–47. http://dx.doi.org/10.15199/48.2023.08.25.

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ISMAEEL, TARIQ ZEYAD, and AHMED FREIDOON FADHIL. "MULTISPECTRAL IMAGE FUSION USING SLANTLET-BASED RIDGELET TRANSFORM." Journal of Engineering 13, no. 04 (2007): 2074–88. http://dx.doi.org/10.31026/j.eng.2007.04.14.

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22

Khaleel, Asmaa. "Efficient PCG classification system based on Slantlet transform." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 8 (2023): 143–47. http://dx.doi.org/10.15199/48.2023.08.25.

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23

Kekre, H. B., Tanuja Sarode, Pallavi Halarnkar, and Debkanya Mazumder. "IMAGE SCRAMBLING USING NON SINUSOIDAL TRANSFORM AND KEY BASED SCRAMBLING TECHNIQUE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 8 (2014): 3809–22. http://dx.doi.org/10.24297/ijct.v12i8.2997.

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Nowadays Digital Images find a lot of applications almost in all the fields, for e.g. Information Hiding. Security of these images is very important. Image encryption is one method of providing security to digital images. In this paper we have proposed a method for Image scrambling which is based on a combination of spatial and frequency domain.Â Four different Non sinusoidal transforms, Walsh , Slant ,Kekre and Haar were used. Three different types i.e Row , Column and Full Transforms of these four transforms were applied for Image scrambling , out of these four Kekre Transform gave the best

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24

Abou-Loukh, Sadiq J., and Jaleel Sadoon Jameel. "Compression of an ECG Signal Using Mixed Transforms." Journal of Engineering 20, no. 06 (2023): 109–23. http://dx.doi.org/10.31026/j.eng.2014.06.08.

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Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG datavolume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the E

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25

M., Hitha, Rudresh M.D., and R. Sundaraguru. "Implementation of Image Denoising Techniques using Novel Slantlet Transform." International Journal of Computer Applications 183, no. 11 (2021): 15–21. http://dx.doi.org/10.5120/ijca2021921416.

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Alobaidy, Muhamad Azhar Abdilatef, Jassim Mohammed Abdul-Jabbar, and Saad Zaghlul Al-khayyt. "Slantlet transform used for faults diagnosis in robot arm." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 281. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp281-290.

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<p class="JESTECAbstract">The <span>robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first

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Panda, G., and S. K. Meher. "An Efficient Approach to Signal Compression using Slantlet Transform." IETE Journal of Research 46, no. 5 (2000): 299–307. http://dx.doi.org/10.1080/03772063.2000.11416169.

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28

Mundher, Myasar, Dzulkifli Muhamad, Amjad Rehman, Tanzila Saba, and Firdous Kausar. "Digital Watermarking for Images Security using Discrete Slantlet Transform." Applied Mathematics & Information Sciences 8, no. 6 (2014): 2823–30. http://dx.doi.org/10.12785/amis/080618.

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29

Flaih Hassan, Nidaa, and Reem Majeed Ibrahim. "Classification of Gender Face Image Based on Slantlet Transform." Engineering and Technology Journal 34, no. 4 (2016): 566–77. http://dx.doi.org/10.30684/etj.34.4b.15.

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30

Alobaidy, Muhamad Azhar Abdilatef, Jassim Mohammed Abdul-Jabbar, and Saad Zaghlul Al-khayyt. "Slantlet transform used for faults diagnosis in robot arm." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (2022): 281–90. https://doi.org/10.11591/ijeecs.v25.i1.pp281-290.

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The robot arm systems are the most target systems in the fields of faults detection and diagnosis which are electrical and the mechanical systems in many fields. Fault detection and diagnosis study is presented for two robot arms. The disturbance due to the faults at robot's joints causes oscillations at the tip of the robot arm. The acceleration in multi-direction is analysed to extract the features of the faults. Simulations for planar and space robots are presented. Two types of feature (faults) detection methods are used in this paper. The first one is the discrete wavelet transform, w

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31

Pallaw, Vijay Krishna, and Kamred Udham Singh. "X-Ray Image Authentication Scheme Using SLT and Contourlet Transform for Modern Healthcare System." JUCS - Journal of Universal Computer Science 28, no. 9 (2022): 916–29. http://dx.doi.org/10.3897/jucs.94132.

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The network&rsquo;s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals ar

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32

Pallaw, Vijay Krishna, and Kamred Udham Singh. "X-Ray Image Authentication Scheme Using SLT and Contourlet Transform for Modern Healthcare System." JUCS - Journal of Universal Computer Science 28, no. (9) (2022): 916–29. https://doi.org/10.3897/jucs.94132.

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The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are re

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Agaian, Sos, Khaled Tourshan, and Joseph P. Noonan. "Generalized parametric Slant–Hadamard transform." Signal Processing 84, no. 8 (2004): 1299–306. http://dx.doi.org/10.1016/j.sigpro.2004.04.009.

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34

H., Kattoush A., and Qasaymeh M. M. "Design and Simulation of MC-CDMA Transceiver via Slantlet Transform." International Journal of Information Technology and Computer Science 4, no. 3 (2012): 16–23. http://dx.doi.org/10.5815/ijitcs.2012.03.03.

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35

Panda, G., P. K. Dash, A. K. Pradhan, and S. K. Meher. "Data Compression of Power Quality Events Using the Slantlet Transform." IEEE Power Engineering Review 22, no. 1 (2002): 72. http://dx.doi.org/10.1109/mper.2002.4311703.

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Panda, G., P. K. Dash, A. K. Pradhan, and S. K. Meher. "Data compression of power quality events using the slantlet transform." IEEE Transactions on Power Delivery 17, no. 2 (2002): 662–67. http://dx.doi.org/10.1109/61.997957.

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37

Tarik Z.Ismaeel. "DESIGN & EVOLUTION OF A STEGANOGRAPHY SYSTEM FOR SPEECH SIGNAL BY SLANTLET TRANSFOR." Diyala Journal of Engineering Sciences 5, no. 2 (2012): 99–113. http://dx.doi.org/10.24237/djes.2012.05208.

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In this paper a Stegan graphic system was proposed to hide up a secret speech signal in a cover speech signal, using Slant let transform. The combination of a Stegan graphy and cryptography is used to increase the level of security and to make the system more rigid and complex to be defeated by attackers. In cryptography the secret speech signal is converted from 1-D to 2-D signal, divided into blocks each of size (8*8) sampled and then the samples are converted to the binary form, after that, columns transposition is applied to get the ciphered signal. This cipher signal will be the next stag

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38

Elsharkawy, M., W. L. Tsang, and M. Aburdene. "Short Data Parallel Vector Slant Transform." Journal of Parallel and Distributed Computing 23, no. 1 (1994): 27–36. http://dx.doi.org/10.1006/jpdc.1994.1116.

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39

Veearaswamy, K., M. Koteswara Rao, K. Anithasheela, and Ch Himabindu. "HVS Based Face Recognition Using Slant Transform." International Journal of Engineering & Technology 7, no. 3.34 (2018): 313. http://dx.doi.org/10.14419/ijet.v7i3.34.19215.

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There are many person identification technologies like password, PIN, key, and token are used in many applications. Present, popular identification technology is face. In many applications the database is large. Hence, recognition with high speed is major challenge. This paper presents a recognition using HVS features in transform domain. Human visual system identifies perceptual important information in the images. Slant transform basis vector is sawtooth. It efficiently represents linear brightness variations along an image line. Hence, in this work HVS features in slant transform domain is

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Agaian, S., K. Tourshan, and J. P. Noonan. "Parameterisation of slant-Haar transforms." IEE Proceedings - Vision, Image, and Signal Processing 150, no. 5 (2003): 306. http://dx.doi.org/10.1049/ip-vis:20030522.

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Hadad, Abbas Abd-Alhussein, Hiyam N. Khalid, Zainab S. Naser, and Mustafa Sabah Taha. "A Robust Color Image Watermarking Scheme Based on Discrete Wavelet Transform Domain and Discrete Slantlet Transform Technique." Ingénierie des systèmes d information 27, no. 2 (2022): 313–19. http://dx.doi.org/10.18280/isi.270215.

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Watermarking is primarily used to prevent unauthorized copying of digital data by inserting a watermark as a symbol of ownership in the digital data. The purpose of this work is to increase the imperceptibility and robustness of watermarked image by utilizing the Discrete Wavelet Transform (DWT) domain and Discrete Slantlet Transform (DST) technique. In compared to existing methodologies, the strategies used in this work considerably increased the robustness, imperceptibility, and protection of the watermarked image against JPEG compression and other types of noise attacks. The proposed method

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Mitchell, Alan R., and Panos G. Kelamis. "Efficient tau‐p hyperbolic velocity filtering." GEOPHYSICS 55, no. 5 (1990): 619–25. http://dx.doi.org/10.1190/1.1442873.

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Time and offset varying velocity filtering can be achieved by limiting the data input to forward tau‐p transforms. This limiting procedure, called hyperbolic velocity filtering (HVF), suppresses transform‐related artifacts as well as coherent and noncoherent noise while retaining elliptical (reflection) events. We show that HVF can be viewed as a muting process in the slant‐stack domain. Based on this simple but physical interpretation of HVF, a more efficient computer implementation is proposed. We further examine possible applications of HVF for processing seismic reflection data and illustr

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马, 小雪. "Fragile Watermark Algorithm Based on Slant Transform." Computer Science and Application 06, no. 06 (2016): 323–28. http://dx.doi.org/10.12677/csa.2016.66039.

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Zadiraka, V. K., and V. N. Evtushenko. "Optimal zone coding using the slant transform." Cybernetics and Systems Analysis 30, no. 4 (1994): 518–22. http://dx.doi.org/10.1007/bf02366561.

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Burki, J., and C. F. Barnes. "Slant Plane CSAR Processing Using Householder Transform." IEEE Transactions on Image Processing 17, no. 10 (2008): 1900–1907. http://dx.doi.org/10.1109/tip.2008.2002161.

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Adnan, Myasar Mundher, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Tanzila Saba, Suliman Mohamed Fati, and Saeed Ali Bahaj. "An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform." IEEE Access 10 (2022): 7520–32. http://dx.doi.org/10.1109/access.2022.3140861.

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Kundu, P. K., A. Chatterjee, and P. C. Panchariya. "Electronic Tongue System for Water Sample Authentication: A Slantlet-Transform-Based Approach." IEEE Transactions on Instrumentation and Measurement 60, no. 6 (2011): 1959–66. http://dx.doi.org/10.1109/tim.2011.2115410.

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Maitra, Madhubanti, and Amitava Chatterjee. "A Slantlet transform based intelligent system for magnetic resonance brain image classification." Biomedical Signal Processing and Control 1, no. 4 (2006): 299–306. http://dx.doi.org/10.1016/j.bspc.2006.12.001.

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Aggarwal, Vibha, and Manjeet Singh Patterh. "ECG compression using Slantlet and lifting wavelet transform with and without normalisation." International Journal of Electronics 100, no. 5 (2013): 626–36. http://dx.doi.org/10.1080/00207217.2012.720941.

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S., Tejas. "Extended Performance of Digital Video Watermarking using Hybrid Wavelet Transform with Haar, Cosine, Kekre, Walsh, Slant and Sine Transforms." International Journal of Computer Applications 148, no. 12 (2016): 42–46. http://dx.doi.org/10.5120/ijca2016911329.

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