Relevant bibliographies by topics / Hybrid Kekre and Haar Wavelet Transforms

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Academic literature on the topic 'Hybrid Kekre and Haar Wavelet Transforms'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Hybrid Kekre and Haar Wavelet Transforms"

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Er., Jagdeep Singh*, and Parminder Singh Er. "IMAGE COMPRESSION WITH TILING USING HYBRID KEKRE AND HAAR WAVELET TRANSFORMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 321–25. https://doi.org/10.5281/zenodo.47044.

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In today’s life with the increasing use of digital data in every field every organization has to handle large amount of data. To store this digital data, large disk space is needed.   In recent years a lot of research has been done to compress the size of digital data which helps to store data in low disk space and to save the transmission bandwidth required to transmit data. Different types of transforms have been successfully used to compress the images. Image compression can be done by use of single transform or by hybrid of different transforms. Research shows hybrid transf
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Chavan, Manoj, Ravish R. Singh, and Vinayak Bharadi. "Online signature verification using hybrid wavelet transform." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1823. http://dx.doi.org/10.11591/ijece.v10i2.pp1823-1832.

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Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vecto
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Manoj, Chavan, R. Singh Ravish, and Bharadi Vinayak. "Online signature verification using hybrid wavelet transform." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1823–32. https://doi.org/10.11591/ijece.v10i2.pp1823-1832.

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Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vecto
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Manoj Chavan. "Handwritten online signature verification and Forgery Detection for Online Handwritten Signature using Hybrid Wavelet Transform-1 with HMM classifier." Journal of Electrical Systems 20, no. 4s (2024): 2471–78. http://dx.doi.org/10.52783/jes.2800.

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Online signature verification employs a distinctive biometric trait by utilizing both static and dynamic features extracted from 2D signature images. A hybrid wavelet transform, denoted as HWT-1 with a size of 256, is formed through the Kronecker product of two orthogonal transforms, such as DCT, DHT, Haar, Hadamard, and Kekre, each with sizes 4 and 64. This HWT facilitates signal analysis at both global and local levels, akin to traditional wavelet transforms. Specifically, HWT-1 processes the 256 samples of online handwritten signatures, yielding 128 samples that constitute the feature vecto
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Manoj Chavan. "Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier." Journal of Electrical Systems 20, no. 4s (2024): 2453–62. http://dx.doi.org/10.52783/jes.2798.

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Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verific
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Manoj Chavan. "Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier." Journal of Electrical Systems 20, no. 4s (2024): 2463–70. http://dx.doi.org/10.52783/jes.2799.

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Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the ve
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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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Kekre, Dr H. B., Sudeep D. Thepade, and Akshay Maloo. "Performance Comparison of Image Retrieval Techniques using Wavelet Pyramids of Walsh, Haar and Kekre Transforms." International Journal of Computer Applications 4, no. 10 (2010): 1–8. http://dx.doi.org/10.5120/866-1216.

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Geetika Agotra and Prof. Manish Kumar Singhal. "A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 143–49. http://dx.doi.org/10.32628/ijsrst2411430.

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This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular
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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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Book chapters on the topic "Hybrid Kekre and Haar Wavelet Transforms"

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Kekre, H. B., Sudeep D. Thepade, and Ratnesh N. Chaturvedi. "“Color to Gray and Back” Using DST-DCT, Haar-DCT, Walsh-DCT, Hartley-DCT, Slant-DCT, Kekre-DCT Hybrid Wavelet Transforms." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1768-8_54.

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Conference papers on the topic "Hybrid Kekre and Haar Wavelet Transforms"

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Kulkarni, Tejas S., and Jaya H. Dewan. "Digital video watermarking using Hybrid wavelet transform with Cosine, Haar, Kekre, Walsh, Slant and Sine transforms." In 2016 International Conference on Computing Communication Control and automation (ICCUBEA). IEEE, 2016. http://dx.doi.org/10.1109/iccubea.2016.7860055.

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Thepade, Sudeep D., and Jaya H. Dewan. "Image compression using hybrid wavelet transform with varying proportions of cosine, Haar, Walsh and Kekre transforms with assorted color spaces." In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2014. http://dx.doi.org/10.1109/iccicct.2014.6993197.

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Thepade, Sudeep D., and Smita S. Chavan. "Robust image steganography with wavelet transform and Hybrid wavelet transform generated using Kekre, Walsh and sine transforms." In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2013. http://dx.doi.org/10.1109/icacci.2013.6637483.

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Thepade, Sudeep D., and S. Erandole. "Effect of tiling in image compression using wavelet transform & hybrid wavelet transform for cosine & kekre transforms." In 2013 International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN). IEEE, 2013. http://dx.doi.org/10.1109/ice-ccn.2013.6528604.

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Thepade, Sudeep, and Pushpa R. Mandal. "Energy compaction based novel Iris recognition techniques using partial energies of transformed iris images with Cosine, Walsh, Haar, Kekre, Hartley Transforms and their Wavelet Transforms." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030641.

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Thepade, Sudeep D., and Yogita D. Shinde. "Robust CBIR using sectorisation of hybrid wavelet transforms with Cosine-Walsh, Cosine-Kekre, Cosine-Hartley combinations." In 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/pervasive.2015.7087060.

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Thepade, Sudeep D., and Nalini Yadav. "Partial energy of hybrid wavelet transformed videos for content based video retrieval with various similarity measures using Cosine, Haar and Walsh transforms." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342663.

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