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Content-Based Feature Extraction: Image Transforms
Published in Rik Das, Content-Based Image Classification, 2020
Discrete Hartley transform (DHT) is termed as a Fourier-related transform of discrete periodic data, which transforms the real inputs to real outputs while avoiding the elementary association of complex numbers [4]. It has acted as a linear operator and has been the discrete analogue of the continuous Hartley Transform. DHT has been a linear, invertible function H: Rn → Rn (where R denotes the set of real numbers). Transformation of n real numbers starting from x0,..., xn−1 into n real numbers h0, …, hn−1 can be performed by equation 5.4Hk=∑n=0N−1xn[cos(2ΠNnk)+sin(2ΠNnk)]K=0,……,N−1
Separation of Machine-Printed and Handwritten Texts in Noisy Documents using Wavelet Transform
Published in IETE Technical Review, 2019
Generally, it is seen that handwritten text and noise are identified by one class. To tackle this issue, properties like texture smoothness and abrupt variations in intensity levels are exploited for these classes. Standard transforms like discrete Hartley transform [5] and discrete Fourier transform [6] extract detail information efficiently in one dimension. With reference to two-dimensional signal like an image, these transforms capture details if the two-dimensional signal is represented by a set of one-dimensional signal functions. Moreover, noise contains smooth variations in document images. These variations consist of distinguishable information that can be easily captured using discrete wavelet transform (DWT) at different resolution levels and in different directions. In addition, the features based on means and variances of a wavelet-decomposed image provide information related to energy distribution of sub-bands. Hence, these features characterize the textures more effectively [7]. Recent transforms like curvelets [8] and contourlets [9] also pose multi-directional properties and are proficient to approximate the smooth curves or variations of texts easily. However, these transforms are complicated in nature and computationally expensive. As a result, training and testing of big databases using these transforms become very slow. Herein the proposed algorithm, directionality of DWT is further enhanced by combining it with SDDWT.
Markov chain latent space probabilistic for feature optimization along with Hausdorff distance similarity matching in content-based image retrieval
Published in The Imaging Science Journal, 2022
In this study [22], they provide a method for segmenting the brain vasculature in magnetic resonance angiography and low-contrast computed tomography angiography. A stochastic resonance-based method is suggested to improve the contrast of a chosen angiographic image for patch placement in the discrete Hartley transform domain. Vasculature is separated from the phase-map data using the level-set approach. The calculated average dice coefficient (in%) is 94.11.2.
Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks
Published in IETE Journal of Research, 2023
M. Prabhu, B. Muthu Kumar, A. Ahilan
In 2022 Xu, X.Y., et al [20] proposed a discrete Hartley transform-based optical orthogonal frequency division multiplexing system with index modulation. The hardest part of optical OFDM is getting actual, non-negative time-domain signals that are good for direct detection/intensity modulation using VLC systems. The most advantageous choice in terms of BER performance and computational complexity, according to simulation results.