color image processing
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2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lina Zhang ◽  
Yu Sang ◽  
Donghai Dai

Polar harmonic transforms (PHTs) have been applied in pattern recognition and image analysis. But the current computational framework of PHTs has two main demerits. First, some significant color information may be lost during color image processing in conventional methods because they are based on RGB decomposition or graying. Second, PHTs are influenced by geometric errors and numerical integration errors, which can be seen from image reconstruction errors. This paper presents a novel computational framework of quaternion polar harmonic transforms (QPHTs), namely, accurate QPHTs (AQPHTs). First, to holistically handle color images, quaternion-based PHTs are introduced by using the algebra of quaternions. Second, the Gaussian numerical integration is adopted for geometric and numerical error reduction. When compared with CNNs (convolutional neural networks)-based methods (i.e., VGG16) on the Oxford5K dataset, our AQPHT achieves better performance of scaling invariant representation. Moreover, when evaluated on standard image retrieval benchmarks, our AQPHT using smaller dimension of feature vector achieves comparable results with CNNs-based methods and outperforms the hand craft-based methods by 9.6% w.r.t mAP on the Holidays dataset.


2021 ◽  
Vol 2094 (2) ◽  
pp. 022001
Author(s):  
B Kh Tazmeev ◽  
V V Tsybulevsky

Abstract High-speed visualization of the discharge with a liquid cathode, color image processing were performed. The area of cathode spots concentration was identified. Statistical characteristics of the distribution of cathode spots were obtained in order to determine the range in which the intensity code of the green color channel changes, the polygon function of the empirical distribution of the intensity code for the green color channel. The graphical dependence on the frequency of the cathode spot indication hit into the specified interval of the color intensity code was created.


2021 ◽  
Vol 37 ◽  
pp. 524-543
Author(s):  
Mohamed El Guide ◽  
Alaa El Ichi ◽  
Khalide Jbilou ◽  
Rachid Sadaka

The present paper is concerned with developing tensor iterative Krylov subspace methods to solve large multi-linear tensor equations. We use the T-product for two tensors to define tensor tubal global Arnoldi and tensor tubal global Golub-Kahan bidiagonalization algorithms. Furthermore, we illustrate how tensor-based global approaches can be exploited to solve ill-posed problems arising from recovering blurry multichannel (color) images and videos, using the so-called Tikhonov regularization technique, to provide computable approximate regularized solutions. We also review a generalized cross-validation and discrepancy principle type of criterion for the selection of the regularization parameter in the Tikhonov regularization. Applications to image sequence processing are given to demonstrate the efficiency of the algorithms.


2021 ◽  
Author(s):  
Konstantinos N Plataniotis

Comprehensive Analysis of Edge Detection in Color Image Processing


2021 ◽  
Author(s):  
Konstantinos N Plataniotis

Comprehensive Analysis of Edge Detection in Color Image Processing


2021 ◽  
Vol 182 ◽  
pp. 646-660
Author(s):  
Luis Alberto Rodríguez Rodríguez ◽  
Celina Lizeth Castañeda-Miranda ◽  
Mireya Moreno Lució ◽  
Luis Octavio Solís-Sánchez ◽  
Rodrigo Castañeda-Miranda

HPB ◽  
2021 ◽  
Vol 23 ◽  
pp. S691-S692
Author(s):  
C. Gómez-Gavara ◽  
G. Piella ◽  
J. Vázquez ◽  
R. Martín ◽  
B. Parés ◽  
...  

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