Recent trends in image processing and pattern recognition
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Recent trends in image processing and pattern recognition K. C. Santosh 1 & Sameer K. Antani 2 # Springer Science+Business Media, LLC, part of Springer Nature 2020
The Call for Papers of the special issue was initially sent out to the participants of the 2018 conference (2nd International Conference on Recent Trends in Image Processing and Pattern Recognition). To attract high quality research articles, we also accepted papers for review from outside the conference event. Of 123 submissions, 22 papers were accepted. The acceptance rate, therefore, is just under 18%. In “Multilevel Polygonal Descriptor Matching Defined by Combining Discrete Lines and Force Histogram Concepts,” authors presented a new method to describe shapes from a set of polygonal curves using a relational descriptor. In their study, relational descriptor is the main idea of the paper. In “An Asymmetric Cryptosystem based on the Random Weighted Singular Value Decomposition and Fractional Hartley Domain,” authors proposed an encryption system for double random phase encoding based on random weighted singular value decomposition and fractional Hartley transform domain. Authors claimed that the proposed cryptosystem is efficiently compared with singular value decomposition and truncated singular value decomposition. In “Classification of Complex Environments using Pixel Level Fusion of Satellite Data,” authors analyzed composite land features by fusing two original hyperspectral and multispectral datasets. In their study, the fusion image technique was found to be superior to the single original image. In “Image Dehazing using Window-based Integrated Means Filter,” authors reported that the proposed technique outperforms the state-of-the-arts in single image dehazing approaches. In “Research on Fundus Image Registration and Fusion Method based on Nonsubsampled Contourlet and Adaptive Pulse Coupled Neural Network,” authors presented a registration and fusion method of fluorescein fundus angiography image and color fundus image that combines
* K. C. Santosh [email protected] Sameer K. Antani [email protected]
1
University of South Dakota, Vermillion, SD 57069, USA
2
U.S. National Library of Medicine, NIH, Bethesda, MD 20894, USA
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Nonsubsampled Contourlet (NSCT) and adaptive Pulse Coupled Neural Network (PCNN). Authors claimed that the image fusion provides an effective reference for the clinical diagnosis of fundus diseases. In “Super Resolution of Single Depth Image based on Multi-dictionary Learning with Edge Feature Regularization,” authors focused on super resolution based on multi-dictionary learning with edge regularization model. With this, the reconstructed depth images were found to be superior with respect to the state-of-art methods. In “A Universal Foreground Segmentation Technique using Deep Neural Network,” authors presented an idea of optical-flow details to make use of temporal information in deep neural network. In “Removal of ‘Salt & Pepper’ Noise from Color Images using Adaptive Fuzzy Tec
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