Monitor systems are ubiquitously deployed in public areas. However, monitor systems face a major challenge regarding long-distance object recognition. Super-resolution constitutes a popular choice to address this challenge. Since super-resolution methods are used in many applications, it is necessary to understand these methods and make a comparative study of them. In this paper, we perform a comparative study on six super-resolution methods over two recognition algorithms. The paper evaluates super-resolution performance based on recognition accuracy, and serves as a summary assessment of image super-resolution algorithms. © 2018 IEEE.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

X. Yang

W. Wu

K. Liu

P. W. Kim

G. Jeon

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

IEEE Access

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms. © 2005-2012 IEEE.

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Journal	Data powered by TypesetIEEE Access
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers (IEEE)
ISSN	21693536
Open Access	Yes