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01-07-2019 | REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES | Issue 3/2019

Pattern Recognition and Image Analysis 3/2019

Panoramic Image Mosaics via Distributed Systems Using Color Moments and Local Wavelet-Features

Journal:
Pattern Recognition and Image Analysis > Issue 3/2019
Authors:
Feng Guo, Ying Wang
Important notes
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819030088/MediaObjects/11493_2019_6006_Fig3_HTML.gif
Feng Guo is an associate professor at the Department of Information Technology, Qingdao Vocational and Technical College of Hotel Management. He is currently the Head of the Department of Information Technology. He has authored over 20 journal and conference papers and holds 2 granted national patents. Prof. Guo’s research interest includes communication networks and distributed systems, pattern analysis and image processing.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819030088/MediaObjects/11493_2019_6006_Fig4_HTML.gif
Ying Wang is an associate professor at the Department of Information Technology, Qingdao Vocational and Technical College of Hotel Management. She has published 20 research papers and holds 1 granted national patents. Her current research interests include pattern analysis and image processing, communication networks and distributed computing.

Abstract

In this paper, an efficient method based on color moments and local wavelet-features is proposed for panoramic image mosaics. Color moments are used to efficiently represent physical quantities of objects in an image. Wavelet can describe a wide variety of image characteristics and is a key component for image-related applications. Therefore, during the feature-extraction stage for panoramic image mosaics, we exploit color moments and wavelet-subband statistics to construct local feature vectors for image-patch representation. With a distributed system of local area network, the proposed mosaics system can achieve an average 295 FPS. Experimental results show that the local wavelet-features are able to produce plausible and satisfactory panoramic images.

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