Automated blurred image region classification

In this paper authors present a simple method for recognizing blurred regions in the image. Pro- posed algorithm is based on 81 simple features — moments of histogram of image subbands, that were obtained during image decomposition, and ratio derived from gray level co-occurrence matrix (GLCM) are used. The method is compared with a different method, that is based on approaches found in literature. To increase the efficiency of algorithms, authors combined three solutions (edge-detection, gray level co-occurrence matrix and fast image sharpness). The aim of the research was to verify whether it is possible to use simpler methods of feature extraction to achieve similar, or even better, results.

[1]  Phong V. Vu,et al.  A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation , 2012, IEEE Signal Processing Letters.

[2]  Sumeet Dua,et al.  Data Mining and Machine Learning in Cybersecurity , 2011 .

[3]  D. Powers.  Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation , 2008 .

[4]  Henrik Madsen,et al.  Introduction to General and Generalized Linear Models , 2010 .

[5]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[6]  Zhu Yun-fang,et al.  Blur detection for surveillance video based on heavy-tailed distribution , 2010, 2010 Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia).

[7]  Jin Zhang,et al.  Analysis of Texture Images Generated by Olfactory System Bionic Model , 2010 .

[8]  Ali N. Akansu,et al.  Emerging applications of wavelets: A review , 2010, Phys. Commun..

[9]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[10]  Hal S. Stern,et al.  CHAPMAN & HALL/CRC Texts in Statistical Science Series , 2002 .

[11]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.

[12]  Claudio Moraga,et al.  The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning , 1995, IWANN.

[13]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[14]  J. Orbach.  Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. , 1962 .

[15]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[16]  James A. Anderson,et al.  Neurocomputing: Foundations of Research , 1988 .

[17]  Gary R. Bradski,et al.  Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library , 2016 .