A Fast Orientation Estimation Approach of Natural Images

This correspondence paper proposes a fast orientation estimation approach of natural images without the help of semantic information. Different from traditional low-level features, our low-level features are extracted inspired by the biological simple cells of the visual cortex. Two approximated receptive fields to mimic the biological cells are presented, and a local rotation operator is introduced to determine the optimal output and local orientation corresponding to an image position, which serve as the low-level feature employed in this paper. To generate the low-level features, a bisection method is applied to the first derivative of the model of receptive fields. Moreover, the feature screener is introduced to eliminate too much useless low-level features, which will speed up the processing time. After all the valuable low-level features are combined, the overall image orientation is estimated. The proposed approach possesses several features suitable for real-time applications. First, it avoids the tedious training procedure of some conventional methods. Second, no specific reference such as the horizon is assumed and no a priori knowledge of image is required. The proposed approach achieves a real-time orientation estimation of natural images using only low-level features with a satisfactory resolution. The effectiveness of our proposed approach is verified on real images with complex scenes and strong noises.

[1]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[2]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  J. Bigun,et al.  Optimal Orientation Detection of Linear Symmetry , 1987, ICCV 1987.

[4]  Zoran A. Ivanovski,et al.  Automatic image orientation detection with prior hierarchical content-based classification , 2011, 2011 18th IEEE International Conference on Image Processing.

[5]  Josef Bigün,et al.  N-folded Symmetries by Complex Moments in Gabor Space and their Application to Unsupervised Texture Segmentation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[7]  Marc Schlipsing,et al.  Video-based roll angle estimation for two-wheeled vehicles , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[8]  Yongmei Wang,et al.  Content-based image orientation detection with support vector machines , 2001, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL 2001).

[9]  Hans Knutsson,et al.  Robust N-dimensional orientation estimation using quadrature filters and tensor whitening , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  J. Pablo Marquez,et al.  Fourier analysis and automated measurement of cell and fiber angular orientation distributions , 2006 .

[11]  Pierre Baylou,et al.  Estimating local multiple orientations , 2007, Signal Process..

[12]  Eero P. Simoncelli,et al.  Steerable wedge filters for local orientation analysis , 1996, IEEE Trans. Image Process..

[13]  Nam Ik Cho,et al.  Skew estimation of natural images based on a salient line detector , 2013, J. Electronic Imaging.

[14]  Nanning Zheng,et al.  Skew Estimation of Document Images Using Bagging , 2010, IEEE Transactions on Image Processing.

[15]  Carl-Fredrik Westin,et al.  Representing Local Structure Using Tensors II , 2011, SCIA.

[16]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[17]  Anil K. Jain,et al.  Automatic image orientation detection , 2002, IEEE Trans. Image Process..

[18]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[19]  HongJiang Zhang,et al.  Detecting image orientation based on low-level visual content , 2004, Comput. Vis. Image Underst..

[20]  Jiebo Luo,et al.  Automatic image orientation detection via confidence-based integration of low-level and semantic cues , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  H. Knutsson,et al.  Orientation Estimation in Ambiguous Neighbourhoods , 1992 .

[22]  Raimondo Schettini,et al.  Image orientation detection using LBP-based features and logistic regression , 2013, Multimedia Tools and Applications.

[23]  Bidyut Baran Chaudhuri,et al.  Skew Angle Detection of Digitized Indian Script Documents , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Xu Liu,et al.  Image orientation detection with integrated human perception cues (or which way is up) , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[25]  Jaromír Antoch,et al.  Estimation of fiber system orientation for nonwoven and nanofibrous layers: local approach based on image analysis , 2014 .

[26]  Hermann Winner,et al.  Roll angle estimation for motorcycles: Comparing video and inertial sensor approaches , 2012, 2012 IEEE Intelligent Vehicles Symposium.