Texture segmentation using local energy in wavelet scale space

1996 ◽  
pp. 304-313 ◽  
Author(s):  
Zhi-Yan Xie ◽  
Michael Brady
Keyword(s):  
Scale Space ◽  
Texture Segmentation ◽  
Local Energy

scholarly journals Natural Scene Text Detection and Segmentation Using Phase-Based Regions and Character Retrieval

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Julia Diaz-Escobar ◽  
Vitaly Kober
Keyword(s):  
Recognition System ◽  
Scale Space ◽  
Text Detection ◽  
Local Energy ◽  
Natural Scene ◽  
Phase Congruency ◽  
Segmentation Method ◽  
Low Contrast ◽  
Scene Text Detection ◽  
Candidate Regions

Multioriented text detection and recognition in natural scene images are still challenges in the document analysis and computer vision communities. In particular, character segmentation plays an important role in the complete end-to-end recognition system performance. In this work, a robust multioriented text detection and segmentation method based on a biological visual system model is proposed. The proposed method exploits the local energy model instead of a common approach based on variations of local image pixel intensities. Features such as lines and edges are obtained by searching for the maximum local energy utilizing the scale-space monogenic signal framework. The candidate text components are extracted from maximally stable extremal regions of the local phase information of the image. The candidate regions are filtered by their phase congruency and classified as text and nontext components by the AdaBoost classifier. Finally, misclassified characters are restored, and all final characters are grouped into words. Experimental results show that the proposed text detection and segmentation method is invariant to scale and rotation changes and robust to perspective distortions, blurring, low resolution, and illumination variations (low contrast, high brightness, shadows, and nonuniform illumination). Besides, the proposed method achieves often a better performance compared with state-of-the-art methods on typical natural scene datasets.

NeighShrink Despeckling for SAR Images Based on Scale Space Correlation

2011 ◽  
Vol 30 (8) ◽  
pp. 1940-1943
Author(s):  
Heng-chao Li ◽  
Wen Hong ◽  
Yi-rong Wu
Keyword(s):  
Scale Space ◽  
Sar Images ◽  
Space Correlation

Capture Strategy Based on a Distributed Control System for a Large-scale Space End-Effector

ROBOT ◽  
2011 ◽  
Vol 33 (4) ◽  
pp. 434-439 ◽  
Author(s):  
Dangyang JIE ◽  
Fenglei NI ◽  
Yisong TAN ◽  
Hong LIU ◽  
Hegao CAI
Keyword(s):  
Control System ◽  
Distributed Control ◽  
Large Scale ◽  
Scale Space ◽  
Distributed Control System ◽  
End Effector

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

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