WILLIAMS, Ian, Nicholas BOWRING and David SVOBODA. A performance evaluation of statistical tests for edge detection in textured images. Computer Vision and Image Understanding. Elsevier, 2014, vol. 122, May 2014, p. 115-130. ISSN 1077-3142. Available from: https://dx.doi.org/10.1016/j.cviu.2014.02.009. |
Other formats:
BibTeX
LaTeX
RIS
@article{1173216, author = {Williams, Ian and Bowring, Nicholas and Svoboda, David}, article_number = {May 2014}, doi = {http://dx.doi.org/10.1016/j.cviu.2014.02.009}, keywords = {Edge detection; Statistical tests; Textured images; Histological images; Performance measures}, language = {eng}, issn = {1077-3142}, journal = {Computer Vision and Image Understanding}, title = {A performance evaluation of statistical tests for edge detection in textured images}, volume = {122}, year = {2014} }
TY - JOUR ID - 1173216 AU - Williams, Ian - Bowring, Nicholas - Svoboda, David PY - 2014 TI - A performance evaluation of statistical tests for edge detection in textured images JF - Computer Vision and Image Understanding VL - 122 IS - May 2014 SP - 115-130 EP - 115-130 PB - Elsevier SN - 10773142 KW - Edge detection KW - Statistical tests KW - Textured images KW - Histological images KW - Performance measures N2 - This work presents an objective performance analysis of statistical tests for edge detection which are suitable for textured or cluttered images. The tests are subdivided into two-sample parametric and non-parametric tests and are applied using a dual-region based edge detector which analyses local image texture difference. Through a series of experimental tests objective results are presented across a comprehensive dataset of images using a Pixel Correspondence Metric (PCM). The results show that statistical tests can in many cases, outperform the Canny edge detection method giving robust edge detection, accurate edge localisation and improved edge connectivity throughout. A visual comparison of the tests is also presented using representative images taken from typical textured histological data sets. The results conclude that the non-parametric Chi Square and Kolmogorov Smirnov statistical tests are the most robust edge detection tests where image statistical properties cannot be assumed a priori or where intensity changes in the image are nonuniform and that the parametric Difference of Boxes test and the Student’s t-test are the most suitable for intensity based edges. Conclusions and recommendations are finally presented contrasting the tests and giving guidelines for their practical use while finally confirming which situations improved edge detection can be expected. ER -
WILLIAMS, Ian, Nicholas BOWRING and David SVOBODA. A performance evaluation of statistical tests for edge detection in textured images. \textit{Computer Vision and Image Understanding}. Elsevier, 2014, vol.~122, May 2014, p.~115-130. ISSN~1077-3142. Available from: https://dx.doi.org/10.1016/j.cviu.2014.02.009.
|