A DCT Based Filtering of Biomedical Images

Gupta, Pradeep Kumar; Rajan, K.

doi:10.1109/icit.2006.372657

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GHT algorithm has been successfully used for pattern recognition [23], detection [24], and classification [6]. Due to its high computational cost [25], a few variants of the GHT, such as the invariant generalized Hough transform [25], the randomized Hough transform [26], and the randomized, generalized Hough transform [9] have been developed to improve the detection time and storage capacity.…”

Section: Generalized Hough Transformmentioning

confidence: 99%

Generalized Hough Transform for Object Classification in the Maritime Domain

Rerkngamsanga¹

2015

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. FUNDING NUMBERS AUTHOR(S) Pornrerk Rerkngamsanga PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Naval Postgraduate School Monterey, CA 93943-5000 PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES)N/A SPONSORING / MONITORING AGENCY REPORT NUMBER11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. IRB Protocol number ____N/A____. 12a. DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited 12b. DISTRIBUTION CODE ABSTRACT (maximum 200 words)A Generalized Hough Transform (GHT)-based classification scheme for an object-of-interest in maritime-domain images is proposed in this thesis. First, the object edge points are extracted and used to generate a representation of the object as a Hough coordinate table by using the GHT algorithm. The table is then reformatted to a contour map called a Hough features map. The coordinates of dominant peaks or Hough features on the map are extracted and fed into a feed-forward, back-propagation neural network for classification. In this research, the scheme is tested using perfect shapes of triangles, squares, circles, and stars and maritime-domain images of ships, aircraft, and clouds, and the classification results obtained are reported. SUBJECT TERMS

show abstract