Applying for Image Comparison with the Cumulative Frequency of The Pixel Color Scale
Keywords:
Cumulative Frequency, Image Processing, Color Scale, Digital Image, Application SoftwareAbstract
Objectives of this study To develop a prototype program to compare the similarity of images. Methods for studying and designing and developing a program to compare the similarity of images. which can be applied to work systems that require general photo inspection It uses the principle of detecting the intensity of the color level in each pixel and the cumulative frequency of the color level in each range.
The results also measure the efficiency of the image processing system. newly developed algorithm Out of a group of 30 sample images, on the Pixels 8,000 to 10,000 equivalence scale, 63.33% is considered high. Medium and on the parity scale of Pixels 6,000 to 7,000 there is 13.33%. The level of similarity is fair.
In summary, the image comparison performance at the 8,000 to 10,000 pixel level is quite accurate and suitable for application in image comparison processing applications. But the developed system should be tested and compared with the system or related research to determine the accuracy of the developed system and can be used. applied to further improve the quality
References
ทรรศ จอมขันธิพล. (2565). ระบบจดจำใบหน้า. สืบค้นจาก http://cpe.rsu.ac.th/ut/courses/T151/cpe489/portfolio /482316/FaceRecognition.ppt.
สนั่น ศรีสุข,คำรณ สุนัติ, และ วีระศักดิ์ คุรุธัช. (ม.ป.ป.). การค้นหาภาพใบหน้าโดยใช้ใบหน้าไอเกนและการวิเคราะห์สหสัมพันธ์. สืบค้นจาก http://dspace.bru.ac.th/xmlui/bitstream/handle/123456789/6999
GitHub Pages. (2019). The Database of Faces (AT&T). Retrieved from https://git-disl.github.io /GTDLBench/datasets /att_face_dataset.
Gonzalez R. C. Woods R. E. & Eddins S. L. (2004). Digital Image Processing using MATLAB, USA: Pearson Prentice: Hall Upper Saddle River, NJ Publisher.
Hamid, R. A., & Thom, J. A. (2010, December). Criteria that have an effect on users while making image relevance judgments. In Proceedings of the fifteenth Australasian document computing symposium (pp. 76-83). Australia.
Jia, W., Zhang, H., He, X., & Wu, Q. (2006, October). Image matching using colour edge cooccurrence histograms. In 2006 IEEE International Conference on Systems, Man and Cybernetics (Vol. 3, pp. 2413-2419). IEEE.
Lin, S. H. (2000). An introduction to face recognition technology. Informing Sci. Int. J. an Emerg. Transdiscipl, 3, 1-7.
Mark Rouse. (2005). Comparing Images Using GDI+. Retrieved from http://www.codeproject.com/KB/GDI-plus/ comparingimages.aspx.
Metz, C. E. (1978, October). Basic principles of ROC analysis. In Seminars in nuclear medicine (Vol. 8, No. 4, pp. 283-298). WB Saunders.
Rathi, R., Choudhary, M., Tech, M., & Chandra, B. (2012). An Application of Face Recognition System using Image Processing and Neural Networks. Int. J. Comp. Tech. Appl, 3(1), 45-49.
Robert Nowak. (n.d.). Digital Image Processing Basics. Retrieved from http://cnx.org/content/m10973/2.2.
software test technique. http://www.slideshare.net/Softwarecentral/software-test-technique.
software test technique. http://www.springerlink.com/index/t14421u508822507.pdf
Sourav Banerjee. (n.d.). Image Comparison in C#. Retrieved from http://www.dotnetspider.com/resources/19811-Image-Compare-C.aspx.
Taylor, J. (1997). Introduction to error analysis, the study of uncertainties in physical measurements (2nd ed.). NY: University Science Books.
Taylor, J. (1999). An Introduction to Error Analysis the Study of Uncertainties in Physical Measurements. NY: University Science Books.
Wikipedia. (n.d.). Principal component analysis. Retrieved from http://en.wikipedia.org/wiki/Principal_ component_analysis
Wong, K. M., Cheung, C. H., & Po, L. M. (2002, September). Merged-color histogram for color image retrieval. In Proceedings. International Conference on Image Processing (Vol. 3, pp. 949-952). IEEE.
Wu, X. (1991). Efficient statistical computations for optimal color quantization. In Graphics Gems II (pp. 126-133). Morgan Kaufmann.
