Object Detection and Recognition Using Local Quadrant Pattern
DOI:
https://doi.org/10.31642/JoKMC/2018/060202Keywords:
Local Quadrant Pattern (LQP), Object Detection, Threshold , Local Ternary Pattern (LTP) , Skin , CancerAbstract
Object detection and recognition is one of the important techniques in computer vision for searching and scanning and identifying an object in images or videos. Object detection and recognition enters into many important fields where one of the uses of object detection and recognition is to detect region of injury and determine the type of injury. This paper suggested a new effective method called Local Quadrant Pattern (LQP). The proposed method uses a window and passes it on all pixels of the image and uses the pixel direction to arrange the adjacent pixels. It also uses four code values to encode and then produce a texture feature matrix which is used to detect objects as well as extract features based on magnitude of pixels for image classification. The experiments were conducted on the infected regions in the skin and the results showed the ability of the method to detect regions of infection as well as the high accuracy in the classification of those regions.Downloads
References
O. Stenroos, "Object detection from images using convolutional neural networks," Master, , Communication and Information Sciences, Aalto University, 2017.
H. A. Mohammed, "Object detection and recognition in complex scenes," Master, Computer Science Engineering, University of Algarve, 2014.
Y. Amit and P. Felzenszwalb, "Object Detection," Computer Vision: A Reference Guide, pp. 537-542, 2014. DOI: https://doi.org/10.1007/978-0-387-31439-6_660
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models," IEEE transactions on pattern analysis and machine intelligence, vol. 32, pp. 1627-1645, 2010. DOI: https://doi.org/10.1109/TPAMI.2009.167
K. R. Kumar, V. B. Prakash, V. Shyam, and M. A. Kumar, "Texture and Shape based Object Detection Strategies," Indian Journal of Science and Technology, vol. 9, 2016. DOI: https://doi.org/10.17485/ijst/2016/v9i30/98709
M. Burić, M. Pobar, and M. Ivašić-Kos, "Object Detection in Sports Videos," in 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018. DOI: https://doi.org/10.23919/MIPRO.2018.8400189
A. Santosh and G. Sadashivappa, "Skin cancer detection and diagnosis using image processing and Implementation using neural networks and ABCD parameters," International Journal of Electronics, Communication& Instrumentation EngineeringResearch and Development (IJECIERD), vol. 4, pp. 85-96, 2014.
R. Sumithra, M. Suhil, and D. Guru, "Segmentation and classification of skin lesions for disease diagnosis," Procedia Computer Science, vol. 45, pp. 76-85, 2015. DOI: https://doi.org/10.1016/j.procs.2015.03.090
S. Jain and N. Pise, "Computer aided melanoma skin cancer detection using image processing," Procedia Computer Science, vol. 48, pp. 735-740, 2015. DOI: https://doi.org/10.1016/j.procs.2015.04.209
R. Kasmi and K. Mokrani, "Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule," IET Image Processing, vol. 10, pp. 448-455, 2016. DOI: https://doi.org/10.1049/iet-ipr.2015.0385
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Copyright (c) 2019 Asaad Hashim, Hassan Mahdi
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