Object Detection from Mongolian Nomadic Environmental Images

다국어 초록 (Multilingual Abstract)

Mongolian historical and cultural monuments on settlement areas of stone inscriptions, stone images, rock-drawings, remains of cities, architecture are still telling us their stories. These monuments depict the understanding of the word, philosophical and artistic outlook, beliefs, religion, national art, language, culture and traditions of Mongols [1]. Nowadays computer science, especially computer vision is applying in the other science fields. The main problem is how to apply and which algorithm can detect and classify the objects correctly. In this paper, we propose a method to detect object from Mongolian nomadic environment images. This work proposes a method for object detection that is the combination of the binary operations in the edge detection results. We found out the best method and parameters of state-of-the-art machine learning algorithms. In experimental result, we evaluate our results with 10-fold cross validation and split 66% strategies.

참고문헌 (Reference)

• 1 임동훈, "잡음영상에서 로버스트 순위-순서 검정을 이용한 효과적인 에지검출" 한국통계학회 20 (20): 147-157, 2007
• 2 S. Mkwelo, "Watershed-based segmentation of rock scenes and proximity-based classification of watershed regions under uncontrolled lighting conditions" 107-111, 2003
• 3 L. Dashnyam, "Tseveendorj, Historical and cultural monuments of Mongolia" Munkhiin useg Inc 1999
• 4 P. Devikar, "Transfer Learning for Image Classification of various dog breeds" 5 (5): 2707-2715, 2016
• 5 D. Marr, "Theory of Edge Detection" 187-217, 1980
• 6 P. P. Acharjya, "Study and Comparison of Different Edge Detectors for Image Segmentation" 12 (12): 29-32, 2012
• 7 D. Kradolfer, "Recurrent Neural Networks for Drawing Classification"
• 8 W. Wang, "Pattern Recognition Techniques, Technology and Applications" IntechOpen Inc 2008
• 9 Gantuya. P, "Mongolian Traditional Stamp Recognition using Scalable kNN" 한국인터넷방송통신학회 4 (4): 170-176, 2015
• 10 G. Mibei, "Introduction to types and classification of rocks" 2 (2): 2014
• 11 F. Lu, "Image Segmentation Technique Used in Estimation of The Size Distribution of Rock Fragments in Mining" 1988
• 12 A. Abdelfattah, "Image Classification using Deep Neural Networks — A beginner friendly approach using TensorFlow"
• 13 Q. H. Zhang, "Edge detection models, Lecture Notes in Computer Science" 187-217, 1980
• 14 D. F. Zhang, "Digital Image Processing" 383-390, 2011
• 15 Y. Q. Lv, "Detection Algorithm of Picture Edge" 27 (27): 34-35, 2009
• 16 A. C. John, "Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence" 8 (8): 679-6987, 1986
• 17 L. Lepisto, "Color and Texture based classification of rock images using classifier combination" Tampere University of Technology 2006
• 18 N. Batbold, "Batbold, Rock art of Mongolia Archeological Relics of Mongolia" Munkhiin useg Inc 2016
• 19 X. L. Xu, "Application of Matlab in Digital Image Processing, Modern Computer" 2 (2): 01-04, 2012
• 20 C. I. Kim, "Adaptive determination of filter scales for edge detection" 14 (14): 579-585, 1992
• 21 TA. Abbasi, "A novel FPGA-based architecture for Sobel edge detection operator" 94 (94): 889-896, 2007