Dept. of Mathematics and Computer Science, Faculty of Science, Federal University, Kashere, Gombe, Nigeria
The essence of the study is to analyse an algorithm which will provide a robust and computationally light method, which might be suitable to implement in the real-time industrial application such as object detection and recognition. For industrial applications, the primary step in automatic detection and classification of an object is to find the object automatically from an image using features related to its shape. This chore is a very complex one. Therefore, to hit the target Histogram of oriented gradient (HOG) algorithm is selected to extract the image features. Average Magnitude Difference Function AMDF is employed to correct the alignment defect. Finally, Artificial Neural Network (ANN) was employed to detect the type of object in the image efficiently. None the less, a database was generated. The database consists of images of real industrial products which are of different shapes and sizes, captured under different lightning conditions. The outcome of the experiment conducted on the database recorded 98.10% success.
F. S. Ishaq, I. A. Alhaji,Halis Altun,Y. Atomsa, M. L. Jibrin, S. A. Sani(2018). Evaluation of Industrial Based Object Detection Method Using Artificial Neural Network. International Journal of Computer Engineering In Research Trends, 5(2), 50-55. Retrieved from http://ijcert.org/ems/ijcert_papers/V5I207.pdf
: 1-D mask, HOG algorithm, AMDF algorithm, k-nearest Neighbours algorithm, Cross-correlation Functions algorithms and MLP algorithm
 Demirci, B., Arslan, O., Tunaboylu, N. S., & Altun, H. (2013, May). Implementing HOG & AMDF based shape detection algorithm for computer vision & robotics education using LEGO Mindstorms NXT. In Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on (pp. 288-293). IEEE.
 Ahsan, A. M., & Mohamad, D. B. (2013). Features Extraction for Object Detection Based on Interest Point. TELKOMNIKA Indonesian Journal of Electrical Engineering, (Vol. 11, No. 5, pp. 2716-2722).
 Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: A survey. Acm computing surveys (CSUR), (Vol. 38, No. 4, pp. 13).
 Comaniciu, D., & Meer, P. (1999). Mean shift analysis and applications. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on (Vol. 2, pp. 1197-1203). IEEE.
 Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (Vol. 22, No. 8, pp. 888-905).
 Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International journal of computer vision, (Vol. 22, No. 1, pp. 61-79).
 Stauffer, C., & Grimson, W. E. L. (2000). Learning patterns of activity using real-time tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (Vol. 22, No. 8, pp. 747-757).
 Oliver, N. M., Rosario, B., & Pentland, A. P. (2000). A Bayesian computer vision system for modelling human interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (Vol. 22, No. 8, pp. 831-843).
 Monnet, A., Mittal, A., Paragios, N., & Ramesh, V. (2003, October). Background modelling and subtraction of dynamic scenes. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on (pp. 1305-1312). IEEE.
 Harris, C., & Stephens, M. (1988, August). A combined corner and edge detector. In Alvey vision conference (Vol. 15, p. 50).
 Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, (Vol. 60, No. 2, pp. 91-110).
 Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, (Vol. 110, No. 3, pp. 346-359).
 Shashua, A., Gdalyahu, Y., & Hayun, G. (2004, June). Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In Intelligent Vehicles Symposium, 2004 IEEE (pp. 1-6). IEEE.
 Dadal, N. and Triggs, B.: Finding People in Images and Videos, PhD thesis, French National Institute for Research in Computer Science and Control (INRIA), July 2006.
 Peker, M., Altun, H., & Karakaya, F. (2012, October). Hardware emulation of HOG and AMDF based scale and rotation invariant robust shape detection. In Engineering and Technology (ICET), 2012 International Conference on (pp. 1-5). IEEE.
  Arslan, O., Demirci, B., Altun, H., & Tunaboylu, N. S. (2013, April). A novel rotation- invariant template matching based on HOG and AMDF for industrial laser cutting applications. In Mechatronics and its Applications (ISMA), 2013 9th International Symposium on (pp. 1-5). IEEE.
 Papageorgiou, C. P., Oren, M., & Poggio, T. (1998, January). A general framework for object detection. In Computer vision, 1998. Sixth international conference on (pp. 555-562). IEEE.
 Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (Vol. 20, No. 1, pp. 23-38.
 Viola, P., Jones, M. J., & Snow, D. (2003, October). Detecting pedestrians using patterns of motion and appearance. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on (pp. 734-741). IEEE.