Reducing Image Size and Noise Removal in Fast Object Detection using Wavelet Transform Neural Network
الموضوعات :
mahmoud jeddi
1
,
Ahmad Khoogar
2
,
Ali Mehdipoor Omrani
3
1 - university complex of materials and manufacturing technology,Malek Ashtar university of technology,Tehran,Iran
2 - Department of Mechanical Engineering
Maleke Ashtar University of Technology
3 - Departement Of Mechanical Engineering, Malek Ashtar university of Technology, Lavizan, Iran
الکلمات المفتاحية: Object Detection, Wavelet Transform, Feature extraction, Neural network, Image compression,
ملخص المقالة :
A robot detects its surroundings through camera information and its response requires a high-speed image process. Due to the increasing application of vision systems, various algorithms have been developed to increase speed of image processing. This paper proposes a double density Discrete Wavelet-based Neural Network to enhance feature extraction and classification of parts in each picture. The Discrete Wavelet-based Neural Network combines multi-scale analysis ability of the wavelet transform and the classification capability of the artificial neural network by setting the wavelet function as the transfer function of the neural network. The automatic assembly process needs to capture the image in an online process in order to recognize the parts in the image and identify the location and orientation of the parts. In this part, the two dimensional double density discrete wavelet transform have been applied to compress and remove noise from the captured Image. By applying a value for the threshold, the coefficients of the wavelet transform function are obtained using these coefficients and the characteristics of the wavelet coefficients are calculated. Subsequently, a multilayer perceptron is trained using these extracted features of the images. To find the best vector characteristics, various combinations of extracted properties have been investigated. This method has succeeded in object detection and results show that the Neural Networks and the training algorithm based on the wavelet transform function have exquisite accuracy in classification. Thus, the developed method is considered effective as compared to other state-of-the-art techniques.
[1] Rojas, R. He. J, Guan, Y., A 3D Object Detection And Pose Estimation Pipeline Using RGB-D Images, IEEE International Conference on Robotics and Biomimetics ROBIO, 2017, pp. 1527–1532.
[2] Mallat, S., A Wavelet Tour of Signal Processing, Elsevier, the Sparse Way, 3rd Edition, 2008, pp. 270-320, 9780123743701.
[3] Verschae, R., Ruiz, D. S., Object Detection: Current And Future Directions, Front. Robot. AI, Vol. 2, 2015 pp. 29.
[4] Ruiz, L. A., Fdez-Sarria, A., and Recio, J. A., Texture feature Extraction for Classification of Remote Sensing Data Using Wavelet Decomposition: A Comparative Study, in 20th ISPRS Congress, Vol. 35, No. part B, 2004, pp. 1109–1114.
[5] Oren, M. C., Papageorgiou, P., Sinha, Osuna, E., and Poggio., T., Pedestrian Detection Using Wavelet Templates, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997, pp. 193–199.
[6] Starck, J. L., Elad, M., and Donoho, D. L., Image Decomposition via the Combination of Sparse Representations and a Variational Approach, IEEE Trans. Image Process., Vol. 14, No. 10, 2005, pp. 1570–1582.
[7] Calderbank, A. R., Daubechies, I., Sweldens, W., and Yeo, B. L., Lossless Image Compression Using Integer To Integer Wavelet Transforms, in Proceedings of International Conference on Image Processing, Vol. 1, 1997, pp. 596–599.
[8] Ellinas, J. N., Sangriotis, M. S., Stereo Image Compression Using Wavelet Coefficients Morphology, Image Vis. Comput., Vol. 22, No. 4, 2004, pp. 281–290.
[9] Lalonde, M., Beaulieu, M., and Gagnon, L., Fast and Robust Optic Disc Detection Using Pyramidal Decomposition and Hausdorff-Based Template Matching, IEEE Trans. Med. Imaging, Vol. 20, No. 11, 2001, pp. 1193–1200.
[10] Yap, V. V., Wavelet-Based Image Compression for Mobile Applications, Ph.D. Dissertation, Middlesex University, School of Computing Science, 2005, pp. 66-120.
[11] Vimala, C., Priya, P. A., Artificial Neural Network Based Wavelet Transform Technique for Image Quality Enhancement, Comput. Electr. Eng., Vol. 76, 2019, pp. 258–267.
[12] Strang, G., Nguyen, T., Wavelets and Filter Banks, SIAM, Well Cambridge Press, USA, 1996, pp. 425-440.
[13] Azzalini, A., Farge, M., and Schneider, K., Nonlinear Wavelet Thresholding: a Recursive Method to Determine the Optimal Denoising Threshold, Appl. Comput. Harmon. Anal., Vol. 18, No. 2, 2005, pp. 177–185.
[14] Avci, E., An Expert System Based on Wavelet Neural Network-Adaptive Norm Entropy for Scale Invariant Texture Classification, Expert Syst. Appl., Vol. 32, No. 3, 2007, pp. 919–926.
[15] Parker, J. R., Algorithms for Image Processing and Computer Vision, John Wiley & Sons, second Edition, USA, 2010, pp. 425-440, 978-0-470-64385-3.
[16] Subha, P., Automatic Feature Based Image Registration using DWT and SIFT, Int. J. Eng. Sci. Comput., Vol. 10, No. 2321–3361, 2014, pp. 454–457.
[17] Haralick, R. M., and Shapiro, L. G., Computer and Robot Vision, Vol. 1. Addison-wesley Reading, 1992.
[18] Ding, S., Zhang, J., Xu , X., and Zhang, Y., A Wavelet Extreme Learning Machine, Neural Comput. Appl., Vol. 27, No. 4, 2016, pp. 1033–1040.
