آمار

تعداد نشریات11
تعداد شماره‌ها229
تعداد مقالات2,338
تعداد مشاهده مقاله3,728,188
تعداد دریافت فایل اصل مقاله2,730,117
Mohammadi Anbaran, A., Torkzadeh, P., Ebrahimpour, R., Bagheri, N.. (1399). Fast and Efficient Hardware Implementation of 2D Gabor Filter for a Biologically-Inspired Visual Processing Algorithm. فناوری آموزش, 9(1), 93-102. doi: 10.22061/jecei.2020.7548.404
A. Mohammadi Anbaran; P. Torkzadeh; R. Ebrahimpour; N. Bagheri. "Fast and Efficient Hardware Implementation of 2D Gabor Filter for a Biologically-Inspired Visual Processing Algorithm". فناوری آموزش, 9, 1, 1399, 93-102. doi: 10.22061/jecei.2020.7548.404
Mohammadi Anbaran, A., Torkzadeh, P., Ebrahimpour, R., Bagheri, N.. (1399). 'Fast and Efficient Hardware Implementation of 2D Gabor Filter for a Biologically-Inspired Visual Processing Algorithm', فناوری آموزش, 9(1), pp. 93-102. doi: 10.22061/jecei.2020.7548.404
Mohammadi Anbaran, A., Torkzadeh, P., Ebrahimpour, R., Bagheri, N.. Fast and Efficient Hardware Implementation of 2D Gabor Filter for a Biologically-Inspired Visual Processing Algorithm. فناوری آموزش, 1399; 9(1): 93-102. doi: 10.22061/jecei.2020.7548.404

Fast and Efficient Hardware Implementation of 2D Gabor Filter for a Biologically-Inspired Visual Processing Algorithm

Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 9، شماره 1، فروردین 2021، صفحه 93-102    اصل مقاله (1.19 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.7548.404
نویسندگان
A. Mohammadi Anbaran1؛ P. Torkzadeh* 1؛ R. Ebrahimpour2؛ N. Bagheri3
1Electrical Engineering Department, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.
2Artificial Intelligence Department, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University; Tehran, Iran. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
3Communication Engineering Department, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
تاریخ دریافت: 04 خرداد 1399،  تاریخ بازنگری: 01 مهر 1399،  تاریخ پذیرش: 28 آبان 1399 
چکیده
Background and Objectives: Programmable logic devices, such as Field Programmable Gate Arrays, are well-suited for implementing biologically-inspired visual processing algorithms and among those algorithms is HMAX model. This model mimics the feedforward path of object recognition in the visual cortex.
Methods: HMAX includes several layers and its most computation intensive stage could be the S1 layer which applies 64 2D Gabor filters with various scales and orientations on the input image. A Gabor filter is the product of a Gaussian window and a sinusoid function. Using the separability property in the Gabor filter in the 0° and 90° directions and assuming the isotropic filter in the 45° and 135° directions, a 2D Gabor filter converts to two more efficient 1D filters.
Results: The current paper presents a novel hardware architecture for the S1 layer of the HMAX model, in which a 1D Gabor filter is utilized twice to create a 2D filter. Using the even or odd symmetry properties in the Gabor filter coefficients reduce the required number of multipliers by about 50%. The normalization value in every input image location is also calculated simultaneously. The implementation of this architecture on the Xilinx Virtex-6 family shows a 2.83ms delay for a 128×128 pixel input image that is a 1.86X-speedup relative to the last best implementation.
Conclusion: In this study, a hardware architecture is proposed to realize the S1 layer of the HMAX model. Using the property of separability and symmetry in filter coefficients saves significant resources, especially in DSP48 blocks.
کلیدواژه‌ها
Gabor Filter؛ FPGA؛ Separable Filter؛ Convolution؛ HMAX Model
مراجع

[1] D. H. Hubel and T. N. Wiesel, "Receptive fields of single neurones in the cat's striate cortex,” The Journal of physiology, 148(3): 574-591, 1959.

[2] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, "Robust object recognition with cortex-like mechanisms,” IEEE Trans. on Pattern Analysis & Machine Intelligence, 29(3): 411-426, 2007.

[3] J. Mutch and D. G. Lowe, "Multiclass object recognition with sparse, localized features,” in Proc. Computer Vision and Pattern Recognition conf. (CVPR'06): 11-18, 2006.

[4] S. Rajan, P. Chenniappan, S. Devaraj, and N. Madian, "Facial expression recognition techniques: a comprehensive survey,” IET Image Processing, 13(7): 1031-1040, 2019.

[5] R. Mehrotra, K. R. Namuduri, and N. Ranganathan, "Gabor filter-based edge detection,” Pattern recognition, 25(12): 1479-1494, 1992.

[6] S. Liu, Y. Liu, X. Zhu, Z. Liu, G. Huo, T. Ding, et al., "Gabor Filtering and Adaptive Optimization Neural Network for Iris Double Recognition,” in proc. Chinese Conf. on Biometric Recognition: 441-449, 2018.

[7] G. Amayeh, A. Tavakkoli, and G. Bebis, "Accurate and efficient computation of gabor features in real-time applications,” in proc. Int. Symposium on Visual Computing: 243-252, 2009.

[8] Y. Liang, L. Lu, Q. Xiao, and S. Yan, "Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs,” IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, 39(4): 857-870, 2020.

[9] A. Sironi, B. Tekin, R. Rigamonti, V. Lepetit, and P. Fua, "Learning separable filters,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 37(1): 94-106, 2014.

[10] J. Khosravi, M. Shams Esfandabadi, and R. Ebrahimpour, "Image Registration Based on Sum of Square Difference Cost Function," Journal of Electrical and Computer Engineering Innovations, 6(2): 263-271, 2018.

[11] V. Areekul, U. Watchareeruetai, and S. Tantaratana, "Fast separable gabor filter for fingerprint enhancement," in proc. Int. Conf. on Biometric Authentication: 403-409, 2004.

[12] W.-M. Pang, K.-S. Choi, and J. Qin, "Fast Gabor texture feature extraction with separable filters using GPU," Journal of Real-Time Image Processing, 1(12): 5-13, 2016.

[13] G. Orchard, J. G. Martin, R. J. Vogelstein, and R. Etienne-Cummings, "Fast neuromimetic object recognition using FPGA outperforms GPU implementations," IEEE trans. on neural networks and learning systems, 24(8): 1239-1252, 2013.

[14] A. Al Maashri, M. Cotter, N. Chandramoorthy, M. DeBole, C.-L. Yu, V. Narayanan, et al., "Hardware Acceleration for Neuromorphic Vision Algorithms," Journal of Signal Processing Systems, 70(2): 163-175, 2013.

[15] S. Moini, B. Alizadeh, M. Emad, and R. Ebrahimpour, "A resource-limited hardware accelerator for convolutional neural networks in embedded vision applications," IEEE Trans. on Circuits and Systems II: Express Briefs, 64(10): 1217-1221, 2017.

[16] F. Abdi, P. Amiri, and M. H. Refan, "Low Computational Complexity and High Computational Speed in Leading DCD ERLS Algorithm," Journal of Electrical and Computer Engineering Innovations, 7(1): 19-26, 2019.

[17] S. Chikkerur and T. Poggio, "Approximations in the hmax model," Computer Science and Artificial Intelligence Laboratory, Tech. Rep. MIT-CSAIL-TR-2011-021, 2011.

[18] L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories," Computer vision and Image understanding, 106(1): 59-70, 2007.

[19] M. DeBole, Y. Xiao, C.-L. Yu, A. Al Maashri, M. Cotter, C. Chakrabarti, et al., "FPGA-accelerator system for computing biologically inspired feature extraction models," in proc. 45th Asilomar Conference on Signals, Systems and Computers (ASILOMAR): 751-755, 2011.

[20] G. D. Licciardo, C. Cappetta, and L. Di Benedetto, "Design of a Gabor Filter HW Accelerator for Applications in Medical Imaging," IEEE Trans. on Components, Packaging and Manufacturing Technology, 8(7): 1187-1194, 2018.

آمار
تعداد مشاهده مقاله: 751
تعداد دریافت فایل اصل مقاله: 818


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب