Jason Clemons
ABSTRACT
The deployment of computer vision algorithms in mobile applications is growing at a rapid pace. A primary component of the computer vision software pipeline is feature extraction, which identifies and encodes relevant image features. We present an embedded heterogeneous multicore design named EFFEX that incorporates novel functional units and memory architecture support, making it capable of increasing mobile vision performance while balancing power and area. We demonstrate this architecture running three common feature extraction algorithms, and show that it is capable of providing significant speedups at low cost. Our simulations show a speedup of as much as 14x for feature extraction with a decrease in energy of 40x for memory accesses.
- ARM. Cortex-A5 Processor, 2010. http://www.arm.com/products/processors/cortex-a/.Google Scholar
- ARM. Cortex-A8 Processor, 2010. http://www.arm.com/products/processors/cortex-a/.Google Scholar
- D. G. R. Bradski and A. Kaehler. Learning OpenCV. O'Reilly Media, Inc., 2008.Google Scholar
- D. Brooks, V. Tiwari, and M. Martonosi. Wattch: a framework for architectural-level power analysis and optimizations. In ISCA, 2000. Google ScholarDigital Library
- L. Chang and J. Hernández-Palancar. A Hardware Architecture for SIFT Candidate Keypoints Detection. In CIARP, 2009. Google ScholarDigital Library
- N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. Google ScholarDigital Library
- Google. Google Goggles For Android, September 2010. http://www.google.com/mobile/goggles/#text.Google Scholar
- S. Goyal. Object Detection using OpenCV II - Calculation of HoG Features, October 2009. http://smsoftdev-solutions.blogspot.com/2009/10/object-detection-using-opencv-ii.html.Google Scholar
- M. Gschwind, H. Hofstee, B. Flachs, M. Hopkin, Y. Watanabe, and T. Yamazaki. Synergistic Processing in Cell's Multicore Architecture. MICRO, 26(2), 2006. Google ScholarDigital Library
- D. E. Guller, J. P. Singh, and A. Gputa. Parallel Computer Architecture: A Hardware/Software Approach. Morgan Kaufmann, 1998. Google ScholarDigital Library
- R. Hess. SIFT feature detector for OpenCV, February 2009. http://web.engr.oregonstate.edu/~hess.Google Scholar
- S. Jain, V. Erraguntla, S. Vangal, Y. Hoskote, N. Borkar, T. Mandepudi, and V. Karthik. A 90mW/GFlop 3.4GHz Reconfigurable Fused/Continuous Multiply-Accumulator for Floating-Point and Integer Operands in 65nm. In VLSID, Jan. 2010. Google ScholarDigital Library
- R. Kadota, H. Sugano, M. Hiromoto, H. Ochi, R. Miyamoto, and Y. Nakamura. Hardware Architecture for HOG Feature Extraction. In IIH-MSP, sep. 2009. Google ScholarDigital Library
- Layar. Layar Reality Browser, September 2010. http://www.layar.com/.Google Scholar
- D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 2004. Google ScholarDigital Library
- I. T. Ltd. SGX Graphics IP Core Family, 2010.Google Scholar
- J. E. Miller, H. Kasture, G. Kurian, C. Gruenwald III, N. Beckmann, C. Celio, J. Eastep, and A. Agarwal. Graphite: A Distributed Parallel Simulator for Multicores. In HPCA, January 2010.Google ScholarCross Ref
- A. Prengler and K. Adi. A Reconfigurable SIMD-MIMD Processor Architecture for Embedded Vision Processing Applications. SAE World Congress, 2009.Google ScholarCross Ref
- J. Qiu, Y. Lu, T. Huang, and T. Ikenaga. An FPGA-Based Real-Time Hardware Accelerator for Orientation Calculation Part in SIFT. IIH-MSP, 2009. Google ScholarDigital Library
- E. Rosten and T. Drummond. Fusing points and lines for high performance tracking. In ICCV, volume 2, October 2005. Google ScholarDigital Library
- E. Rosten and T. Drummond. Machine learning for high-speed corner detection. In ECCV, May 2006. Google ScholarDigital Library
- B. Silpa, A. Patney, T. Krishna, P. Panda, and G. Visweswaran. Texture filter memory; a power-efficient and scalable texture memory architecture for mobile graphics processors. In ICCAD, 2008. Google ScholarDigital Library
- J. Skribanowitz, T. Knobloch, J. Schreiter, and A. Konig. VLSI implementation of an application-specific vision chip for overtake monitoring, real time eye tracking, and automated visual inspection. In MicroNeuro '99, 1999. Google ScholarDigital Library
- P. Viola and M. Jones. Robust real-time object detection. IJCV, # 2002.Google Scholar
- J. Wortham. Customers Angered as iPhones Overload ATT. New York Times, September 2009.Google Scholar
- C. Wu. SIFTGPU, September 2010. http://www.cs.unc.edu/~ccwu/siftgpu/.Google Scholar
Index Terms
- EFFEX: an embedded processor for computer vision based feature extraction
Recommendations
Fuzzy rough dimensionality reduction: A feature set partition-based approach
AbstractDimensionality reduction is considered in many learning methods using discriminative features to obtain optimal performance. In general, feature extraction and feature selection are two independent methods that cherry-pick the informative ...
Highlights- The ϑ-fuzzy similarity relation is proposed.
- The feature set is divided into the nonsignificant feature set, weak significant feature set, and significant feature set.
- A feature extraction method called FSLLE is proposed, and the ...
Kernel-based feature extraction under maximum margin criterion
In this paper, we study the problem of feature extraction for pattern classification applications. RELIEF is considered as one of the best-performed algorithms for assessing the quality of features for pattern classification. Its extension, local ...
Dynamic image fine feature extraction method based on machine vision
ICASIT 2020: Proceedings of the 2020 International Conference on Aviation Safety and Information TechnologyIn the dynamic image feature extraction of machine vision, a single key frame is often used to extract, which affects the accuracy of parameters when obtaining features. Therefore, this paper proposes a method of dynamic image subtle feature extraction ...
Comments