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FPGA Design of Enhanced Scale-Invariant Feature Transform with Finite-Area Parallel Feature Matching for Stereo Vision
| Content Provider | MDPI |
|---|---|
| Author | Kuo, Chien-Hung Huang, Erh-Hsu Chien, Chiang-Heng Hsu, Chen-Chien |
| Copyright Year | 2021 |
| Description | In this paper, we propose an FPGA-based enhanced-SIFT with feature matching for stereo vision. Gaussian blur and difference of Gaussian pyramids are realized in parallel to accelerate the processing time required for multiple convolutions. As for the feature descriptor, a simple triangular identification approach with a look-up table is proposed to efficiently determine the direction and gradient of the feature points. Thus, the dimension of the feature descriptor in this paper is reduced by half compared to conventional approaches. As far as feature detection is concerned, the condition for high-contrast detection is simplified by moderately changing a threshold value, which also benefits the reduction of the resulting hardware in realization. The proposed enhanced-SIFT not only accelerates the operational speed but also reduces the hardware cost. The experiment results show that the proposed enhanced-SIFT reaches a frame rate of 205 fps for 640 × 480 images. Integrated with two enhanced-SIFT, a finite-area parallel checking is also proposed without the aid of external memory to improve the efficiency of feature matching. The resulting frame rate by the proposed stereo vision matching can be as high as 181 fps with good matching accuracy as demonstrated in the experimental results. |
| Starting Page | 1632 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10141632 |
| Journal | Electronics |
| Issue Number | 14 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-08 |
| Access Restriction | Open |
| Subject Keyword | Electronics Artificial Intelligence Industrial Engineering Sift Fpga Feature Detection Feature Descriptor Feature Matching Stereo Vision Epipolar |
| Content Type | Text |
| Resource Type | Article |
