Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo
Abstract
:1. Introduction
2. Materials and Methods
2.1. System Description
2.2. The Dataset
2.3. The Proposed Algorithm
2.3.1. Image Preprocessing
2.3.2. Training a Cascade Classifier for ISME
2.3.3. Edge Detection of the Target in the Microscopic Image
2.3.4. The Contour Extraction and the Localization of ISME Tip
Algorithm 1: ISME Tip Localization |
Input: edge_image Output: Tip Localization image is represented by tipLoc_image |
BEGIN |
1.Find Contours () |
2.For i = 0: contours.size//contours.size is the number of the contours |
3.Get the length of all of the contours 4.END 5.Get the length of every contour 6.Con = the Longest contour 7.draw the Con on tipLoc_image |
8.For j = 0: Con.size//Con.size is the size of Con 9.find the point named TIP with the minimum x// 10.draw a circle around the TIP |
END |
2.3.5. Edge Detection of Plant Root Using Hough Transformation
Algorithm 2: Boundary Detection of Root |
Input: edge_image Output: Tip Localization image is represented by tipLoc_image |
BEGIN |
1.plines = Hough Line detection of edge_image |
2.For i = 0: plines.size//plines.size is the number of the lines 3.Point_A = A end point of plines segment Point_B = Another end point of plines segment |
4.IF |Point_A.x–Point_B.x| < 1/3 edge_image.width & |Point_A.y – Point_B.y| > 3/4 edge_image.height 5.THEN retain the plines(i) Draw the plines(i) 6.ENDIF 7.END END |
2.3.6. Distance Calculation
2.4. Experiments
3. Results
3.1. Image Preprocessing
3.2. ISME Cascade Classifier Training and ISME Detection
3.3. Edge Detection of Plant Tissues and Microelectrodes
- (1)
- The signal-to-noise ratio is higher.
- (2)
- The location of the edge points must be accurate; in other words, the detected edge points should be as close as possible to the center of the actual edge.
- (3)
- The detection must only have one response to a single edge, that is a single edge has only one unique response, and suppresses the response to false edge.
3.4. Contour Extraction and ISME Tip Localization
3.5. Edge Detection of Plant Root Using Hough Transformation
3.6. Computation of Relative Distance
4. Discussion
4.1. ISME Recognition
4.2. Location of the ISME Tip
4.3. Straight-Line Screening
4.4. Evaluation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Feature of Methods | Positive Samples Number | Negative Samples Number | Positive Samples Resolution (pixel) | Training Time (hours) | Average Test Time (s/frame) | Detection Rate |
---|---|---|---|---|---|---|
Haar-like | 1118 | 1000 | 24 × 24 | 4.5 | 0.23 | 10.97% |
LBP | 1118 | 1000 | 24 × 24 | 1 | 0.30 | 4.31% |
Haar-like | 6600 | 3000 | 24 × 24 | 34 | 0.13 | 57.34% |
LBP | 6600 | 3000 | 24 × 24 | 4.78 | 0.21 | 47.22% |
Haar-like | 6600 | 3000 | 60 × 20 | weeks | 0.07 | 90.75% |
LBP | 6600 | 3000 | 60 × 20 | 26 | 0.13 | 92.73% |
LBP | 1118 | 1000 | 48 × 48 | 2.5 | 0.31 | 5.55% |
LBP | 6600 | 3000 | 48 × 48 | 28.5 | 1.14 | 50.82% |
LBP | 6600 | 3000 | 90 × 30 | 59.5 | 0.16 | 95.68% |
LBP+Haar-like | 6600 | 3000 | 90 × 30 (LBP)/60 × 20 (Haar-like) | -- | 0.23 | 99.14% |
Templet-matching | -- | -- | -- | -- | 0.33–0.44 | 17.50–39.95% |
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Yan, S.-X.; Zhao, P.-F.; Gao, X.-Y.; Zhou, Q.; Li, J.-H.; Yao, J.-P.; Chai, Z.-Q.; Yue, Y.; Wang, Z.-Y.; Huang, L. Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo. Algorithms 2019, 12, 238. https://doi.org/10.3390/a12110238
Yan S-X, Zhao P-F, Gao X-Y, Zhou Q, Li J-H, Yao J-P, Chai Z-Q, Yue Y, Wang Z-Y, Huang L. Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo. Algorithms. 2019; 12(11):238. https://doi.org/10.3390/a12110238
Chicago/Turabian StyleYan, Shi-Xian, Peng-Fei Zhao, Xin-Yu Gao, Qiao Zhou, Jin-Hai Li, Jie-Peng Yao, Zhi-Qiang Chai, Yang Yue, Zhong-Yi Wang, and Lan Huang. 2019. "Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo" Algorithms 12, no. 11: 238. https://doi.org/10.3390/a12110238
APA StyleYan, S. -X., Zhao, P. -F., Gao, X. -Y., Zhou, Q., Li, J. -H., Yao, J. -P., Chai, Z. -Q., Yue, Y., Wang, Z. -Y., & Huang, L. (2019). Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo. Algorithms, 12(11), 238. https://doi.org/10.3390/a12110238