Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method
Abstract
:1. Introduction
2. Background
2.1. Color/Texture-Based Object Detection Methods
2.2. Shape-Based Object Detection Methods
2.3. Grayscale Statistical Feature-Based Object Detection Methods
3. Methodology
3.1. Image Preprocessing
3.2. Columns and Walls Recognition using GLH Statistical Parameters
- (1)
- the intersection angle of a sub-region’s two adjacent boundary lines is less than 5;
- (2)
- variance and skewness values of the sub-region’s GLH are local minimum while the kurtosis value is local maximum among adjacent sub-regions;
- (3)
- the ratio of length to width of a sub-region is set as no less than two for a column, and less than two for a wall. The length of a sub-region is defined by the length of its longer boundary line, and the width is defined as the distance between its two boundary lines [51].
4. Implementation and Results
4.1. Validation
4.2. Implementation and Discussion
4.2.1. Column Detection Results and Discussion
4.2.2. Wall detection results and discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Number of the Image | TP | FP | FN | Precision | Recall |
---|---|---|---|---|---|
TP/(TP+FP) | TP/(TP+FN) | ||||
1 | 5 | 0 | 0 | 100.0% | 100.0% |
2 | 2 | 0 | 2 | 100.0% | 50.0% |
3 | 2 | 0 | 0 | 100.0% | 100.0% |
4 | 5 | 0 | 1 | 100.0% | 83.3% |
5 | 5 | 0 | 2 | 100.0% | 71.4% |
6 | 1 | 0 | 1 | 100.0% | 50.0% |
7 | 4 | 0 | 1 | 100.0% | 80.0% |
8 | 4 | 0 | 2 | 100.0% | 66.7% |
9 | 4 | 0 | 1 | 100.0% | 80.0% |
10 | 6 | 0 | 0 | 100.0% | 100.0% |
11 | 4 | 0 | 0 | 100.0% | 100.0% |
12 | 6 | 0 | 0 | 100.0% | 100.0% |
13 | 6 | 0 | 3 | 100.0% | 66.7% |
14 | 4 | 0 | 4 | 100.0% | 50.0% |
15 | 3 | 0 | 1 | 100.0% | 75.0% |
16 | 3 | 0 | 1 | 100.0% | 75.0% |
17 | 2 | 0 | 0 | 100.0% | 100.0% |
18 | 2 | 0 | 2 | 100.0% | 50.0% |
19 | 6 | 0 | 0 | 100.0% | 100.0% |
20 | 2 | 0 | 0 | 100.0% | 100.0% |
Total | 76 | 0 | 21 | 100.0% | 78.4% |
Number of the Image | TP | FP | FN | Precision | Recall |
---|---|---|---|---|---|
TP/(TP+FP) | TP/(TP+FN) | ||||
1 | 1 | 0 | 0 | 100.0% | 100.0% |
2 | 2 | 0 | 1 | 100.0% | 66.7% |
3 | 1 | 0 | 1 | 100.0% | 50.0% |
4 | 2 | 0 | 0 | 100.0% | 100.0% |
5 | 1 | 0 | 1 | 100.0% | 50.0% |
6 | 1 | 0 | 0 | 100.0% | 100.0% |
7 | 2 | 0 | 1 | 100.0% | 66.7% |
8 | 1 | 0 | 0 | 100.0% | 100.0% |
9 | 1 | 0 | 1 | 100.0% | 50.0% |
10 | 1 | 0 | 0 | 100.0% | 100.0% |
11 | 1 | 0 | 1 | 100.0% | 50.0% |
12 | 1 | 0 | 0 | 100.0% | 100.0% |
13 | 2 | 0 | 1 | 100.0% | 66.6% |
14 | 1 | 0 | 1 | 100.0% | 50.0% |
15 | 3 | 0 | 1 | 100.0% | 75.0% |
16 | 2 | 0 | 0 | 100.0% | 100.0% |
17 | 1 | 0 | 0 | 100.0% | 100.0% |
18 | 1 | 0 | 1 | 100.0% | 50.0% |
19 | 3 | 0 | 0 | 100.0% | 100.0% |
20 | 2 | 0 | 1 | 100.0% | 66.7% |
Total | 30 | 0 | 11 | 100.0% | 73.2% |
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Zhang, Z.; Wei, H.-H.; Yum, S.G.; Chen, J.-H. Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method. Appl. Sci. 2019, 9, 3915. https://doi.org/10.3390/app9183915
Zhang Z, Wei H-H, Yum SG, Chen J-H. Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method. Applied Sciences. 2019; 9(18):3915. https://doi.org/10.3390/app9183915
Chicago/Turabian StyleZhang, Zhenyu, Hsi-Hsien Wei, Sang Guk Yum, and Jieh-Haur Chen. 2019. "Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method" Applied Sciences 9, no. 18: 3915. https://doi.org/10.3390/app9183915
APA StyleZhang, Z., Wei, H.-H., Yum, S. G., & Chen, J.-H. (2019). Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method. Applied Sciences, 9(18), 3915. https://doi.org/10.3390/app9183915