Calibration Experiments for a Computer Vision Oyster Volume Estimation System

Figures & data

Figure 1. Digital Images for a 2-D system.

Figure 2: Scatter Plot for Actual Oyster Volume versus Pixel Count from 2-D System.

Figure 3: Output from R for Simple Linear Regression Using Classical Method.

Figure 4: Output from R for Simple Linear Regression Using Inverse Method.

Table 1. Errors in Volume Estimation from Classical and Inverse Methods.

Oyster ID	Error = Actual - Estimated
	Classical_method	Inverse_method
6	2.08	2.47
13	0.72	0.93
15	0.92	1.27
16	−0.13	−0.10
17	−3.69	−2.44
19	0.22	−0.66
22	0.54	−0.03
25	0.57	−0.99
26	−1.18	−0.95
30	−0.80	−0.16
Mean	−0.19	−0.07
Variance	2.41	1.87

Figure 5. Regression through Origin Output from R Using Classical Method.

Figure 6. Scatter Plots for Comparing 2-D and 3-D Systems.

Figure 7. R Output for Simple Linear Regression Using Classical Method for 3-D data.

Figure 8. Error Plots from 2-D and 3-D Systems.

Table 2. Oyster Grading Scale.

Oyster Grade	Actual Volume
Large	(13, ∞) Above 13 cc
Medium	(10, 13] Between 10 to 13 cc
Small	(0, 10] 10 cc or below

Table 3. Hit Ratios for the 2-D and 3-D Oyster Volume Estimation Systems.

Oyster ID	Oyster Actual Volume	2-D System	3-D System
1	13.04	13.45	13.01
2	11.71	11.39	12.15
3	17.42	17.61	16.34
4	7.23	7.71	7.35
5	10.03	11.44	*9.31
7	9.94	9.23	*10.11
8	7.53	6.84	6.80
9	12.73	*14.99	*13.88
10	12.66	11.40	12.71
11	10.53	*8.11	*9.99
12	10.84	11.52	10.27
14	8.48	9.43	8.94
18	15.44	14.42	15.96
20	8.26	9.26	8.75
21	10.95	10.01	11.93
23	7.34	5.99	7.02
24	13.21	13.51	*12.53
27	11.22	*9.97	11.55
28	9.25	*10.71	8.75
29	13.75	14.56	14.21
Hit Ratio	−	80%	75%

* Not in the same class with the observed oyster.

Table

Oyster Grade	Actual Volume
Large	(13, ∞) Above 13 cc
Medium	(10, 13] Between 10 to 13 cc
Small	(0, 10] 10 cc or below

Figure A1. Optical System Design

Figure A2. Triangulation Technique for Obtaining Oyster Thickness (h) Data.