LOS PUNTOS ME LLEVAN A LEER

In a further experiment to prove the effectiveness of the method; irregular shapes were measured from 2-D images, which gives an excellent opportunity to develop and use in a different field. Figure 4.19 shows the measurement for half of a heart as an irregular shape. The corner of an A4 paper reference is used to calibrate the camera from known dimensions that enables the CMT to calculate the boundaries of the half of the heart.

Figure 4.19: Measurement of an irregular shape. A B

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The results inferred 280 mm for the half of the heart from the real measurement of 275 mm.Figure 4.20 demonstrates the measurement of dimensions of two irregular shapes. The objects were created using the core painting programme in Windows 7. The results from applying CMT on the two images A and B from Figure 4.20 supported the accuracy and robustness of the proposed system because the two images have many curves and angles. Hence, the measurement of image B shows 358 mm from the real measurement 335 mm, that shows 6.6% of error and 1% more in image A.

Figure 4.20: Measurement of two different irregular shapes.

4.6 DISCUSSION

The experiments used 2-D images captured from three different cameras. This variation does not affect the results of the measurements, because the perspective projection uses the object plane of calibration to give the value of focal length used to measure the depth of the object. The results of experiments for regular geometrical shape show slight differences in accuracy which vary between the geometrical shapes. The rectangle and the square give almost the same distance from the reality and it has been used for the proposed method. The error in measurement was highest when the camera was farthest from the object plane at 2 metres. The error was 10mm between the real object dimensions and the measurement using the proposed technique from a low-resolution 2MP camera . This error reduced as the experiment changed the camera resolution within different mobile cameras, if 3 MP camera was used the error was 7mm, and 5mm was achieved when the camera resolution was 8 MP.

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On the another hand, the results of irregular shapes measurement shows the error in distances between the real dimensions in 2-D and the distances that measured by CMT. The percentange of error in measurement was 1.8%, when the calibrated shape has one or two direction of the curve as in Figure 4.12. Whereas, the other shapes of measurement had 6.6% and 7.6% of error when the curves had random boundaries.

Finally to summarise the results of irregular shapes from the experiments, the Table 4.2 shows the initial measurement of the 2-D image shapes that were chosen manually.

Table 4.2: Summary of measurement error

4.7 SUMMARY

In this chapter, a new flexible automatic technique has been developed to calibrate a camera to be used for easily inferring the dimensions of the objects. The technique only requires the camera to observe a planar measurement from different geometric shapes. A rectangle, square and triangle are used as flat objects to determine the camera parameters that enable dimensions of the real objects to be inferred. The system of calibration was tested for three angles of a rotation before testing it in the experiments. Tilt, pan and swing rotation gave different angles of object rotation and the test showed the accuracy of inferred measurements with only small levels of error that did not affect the main purpose of measurement. However, the weakest triangle shape in calibration and measurement were selected based on the practical test of the shape that shows limitation of use in measurement. Moreover, the camera view angles were tested and selected the range of possible view. For example, angles of 0˚ and 180˚ in camera pan give no views of the object, which prevented the calibration and then measurement of dimensions. As it planned from the research to use the calibration system in face recognition cross pose, the previous experiment focused on the tilt and pan angles, and the swing that have not shown significant problems or large difference in distances.

Figures Real dimensions Inferred dimension Error in mm Error Percentage MSE Figure 4.19 280 275 5 1.8% Figure 4.20 (A) 358 335 23 6.6% Figure 4.20 (B) 615 690 75 7.6%

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The calibration depends on the constant pixels of the image, which is influenced by changing the resolution of the picture. Many new algorithms and techniques use a high- resolution stereo camera to infer the depth of 3-D dimensions from 2-D images.Thus, the use of an algorithm faces many challenges related to image resolution, changes in distance and shape, and the cost of the technique. The experimental results show that the inferred dimensions were highly accurate. The technique allows the use of varying resolutions and the results show that the algorithm works with low-grade image quality such as 2MP resolution and with good quality, such as 8MP, which provides excellent accuracy in inferring real dimensions. The 2MP, 3MP and 8MP cameras used are available at low cost for any user. Furthermore, three different geometrical shapes were used to measure regular and irregular shapes to give a wide opportunity to accommodate the largest possible number of formats that are measured around us. Futhermore, the results tested the variant distance of the measurement up to 3 meters to allow the technique to work widely in-door for different purposes of measurement. This technique can be applied to any image captured from any camera.

The triangle shape was not used in these experiments as it showed the maximum error of the measurement in test part compared with the other geometrical shapes of calibration. Also, the project will need the rectangle and square shapes that are used in the window for face detection to face recognition. In the next Chapter 5, the research will present the new system for face recognition based on the inferring information from intrinsic parameters of camera calibration that was achieved in this chapter.