Microscope vision system based on micro laser line scanning for characterizing microscale topography.

A microscope vision system to characterize a microscale surface via micro laser line projection is presented. The characterization is performed by means of surface descriptors, which include the root mean square, kurtosis, skewness, homogeneity, entropy, contrast, and correlation. These descriptors are computed from surface irregularities, which are retrieved by means of the micro laser line projection. The characterization is carried out by an optical microscope system on which a CCD camera and a 36 µm laser line are attached. Thus, the microscope vision system projects the micro laser line on the surface, and the CCD camera captures the line reflection, which provides the surface contour. The contour dimension is computed via Bezier networks by means of the micro laser line coordinates. Thus, the surface descriptors are computed by means of the surface contour to accomplish the characterization. The proposed characterization improves the accuracy of the optical microscope imaging systems, which characterize the microscale surface by means of the gray-level intensity. Thus, the capability of the characterization via micro laser line projection is established by means of the descriptors' accuracy. This contribution is corroborated by characterizing metal and paper surfaces.

Mobile calibration based on laser metrology and approximation networks.

A mobile calibration technique for three-dimensional vision is presented. In this method, vision parameters are computed automatically by approximation networks built based on the position of a camera and image processing of a laser line. The networks also perform three-dimensional visualization. In the proposed system, the setup geometry can be modified online, whereby an online re-calibration is performed based on data provided by the network and the required modifications of extrinsic and intrinsic parameters are thus determined, overcoming any calibration limitations caused by the modification procedure. The mobile calibration also avoids procedures involving references, which are used in traditional online re-calibration methods. The proposed mobile calibration thus improves the accuracy and performance of the three-dimensional vision because online data of calibrated references are not passed on to the vision system. This work represents a contribution to the field of online re-calibration, as verified by a comparison with the results based on lighting methods, which are calibrated and re-calibrated via perspective projection. Processing time is also studied.

Shape connection by pattern recognition and laser metrology.

Shape connection based on the pattern recognition of three-dimensional shapes is presented. In this technique, the object shape is reconstructed by laser scanning and image processing. The object is reconstructed from multiple views when an object occlusion appears. From this process, multiple parts of the object are reconstructed. Then, these parts are assembled to obtain the complete object shape. To perform the assembling, a matching procedure is applied to a transverse section of the multiple views by Hu moments. The depth of the transverse section is computed by an approximation network based on the behavior of the laser line and the camera position. Also, vision parameters are deduced by the network and image processing. In this manner, the shape connection is achieved automatically by computational algorithms. Therefore, errors of physical measurement are not passed to the reconstruction system. Thus, the performance and the accuracy of the reconstruction system are improved. This is elucidated by the comparison between the obtained results by the proposed technique and the obtained results by a contact method. Thus, a contribution in laser metrology for shape connection is achieved.