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3D surface measurement of machine parts is challenging with the increasing demands for micron level measurement accuracy and speed. Optical Metrology based techniques using stereovision face unique challenges in feature extraction due to the complexity of the machine parts and surface finish. For complicated parts, structured laser light is projected on the surface to generate unique or reference features for stereo reconstruction. The induced laser light on the surface is scattered due varies surface phenomena (light scattering, multiple reflections). These scattered and diffused laser lines induce new features on surface, which misguides the surface reconstruction. While targeting micron level accuracy, subpixel feature extraction is also effected by the speckle noise, biasing due to sampling, shape etc. In this paper, we propose new method of improving the accuracy of 3D surface reconstruction on metallic shining surfaces. The proposed template based guidance approach with tangent based feature extraction improves the accuracy of detection in the effected regions by 30%.

We present a novel approach to moving object detection in video taken from a translating, rotating and zooming sensor, with a focus on detecting very small objects in as few frames as possible. The primary innovation is to incorporate automatically computed scene understanding of the video directly into the motion segmentation process. Scene understanding provides spatial and semantic context that is used to improve frame-to-frame homography computation, as well as direct reduction of false alarms. The method can be applied to virtually any motion segmentation algorithm, and we explore its utility for three: frame differencing, tensor voting, and generalized PCA. The approach is especially effective on sequences with large scene depth and much parallax, as often occurs when the sensor is close to the scene. In one difficult sequence, our results show an 8-fold reduction of false positives on average, with essentially no impact on the true positive rate. We also show how scene understanding can be used to increase the accuracy of frame-to-frame homography estimates.