3D foot digital models have great potential for application in ergonomics design and online virtual shoes try-on. Traditional techniques usually take 10 seconds to several minutes to acquire dense data, which is a critical limitation to large scale data collection. To enhance data collection efficiency, a novel approach which combines simulated laser speckle projection stereo with 3D silhouette is proposed. Laser speckle has more statistical advantages so that it can deal with lacking of texture of human skin and make stereo matching easier. With dark field lighting that strengthen foot contour, 3D silhouette can remove border noise caused by our active stereo reconstruction. Besides, all light sources in our work are infrared to avoid ambient light inference. In our design, five active stereo rigs are installed around 4π solid angle centered at the foot position to capture whole foot’s surface mesh data. Composed of a pair of stereo IP-cameras with visible cut filters, an infrared simulated laser speckle mini film projector and a cluster of infrared LEDs surrounding camera lens, each active stereo rig takes charge of obtaining 3D information of corresponding foot part. The five rigs are controlled by an MCU controller to successively capture one pair of speckle pattern images for stereo reconstruction and one pair of edge enhanced dark field lighting images for silhouette. The system design resolution is less than 0.3 mm per pixel. Data capture could be performed in less than 1 second for each foot and more than 500 thousand valid points are acquired as dense point cloud model. Finally, foot mesh model is generated using Poisson reconstruction algorithm.
Semiconductor wafer is elementary unit in semiconductor industry. In the fabrication of semiconductor wafer, surface defects such as dirties, scratches, burrs, chippings and holes may be generated which severely affect the quality of downstream production. Typical inspection of these defects mainly depends on human experts inspecting system which is time-consuming and low efficiency. With the fast development of digital imaging and processing technique, Computer vision automatic inspection method has shown vast potential for product quality test. Due to low contrast and weak context characteristics of wafer surface defects, the existing methods have difficulty to extract whole defect patterns. A novel algorithm for defect contour extraction is proposed based on multi-frame differential image summation. For each side of semiconductor wafer surfaces, multiple images are captured by high resolution digital camera. For each image the gradient is calculated using common used differential mask, and then the gradient is thinned based on edge extraction using Canny operator and smoothed using Gaussian smooth filter. All refined gradient images are added up to enhance defect features and smooth defect-free regions furtherly. Finally, the Canny operator is applied again to extract whole defect’s contour from gradient summation image. Experiments using real semiconductor wafers illustrate that the proposed algorithm can detect most of defects correctly and effectively.