Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects

We introduce a method based on the deflectometry principle for the reconstruction of specular objects exhibiting significant size and geometric complexity. A key feature of our approach is the deployment of an Automatic Virtual Environment (CAVE) as pattern generator. To unfold the full power of this extraordinary experimental setup, an optical encoding scheme is developed which accounts for the distinctive topology of the CAVE. Furthermore, we devise an algorithm for detecting the object of interest in raw deflectometric images. The segmented foreground is used for single-view reconstruction, the background for estimation of the camera pose, necessary for calibrating the sensor system. Experiments suggest a significant gain of coverage in single measurements compared to previous methods. To facilitate research on specular surface reconstruction, we will make our data set publicly available

Tracking and Retexturing Cloth for RealTime Virtual Clothing Applications

Abstract. In this paper, we describe a dynamic texture overlay method from monocular images for real-time visualization of garments in a virtual mirror environment. Similar to looking into a mirror when trying on clothes, we create the same impression but for virtually textured garments. The mirror is replaced by a large display that shows the mirrored image of a camera capturing e.g. the upper body part of a person. By estimating the elastic deformations of the cloth from a single camera in the 2D image plane and recovering the illumination of the textured surface of a shirt in real time, an arbitrary virtual texture can be realistically augmented onto the moving garment such that the person seems to wear the virtual clothing. The result is a combination of the real video and the new augmented model yielding a realistic impression of the virtual piece of cloth

AN INVESTIGATION OF IMAGE PROCESSING TECHNIQUES FOR PAINT DEFECT DETECTION USING A MACHINE VISION SYSTEM

Detection and inspection of metal surface corrosion in the ballast tanks of U.S. Navy ships has been a long time problem. The adverse climatic conditions to which the ballast tanks are exposed and the uneven geometry of ballast tanks makes the visual inspection process of surface coatings a difficult job. Thousands of tanks are inspected yearly, with the average cost of an individual tank inspection at approximately $8-15 thousand/each. To aid the visual inspection process, this research is conducted to develop a new technique to automate the visual task of metal surface inspection by image acquisition and post processing. The best results of image processing are achieved by the enhanced contrast between the paint defect and the background using a newly developed optically active additive (OAA) used in paints. Thorough investigation of image processing algorithms has been carried out and a background of imaging theory and experiments is illustrated in this work