An appropriate response to an emergency situation involving hazardous materials requires a good and accurate knowledge of the characteristics of the materials in question. Inspection and characterization of materials collected from hazardous environments is also of great value to first responders and security personnel. In this paper, we describe 3D modeling of material surfaces from stereo images obtained from a Large Chamber Scanning Electron Microscope (LC-SEM), a one of a kind microscope in the US. By applying an annealing based two-step energy minimization technique, the stereo images are reconstructed into 3D. A virtual 3D view obtained from the stereo pair verifies the validity of the automatic 3D model constructed. The spectral information at two energy levels, one for AlK and the other for NbL, is extracted using Energy Dispersive X-ray Spectroscopy (EDS) and overlaid on the 3D surface. Thus, in addition to performing 3D metrology on the surface, one is able to visually inspect the distribution characteristics of constituent materials and correlate them with surface structure such as creases or dents caused by fracture or other impacts in hazardous environments.

Extract Extended abstract of a paper presented at Microscopy and Microanalysis 2007 in Ft. Lauderdale, Florida, USA, August 5 – August 9, 2007

In its quest for more reliability and higher recognition rates the face recognition community has been focusing more and more on 3D based recognition. Depth information adds another dimension to facial features and provides ways to minimize the effects of pose and illumination variations for achieving greater recognition accuracy. This chapter reviews, therefore, the major techniques for 3D face modeling, the first step in any 3D assisted face recognition system. The reviewed techniques are laser range scans, 3D from structured light projection, stereo vision, morphing, shape from motion, shape from space carving, and shape from shading. Concepts, accuracy, feasibility, and limitations of these techniques and their effectiveness for 3D face recognition are discussed.

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

Micro-electro-mechanical systems (MEMS) are found in area applications such as the automotive industry, the aviation industry, the semiconductor industry, the medical field, and various other fields where miniaturization is taking over. The accurate measurement of features on the surface of MEMS is an important tool for the assessment and monitoring of product quality. Presented here are the algorithms and results of 3D model reconstructions of MEMS devices using a variety of microscopic sensors. These sensors include an atomic force microscope, a scanning electron microscope, and a laser scanning confocal microscope. MEMS devices with micron-size features were first scanned with these microscopes. 3D models were then built and visualized using methods specific to each microscope. This allows for the models use in applications such as inspection, study of wear and tear, behavior, and reaction of such systems to pressure, heat, and friction.

An energy minimizing snake algorithm that runs over a grid is designed and used to reconstruct high resolution 3D human faces from pairs of stereo images. The accuracy of reconstructed 3D data from stereo depends highly on how well stereo correspondences are established during the feature matching step. Establishing stereo correspondences on human faces is often ill posed and hard to achieve because of uniform texture, slow changes in depth, occlusion, and lack of gradient. We designed an energy minimizing algorithm that accurately finds correspondences on face images despite the aforementioned characteristics. The algorithm helps establish stereo correspondences unambiguously by applying a coarse-to-fine energy minimizing snake in grid format and yields a high resolution reconstruction at nearly every point of the image. Initially, the grid is stabilized using matches at a few selected high confidence edge points. The grid then gradually and consistently spreads over the low gradient regions of the image to reveal the accurate depths of object points. The grid applies its internal energy to approximate mismatches in occluded and noisy regions and to maintain smoothness of the reconstructed surfaces. The grid works in such a way that with every increment in reconstruction resolution, less time is required to establish correspondences. The snake used the curvature of the grid and gradient of image regions to automatically select its energy parameters and approximate the unmatched points using matched points from previous iterations, which also accelerates the overall matching process. The algorithm has been applied for the reconstruction of 3D human faces, and experimental results demonstrate the effectiveness and accuracy of the reconstruction.

This paper summarizes the various components of face recognition research conducted at the IRIS Lab. First, fusion of visual and thermal infrared (IR) images for robust face recognition is discussed. Two techniques are implemented: data fusion and decision fusion. With the knowledge that eyeglasses block the emission of thermal energy, an algorithm is designed to detect and replace eyeglasses with an eye template in thermal images. A commercial face recognition software (FaceIt/spl reg/) is used in the evaluation of the various fusion algorithms. Comparison results show that fusion-based face recognition outperforms individual visual or thermal face recognizers under illumination variations and facial expressions. Efforts in the 3D arena are also described. Results of high resolution stereo-based 3D reconstruction of faces are shown and analyzed, in a first approach, then in a second approach, a warping technique is applied to overlay color and thermal textures on 3D mannequin head models, obtained using a laser range scanner.