Active stereo vision is a method of 3D surface scanning involving the projecting and capturing of a series of light patterns where depth is derived from correspondences between the observed and projected patterns. In contrast, passive stereo vision reveals depth through correspondences between textured images from two or more cameras. By employing a projector, active stereo vision systems find correspondences between two or more cameras, without ambiguity, independent of object texture. In this paper, we present a hybrid 3D reconstruction framework that supplements projected pattern correspondence matching with texture information. The proposed scheme consists of using projected pattern data to derive initial correspondences across cameras and then using texture data to eliminate ambiguities. Pattern modulation data are then used to estimate error models from which Kullback-Leibler divergence refinement is applied to reduce misregistration errors. Using only a small number of patterns, the presented approach reduces measurement errors versus traditional structured light and phase matching methodologies while being insensitive to gamma distortion, projector flickering, and secondary reflections. Experimental results demonstrate these advantages in terms of enhanced 3D reconstruction performance in the presence of noise, deterministic distortions, and conditions of texture and depth contrast.

Phase shifting structured light illumination for range sensing involves projecting a set of grating patterns where accuracy is determined, in part, by the number of stripes. However, high pattern frequencies introduce ambiguities during phase unwrapping. This paper proposes a process for embedding a period cue into the projected pattern set without reducing the signal-to-noise ratio. As a result, each period of the high frequency signal can be identified. The proposed method can unwrap high frequency phase and achieve high measurement precision without increasing the pattern number. Therefore, the proposed method can significantly benefit real-time applications. The method is verified by theoretical and experimental analysis using prototype system built to achieve 120 fps at 640 × 480 resolution.

To solve the problems associated with conventional 2-D fingerprint scanners such as skin deformation and print smearing, in this paper we introduce a noncontact fingerprint scanner employing structured light illumination to generate high-resolution albedo images as well as 3-D ridge scans. The question to be answered in this research is whether or not ridge depth information improves the quality and matching capability of acquired fingerprints? For evaluation of this question, we use the National Institute of Standards and Technology fingerprint image quality metrics. These metrics require the 3-D prints to be flattened. We present a complete and detailed flattening algorithm based upon unfolding an elastic tube fit to the peaks and valleys of ridges identified within the scan. Further improvement of the flattened print is achieved through the incorporation of ridge information extracted from the albedo image with the depth and albedo ridge information fused together according to local scan quality. Our study compares image quality between the flattened 3-D prints and ink rolled prints. Most significantly, the matching performance of 3-D flattened to 3-D flattened prints is evaluated based on ridge depth only, albedo only, and depth with albedo fusion.

We introduce a new method for determining the number of straight lines, line angles, offsets, widths, and discontinuities in complicated images. In this method, line angles are obtained by searching the peaks of a hybrid discrete Fourier and bilinear transformed line angle spectrum. Numerical advantages and performance are demonstrated.

Energy-minimizing active contour models or snakes can be used in many applications such as edge detection, motion tracking, image matching, computer vision, and three-dimensional (3-D) reconstruction. We present a novel snake that is superior both in accuracy and convergence speed over previous snake algorithms. High performance is achieved by using spline representation and dividing the energy-minimization process into multiple stages. The first stage is designed to optimize the convergence speed in order to allow the snake to quickly approach the minimum-energy state. The second stage is devoted to snake refinement and to local minimization of energy, thereby driving the snake to a quasiminimum-energy state. The third stage uses the Bellman (1957) optimality principle to fine-tune the snake to the global minimum-energy state. This three-stage scheme is optimized for both accuracy and speed.