Dense disparity map extraction is one of the most active research areas in computer vision. It tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed to solve stereo matching problems. This paper proposes a new stereo matching algorithm, capable of generating the disparity map in real-time and with high accuracy. A novel stereo matching approach is based on per-pixel difference adjustment for the absolute differences, gradient matching and rank transform. The selected cost metrics are aggregated using guided filter. The disparity calculation is performed using dynamic programming with self-adjusting and adaptive penalties to improve disparity map accuracy. Our approach exploits mean-shift image segmentation and refinement technique to reach higher accuracy. In addition, a parallel high-performance graphics hardware based on Compute Unified Device Architecture is used to implement this method. Our algorithm runs at 36 frames per second on 640 × 480 video with 64 disparity levels. Over 707 million disparity evaluations per second (MDE/s) are achieved in our current implementation. In terms of accuracy and runtime, our algorithm ranks the third place on Middlebury stereo benchmark in quarter resolution up to the submitting.

Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision. Usually, to get accurate 3D results the camera should be manually calibrate accurately as well. This paper proposes a robust approach to Auto Calibration stereo camera Without intervention of the user. There are several methods and techniques of calibration that have been proven, in this work we exploiting the geometric constraint, namely, the epipolar geometry. We specifically focuses to use 7 techniques for Features Extraction (SURF, BRISK, FAST, FREAK, MinEigen, MSERF, SIFT), however tries to establish the correspondences between points extracted in stereo images with Various Matching Techniques (SSD, SAD, Hamming). Then we exploits the Fundamental Matrix to estimate the epipolar Line by choosing the perfect Eight-point algorithms (Norm8Point, LMedS, RANSAC, MSAC, LTS). A large number of experiments have been carried out, and very good results have been obtained by Comparison & choice the perfect technique in every stage.

Dense depth map extraction is a dynamic research field in a computer vision that tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed. The local methods based on block matching that are prevalent due to the linear computational complexity and easy implementation. This local cost is used on global methods as graph cut and dynamic programming in order to reduce sensitivity to local to occlusion and uniform texture. This paper proposes a new method for matching images based on a two-stage of block matching as local cost function and dynamic programming as energy optimization approach. In our work introduce the two stage of the zero-mean sum of absolute differences (ZSAD) combined with dynamic programming: the smoothness and ordering constraints are used to optimize correspondences. Stereo matching accuracy and runtime are the fundamental metrics to evaluate the stereo matching methods. The real-time has become a reality through the complexity reduction of the calculation and the use of parallel high-performance graphics hardware. In this paper we evaluate the developed method on using Middlebury stereo benchmark and, we propose a GPU CUDA implementation in order to accelerate our algorithm and reach the real time.

In this paper a robust and simple scheme is presented for three dimensional (3D) shape reconstruction of real object. A novel composite pattern technique is proposed for projecting the light pattern on the object of interest. The proposed scheme reduces the number of patterns by combining the primary color coded channels into one composite format. Our approach uses both spatial and temporal intensity variation for calibration and construction phase. Gamma calibration is considered with the propose scheme. High quality depth map is obtained from the linear light reflected by the shape of object without complex calculations. Experimental results demonstrated that proposed technique is fast and exhibit high level of precision. In addition hardware cost is minimized as compare to current calibration procedures used in structured light scanning system. Our scheme requires a digital camera, flashlight and mask of pattern only.

This paper contributes to 3D facial synthesis by presenting a novel method for parameterization using Landmark Point detection. The approach presented aims at improving facial recognition even in varying facial expressions, and missing data in 3D facial models. As such, the prime objective was to develop an automatically embedded process that can detect any frontal face in 3D face recognition systems, with face segmentation and surface curvature information. Using the hybrid interpolation method, experiments on facial landmarks were performed on 4950 images from Face Recognition Grand Challenge database (FRGC). Distinctive facial landmarks from the nose–tips, Limits mouth and two eye corners formed the statistical inputs for Iterative Closest Point (ICP) in the Point Distribution Model (PDM). Performance or landmark localization is reported by using percentage deviation from the mean 3D profile. Localization results and estimated data on landmark locations demonstrate that the method confirms its effectiveness for proposed application.

This paper presents our methodology for Landmark Point detection to improve 3D face recognition in a presence of variant facial expression. The objective was to develop an automatic process for distinguishing and segmenting to be embedded in a 3D face recognition system using only 3D Point Distribution Model (PDM) as input. The approach used hydride method to extract this features from the surface curvature information. Landmark Localization is done on the segmented face via finding the change that decreases the deviation of the model from the mean profile. Face registering is achieved using previous anthropometric information and the localized landmarks. The results confirm that the method used is accurate and robust for the proposed application.

The present paper is a new technique for the detection of persons, with combination of the three approaches (Adaptive Gaussian Mixture, Objects Classification, and Stereo Vision) with the integration of the multithreaded architecture. The study and validation at high-level of these descriptors was used to determinate the advantages and drawbacks of each approach. Several tests were made to evaluate the precision, execution time, complexity and sensitivity to different situations.