Effective high resolution 3D geometric reconstruction of heritage and archaeological sites from images

Motivated by the need for a fast, accurate, and high-resolution approach to documenting heritage and archaeological objects before they are removed or destroyed, the goal of this paper is to develop and demonstrate advanced image-based techniques to capture the fine 3D geometric details of such objects. The size of the object may be large and of any arbitrary shape which presents a challenge to all existing 3D techniques. Although range sensors can directly acquire high resolution 3D points, they can be costly and impractical to set up and move around archaeological sites. Alternatively, image-based techniques acquire data from inexpensive portable digital cameras. We present a sequential multi-stage procedure for 3D data capture from images designed to model fine geometric details. Test results demonstrate the utility and flexibility of the technique and prove that it creates highly detailed models in a reliable manner for many different types of surface detail

Novel haptic interface For viewing 3D images

In recent years there has been an explosion of devices and systems capable of displaying stereoscopic 3D images. While these systems provide an improved experience over traditional bidimensional displays they often fall short on user immersion. Usually these systems only improve depth perception by relying on the stereopsis phenomenon. We propose a system that improves the user experience and immersion by having a position dependent rendering of the scene and the ability to touch the scene. This system uses depth maps to represent the geometry of the scene. Depth maps can be easily obtained on the rendering process or can be derived from the binocular-stereo images by calculating their horizontal disparity. This geometry is then used as an input to be rendered in a 3D display, do the haptic rendering calculations and have a position depending render of the scene. The author presents two main contributions. First, since the haptic devices have a finite work space and limited resolution, we used what we call detail mapping algorithms. These algorithms compress geometry information contained in a depth map, by reducing the contrast among pixels, in such a way that it can be rendered into a limited resolution display medium without losing any detail. Second, the unique combination of a depth camera as a motion capturing system, a 3D display and haptic device to enhance user experience. While developing this system we put special attention on the cost and availability of the hardware. We decided to use only off-the-shelf, mass consumer oriented hardware so our experiments can be easily implemented and replicated. As an additional benefit the total cost of the hardware did not exceed the one thousand dollars mark making it affordable for many individuals and institutions

Pengukuran Jarak Objek Pejalan Kaki terhadap Kamera Menggunakan Kamera Stereo Terkalibrasi dengan Segmentasi Objek Histogram Of Oriented Gradient

The distance measurement of pedestrian object to camera using a calibrated stereo camera has been investigated. The study was conducted by comparing Data-A and Data-B with different conditions and environment data interpretation. The stereo vision methods include camera calibration, image rectification, disparity counting, three-dimensional reconstruction and object segmentation. The object segmentation is performed using the Histogram of Oriented Gradient feature to segment pedestrian object. Meanwhile, to determine the distance value is based on the information of the centroid of the bounding box segmented object.The calculations were performed using the Euclidian Distance calculation method to find the shortest distance between the centroid of the bounding box with both cameras. From the research results, the best accuracy was obtained with measurement error of 4%

Depth extraction from monocular video using bidirectional energy minimization and initial depth segmentation

In this paper, we propose to extract depth information from a monocular video sequence. When estimating the depth of the current frame, the bidirectional energy minimization in our scheme considers both the previous frame and next frame, which promises a much more robust depth map and reduces the problems associated with occlusion to a certain extent. After getting an initial depth map from bidirectional energy minimization, we further refine the depth map using segmentation by assuming similar depth values in one segmented region. Different from other segmentation algorithms, we use initial depth information together with the original color image to get more reliable segmented regions. Finally, detecting the sky region using a dark channel prior is employed to correct some possibly wrong depth values for outdoor video. The experimental results are much more accurate compared with the state-of-the-art algorithms

Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution

In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images. Conventional stereo matching algorithms fail when addressing anaglyph images that do not have similar intensities on their two respective view images. To resolve this problem, we propose two novel data costs using local color prior and reverse intensity distribution factor for obtaining accurate depth maps. To colorize an anaglyph image, each pixel in one view is warped to another view using the obtained disparity values of non-occluded regions. A colorization algorithm using optimization is then employed with additional constraint to colorize the remaining occluded regions. Experimental results confirm that the proposed unified framework is robust and produces accurate depth maps and colorized stereo images.National Research Foundation of Korea (Basic Science Research Program (Ministry of Education, NRF-2012R1A1A2009495))National Research Foundation of Korea (Korea government (MSIP), grant No. NRF-2013R1A2A2A01069181

A Nonlocal Method with Modified Initial Cost and Multiple Weight for Stereo Matching

This paper presents a new nonlocal cost aggregation method for stereo matching. The minimum spanning tree (MST) employs color difference as the sole component to build the weight function, which often leads to failure in achieving satisfactory results in some boundary regions with similar color distributions. In this paper, a modified initial cost is used. The erroneous pixels are often caused by two pixels from object and background, which have similar color distribution. And then inner color correlation is employed as a new component of the weight function, which is determined to effectively eliminate them. Besides, the segmentation method of the tree structure is also improved. Thus, a more robust and reasonable tree structure is developed. The proposed method was tested on Middlebury datasets. As can be expected, experimental results show that the proposed method outperforms the classical nonlocal methods