In this paper, we are presenting an object-level video editing tool that provides automatic object removal and video summarizing capabilities based on a user selected object. The tool has three main modules; object selection, object detection and background completion. In the object selection module, user selects the required object to be removed or used as reference for video summarization. The object contour is calculated using Livewire algorithm. The object detection module uses a correlation technique to detect the object in all frames. In the background completion module, the background is filled using a novel and efficient algorithm that combines the advantages of texture synthesis algorithms and inpainting techniques. A detailed description of the tool is presented in this paper along with a variety of experimental results.
In many areas of commerce, government, academia, and medicine, large collections of digital images are being used. Usually, the only way of searching these collections is by their name, or by browsing which is unpractical for large number of images. ImageSeeker aims at providing an improved technique to enhance image searching. It focuses on extracting visual contents from images and annotating them; it is based on the concept of CBIR (Content Based Image Retrieval) to retrieve the contents of the image based on what it has learnt during past trainings. When a user requests an image for a certain object, all images containing the same object will show up. ImageSeeker maintains high accuracy in finding matching results to the user's query. The system was tested on images containing natural scenes, specifically, see, sand, grass, clouds and sky.
KEYWORDS: 3D modeling, Reconstruction algorithms, 3D image processing, Cameras, 3D image reconstruction, Image segmentation, 3D acquisition, Calibration, Coded apertures, Data modeling
Image-based reconstruction from randomly scattered views is a challenging problem. We present a new algorithm that extends Seitz and Dyer's Voxel Coloring algorithm for reconstructing a voxelized representation of 3D object from a series of images. Voxel Coloring traverses a discretized 3D space in 'depth order' to identify voxels that have a unique coloring, constant across all possible interpretation of the scene. This approach has several advantages over existing stereo and structure-from-motion approaches to scene reconstruction. First, the technique can handle a great magnitude of visibility change. So, the cameras can be positioned far apart without degrading accuracy or run-time. Second, the technique integrates numerous imags to yield dense reconstruction without degrading run-time. Unlike Seitz and Dyer's algorithm, ours considers the perspective projection effect on the voxel size. We also present a different search method that traverses the voxels along the projection rays of the images. The new search method optimizes the search process by employing the geometry constraint of the pinhole camera model. In the new search method, the size of the voxels is non uniform depending on the distance from the images. Experimental results for simulated and real image sequences show the efficiency of our algorithm.
Camera calibration is a crucial problem for many industrial applications that incorporate visual sensing. In this paper, we present an approach to computing the intrinsic and extrinsic camera parameters taking into account radial lens distortion. The approach consists of directly searching for the camera parameters that best project 3D points of a calibration pattern onto intensity edges in a 2D image of this pattern without explicitly extracting the edges. Our approach can be considered an extension of Robert's method to obtain a more accurate camera model that adjusts for lens distortion. This approach tolerates less accuracy in the image features and avoids heavy dependence on individual, strongly-localized features since feature localization is instead included as part of the error measure used in the optimization process. After describing the details of our approach, the paper shows some experiments to evaluate the approach performance in terms of accuracy, sensitivity to initial conditions and reliability.
Slicing-fitting-linking (SFL) is a fast triangulation technique that guarantees building a closed mesh with consistent normals. The proposed technique can be used with different surface reconstruction cues such as laser scanner, stereo, SFS, and CT/MRI. The output SFL can be in the form of STL files that are suitable for most rapid prototyping machines. The technique has three tasks. The first task is to slice the 3D data points into 2D cross sections parallel to each other. The second task is to fit a curve to the data points of each cross section. The third task is to link the fitted curves to form the mesh. A detailed description of the algorithm is presented in this paper.
For faithful 3D reconstruction of objects with smooth surfaces, stereo vision techniques rely heavily on the surface texture. However, most of the real life objects lack this feature. To `sharpen' the textural content of visual surfaces, a structured-light sensing configuration has been used. This technique can be used to enhance the features used in solving the correspondence problem in computational stereo vision. A light pattern is projected to encode the smooth surface of the object. The observed light pattern is then used to compute surface properties. We present a simple design for a trinocular vision system with structured light using off-the-shelf components. The processing pipeline of the system consists of four stages. First, the light pattern is detected in the captured images. Second, the pattern is skeletonized using connected component labeling. Third, a maximum-weighted bipartite technique is used to do the matching. Finally, a global surface fitting technique is used to integrate the reconstruction from different views in one frame and fit a mesh of triangles to the integrated data. Results using real images are promising. The advantages of this system lie in its design simplicity, low cost and the potential for fast and parallel implementation.
This article presents an investigation study of stereo-based 3D surface reconstruction algorithms by providing an overview of the different approaches that have been investigated in the stereo literature during the last decade. This study considers only the two-views plain stereo algorithms and provides another classification for the stereo approaches based on the features used in the stereo literature. In addition, the article provides full details of two different stereo algorithms that give an idea of how stereo works.
KEYWORDS: 3D modeling, Data modeling, Visual process modeling, Genetic algorithms, Image registration, 3D image processing, 3D acquisition, Algorithm development, Systems modeling, Cameras
A novel approach is proposed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video cameras. The technique utilizes shape from shading to extract 3D information from 2D views of the jaw, and a novel technique for 3D data registration using genetic algorithms. The resulting 3D model can be used for diagnosis, treatment planning, and implant purposes. The overall purpose of this research is to develop a model-based vision system for orthodontics to replace traditional approaches. This system will be flexible, accurate, and will reduce the cost of orthodontic treatments.
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