Depth Recovery of Complex Surfaces from Texture-less Pairs of Stereo Images
نویسندگان
چکیده
In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT indexes are defined by means of disparity between the matching invariant features in rectified stereo images. The integration process is based on correcting the 3D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework.
منابع مشابه
Gibbs Random Field Model Based Weight Selection for the 2-D Adaptive Weighted Median Filter
establish electrical connections between components on multilayered circuit boards. Conditions such as excess filling and lack of filling cause electrical defects. The sample shown in Fig. 9 has a cavity, indicating lack of filling. The specular reflectance and variable size of the tungsten particles gives the surface a random texture. In this case, a total of 18 images were taken using stage p...
متن کاملRecovering LSHGCs and SHGCs
We examine the problem of computing shape descriptions from stereo, where by shape descriptions we mean 3-D volumetric descriptions of objects rather than a 2 2 -D depth map of the scene. We argue that intermediate 2 2 -D depth measurements may not be always directly available from stereo, especially when there are curved surfaces in the scene, and that 3-D volumetric descriptions of objects ma...
متن کاملA Machine Learning Approach to Recovery of Scene Geometry from Images
Recovering the 3D structure of the scene from images yields useful information for tasks such as shape and scene recognition, object detection, or motion planning and object grasping in robotics. In this thesis, we introduce a general machine learning approach called unsupervised CRF learning based on maximizing the conditional likelihood. We describe the application of our machine learning app...
متن کاملA Uniform Bayesian Framework for Integration
Vision researchers have advocated the integration of vision modules. However, generic system integration issues for recovering 3D information have not been adequately addressed in the literature. We propose a uniied Bayesian integration framework for interactions among the vision modules to obtain a complete 3D reconstruction from a pair of intensity (stereo) images. We integrate perceptual gro...
متن کاملIR Stereo Kinect: Improving Depth Images by Combining Structured Light with IR Stereo
RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very...
متن کامل