A Machine Learning Approach to Recovery of Scene Geometry from Images

Computer Science – Computer Vision and Pattern Recognition

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

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 apply our approach to computer vision systems that recover the 3-D scene geometry from images. We focus on recovering 3D geometry from single images, stereo pairs and video sequences. Building these systems requires algorithms for doing inference as well as learning the parameters of conditional Markov random fields (MRF). Our system is trained unsupervisedly without using ground-truth labeled data. We employ a slanted-plane stereo vision model in which we use a fixed over-segmentation to segment the left image into coherent regions called superpixels, then assign a disparity plane for each superpixel. Plane parameters are estimated by solving an MRF labelling problem, through minimizing an energy fuction. We demonstrate the use of our unsupervised CRF learning algorithm for a parameterized slanted-plane stereo vision model involving shape from texture cues. Our stereo model with texture cues, only by unsupervised training, outperforms the results in related work on the same stereo dataset. In this thesis, we also formulate structure and motion estimation as an energy minimization problem, in which the model is an extension of our slanted-plane stereo vision model that also handles surface velocity. Velocity estimation is achieved by solving an MRF labeling problem using Loopy BP. Performance analysis is done using our novel evaluation metrics based on the notion of view prediction error. Experiments on road-driving stereo sequences show encouraging results.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

A Machine Learning Approach to Recovery of Scene Geometry from Images does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A Machine Learning Approach to Recovery of Scene Geometry from Images, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Machine Learning Approach to Recovery of Scene Geometry from Images will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-659303

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.