Real-time 3D visual sensor for robust object recognition.
ABSTRACT This paper presents a novel 3D measurement system, which yields both depth and color information in real time, by calibrating a time-of-flight and two CCD cameras. The problem of occlusions is solved by the proposed fast occluded-pixel detection algorithm. Since the system uses two CCD cameras, missing color information of occluded pixels is covered by one another. We also propose a robust object recognition using the 3D visual sensor. Multiple cues, such as color, texture and 3D (depth) information, are integrated in order to recognize various types of objects under varying lighting conditions. We have implemented the system on our autonomous robot and made the robot do recognition tasks (object learning, detection, and recognition) in various environments. The results revealed that the proposed recognition system provides far better performance than the previous system that is based only on color and texture information.
- SourceAvailable from: Ghassan Hamarneh
Conference Proceeding: N-SIFT: N-DIMENSIONAL SCALE INVARIANT FEATURE TRANSFORM FOR MATCHING MEDICAL IMAGES[show abstract] [hide abstract]
ABSTRACT: We present a fully automated multimodal medical image matching technique. Our method extends the concepts used in the computer vision SIFT technique for extracting and matching distinctive scale invariant features in 2D scalar images to scalar images of arbitrary dimensionality. This extension involves using hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. These features were successfully applied to determine accurate feature point correspondence between pairs of medical images (3D) and dynamic volumetric data (3D+time).Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on; 05/2007
- [show abstract] [hide abstract]
ABSTRACT: We describe a method for tracking people using a Time-of-Flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (Expectation Maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the Time-of-Flight principles for people tracking but also their current limitations.2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 01/2008;
- [show abstract] [hide abstract]
ABSTRACT: This paper presents a method for precise 3D en-vironment mapping. It employs only a 3D Time-of-Flight (ToF) camera and no additional sensors. The camera pose is estimated using visual odometry. Imprecision of depth measurements caused by external interfering factors, e.g. sunlight or reflectiv-ities are properly handled by several filters. Pose tracking and mapping is performed on-the-fly during exploration and allows even for hand-guided operation. The final refinement step, comprising error distribution after loop-closure and surface smoothing, further increases the precision of the resulting 3D map 1 .