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Enhanced Computer Vision with
Microsoft Kinect Sensor: A Review
Jungong Han, Member, IEEE, Ling Shao,Senior Member,
IEEE, ...
Covered Areas
•
•
•
•
•
•
•

Kinect Mechanism
Preprocessing
Object Tracking and Recognition
Human Activity Analysis
Hand G...
Kinect Mechanism
1. Kinect Sensing Hardware
Kinect Mechanism
2. Kinect Software Tools
Kinect Mechanism
3. Kinect Performance Evaluation

Kinect Censor >TOF cameras
Preprocessing
 Kinect Recalibration
 Depth Data Filtering
Object Tracking and Recognition
 Object Detection and Tracking
 Object and Scene Recognition
Object Tracking and
Recognition
Human Activity Analysis
 Pose Estimation
 Activity Recognition
Hand Gesture Analysis
 Hand Detection
 Hand Pose Estimation
 Gesture Classification
Indoor 3-D Mapping
 Sparse Feature Matching
 Dense Point Matching
Problems, Outlook and
Conclusion
 Real-World Applications
 Efficient Integration of Algorithms
 Information Fusion
 Out...
References
 http://vision.in.tum.de/research/rgb-d_sensors_kinect
 http://www.metrilus.de/range-imaging/time-of-flightca...
Enhanced Computer Vision with Microsoft Kinect Sensor: A Review
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Enhanced Computer Vision with Microsoft Kinect Sensor: A Review

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Enhanced Computer Vision with Microsoft Kinect Sensor: A Review

  1. 1. Enhanced Computer Vision with Microsoft Kinect Sensor: A Review Jungong Han, Member, IEEE, Ling Shao,Senior Member, IEEE, Dong Xu, Member, IEEE, And Jamie Shotton, Member, IEEE Presenter: Abu Saleh Md Musa Roll no: 0907013
  2. 2. Covered Areas • • • • • • • Kinect Mechanism Preprocessing Object Tracking and Recognition Human Activity Analysis Hand Gesture Analysis Indoor 3-D Mapping Problems, Outlook and Conclusion
  3. 3. Kinect Mechanism 1. Kinect Sensing Hardware
  4. 4. Kinect Mechanism 2. Kinect Software Tools
  5. 5. Kinect Mechanism 3. Kinect Performance Evaluation Kinect Censor >TOF cameras
  6. 6. Preprocessing  Kinect Recalibration  Depth Data Filtering
  7. 7. Object Tracking and Recognition  Object Detection and Tracking  Object and Scene Recognition
  8. 8. Object Tracking and Recognition
  9. 9. Human Activity Analysis  Pose Estimation  Activity Recognition
  10. 10. Hand Gesture Analysis  Hand Detection  Hand Pose Estimation  Gesture Classification
  11. 11. Indoor 3-D Mapping  Sparse Feature Matching  Dense Point Matching
  12. 12. Problems, Outlook and Conclusion  Real-World Applications  Efficient Integration of Algorithms  Information Fusion  Outlook for the Future
  13. 13. References  http://vision.in.tum.de/research/rgb-d_sensors_kinect  http://www.metrilus.de/range-imaging/time-of-flightcameras/  http://www.cs.washington.edu/rgbd-dataset/  http://en.wikipedia.org/wiki/Kinect

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