Presentation is loading. Please wait.

Presentation is loading. Please wait.

Stereo Vision System Principle of Operation: Difference between two cameras gives depth information Steps: –Compute disparity image –Find obstacles in.

Published byEmilio Chesterman Modified over 9 years ago

Similar presentations

Presentation on theme: "Stereo Vision System Principle of Operation: Difference between two cameras gives depth information Steps: –Compute disparity image –Find obstacles in."— Presentation transcript:

1 Stereo Vision System Principle of Operation: Difference between two cameras gives depth information Steps: –Compute disparity image –Find obstacles in each column –Approximate with rectangles –Filter in time domain

2 Challenges Quantization Noise Lighting condition Field of view Occlusion Range

3 The left camera sees: Point Grey Bumblebee Focal Length: 4mm Resolution: 640x480 Black & White

4 The right camera sees: Point Grey Bumblebee Focal Length: 4mm Resolution: 640x480 Black & White

5 Yielding a disparity map: Intensity indicates distance: the lighter, the closer White indicates an invalidated location.

6 Processing in each column: Intensity indicates confidence that an obstacle exists at that location. A darker line indicates a higher confidence.

7 Bounding with rectangles: Darker rectangles indicate higher confidence.

8 Data Representation Reference: http://www.darpa.mil/grandchallen ge/TechPapers/Stanford.pdf vs.

9 Tracking

Download ppt "Stereo Vision System Principle of Operation: Difference between two cameras gives depth information Steps: –Compute disparity image –Find obstacles in."

Similar presentations

Discontinuity Preserving Stereo with Small Baseline Multi-Flash Illumination Rogerio Feris 1, Ramesh Raskar 2, Longbin Chen 1, Karhan Tan 3 and Matthew.

Discontinuity Preserving Stereo with Small Baseline Multi-Flash Illumination Rogerio Feris 1, Ramesh Raskar 2, Longbin Chen 1, Karhan Tan 3 and Matthew.
Miroslav Hlaváč Martin Kozák 27. 07. 2011 Fish position determination in 3D space by stereo vision.

Miroslav Hlaváč Martin Kozák Fish position determination in 3D space by stereo vision.
Chapter 5 Integrals 5.1 Areas and Distances

Chapter 5 Integrals 5.1 Areas and Distances
Exposure and Dynamic Range Low contrast | High Contrast.

Exposure and Dynamic Range Low contrast | High Contrast.
CS 376b Introduction to Computer Vision 04 / 21 / 2008 Instructor: Michael Eckmann.

CS 376b Introduction to Computer Vision 04 / 21 / 2008 Instructor: Michael Eckmann.
Depth of Field. What the what?? Is Depth of Field.

Depth of Field. What the what?? Is Depth of Field.
Light Field Rendering Shijin Kong Lijie Heng.

Light Field Rendering Shijin Kong Lijie Heng.
December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.

December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.
MSU/CSE Fall 20141 Computer Vision: Imaging Devices Summary of several common imaging devices covered ( Chapter 2 of Shapiro and Stockman)

MSU/CSE Fall Computer Vision: Imaging Devices Summary of several common imaging devices covered ( Chapter 2 of Shapiro and Stockman)
Copyright  Philipp Slusallek Cs448.98.fall IBR: Model-based Methods Philipp Slusallek.

Copyright  Philipp Slusallek Cs fall IBR: Model-based Methods Philipp Slusallek.
Stockman MSU/CSE Fall 20081 Computer Vision: Imaging Devices Summary of several common imaging devices covered in Chapter 2 of Shapiro and Stockman.

Stockman MSU/CSE Fall Computer Vision: Imaging Devices Summary of several common imaging devices covered in Chapter 2 of Shapiro and Stockman.
Motion Depth Cues – Motion 1. Parallax. Motion Depth Cues – Parallax.

Motion Depth Cues – Motion 1. Parallax. Motion Depth Cues – Parallax.
MSU CSE 803 Stockman Perspective algebra Geometry of similar triangles yields algebra for computing world-image transformation.

MSU CSE 803 Stockman Perspective algebra Geometry of similar triangles yields algebra for computing world-image transformation.
Introduction to Computer Vision 3D Vision Topic 9 Stereo Vision (I) CMPSCI 591A/691A CMPSCI 570/670.

Introduction to Computer Vision 3D Vision Topic 9 Stereo Vision (I) CMPSCI 591A/691A CMPSCI 570/670.
1 Image filtering Images by Pawan SinhaPawan Sinha.

1 Image filtering Images by Pawan SinhaPawan Sinha.
1 Images and Transformations Images by Pawan SinhaPawan Sinha.

1 Images and Transformations Images by Pawan SinhaPawan Sinha.
1 Image filtering Hybrid Images, Oliva et al.,

1 Image filtering Hybrid Images, Oliva et al.,
CSE473/573 – Stereo Correspondence

CSE473/573 – Stereo Correspondence
CIS 681 Distributed Ray Tracing. CIS 681 Anti-Aliasing Graphics as signal processing –Scene description: continuous signal –Sample –digital representation.

CIS 681 Distributed Ray Tracing. CIS 681 Anti-Aliasing Graphics as signal processing –Scene description: continuous signal –Sample –digital representation.
100+ Times Faster Weighted Median Filter [cvpr ‘14]

100+ Times Faster Weighted Median Filter [cvpr ‘14]

Similar presentations

Ads by Google