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I myself make the source code for my software freely available for the purpose of scientific dissemination. I work in a field (computer vision) where complex software is developed and forms the basis of experiments. Publishing papers which describe the algorithm and results is the main output but this has some limitations. Often there isnt the space to describe all the subtle aspects which make the program work. Perhaps the author does not even appreciate themselves what it is which is really driving the process (code can chge an aweful lot from conception to use). Also we want others to build on our work and that process is made much more difficult when everyone has to re-implement algorithms from scratch, possibly from incomplete or inaccurate papers.

Sharing code to explain techniques is something that has happened in experimental science for many years. Mordern open source frameworks such as GNU have made this task much easier by providing tools and standards. The web has also massively improved distribution.

The technology employed (both hardware and software) is limited. CMOS sensors of the type described suffer from poor signal to noise as well as interlacing artifacts. Pixel jitter is of major importance in machine vision and I doubt these sensors offer much clock control over and above the 1 pixel mark (if any).

The matching algorithm described is very primitive, assuming rotation in depth between views doesn't effect the scene projection into the image - ooh but it does. The concensus matching algorithm is very simple and whilst it does recognise the problems of illumination variation it fails to solve the problem in a manner you could describe as robust. Also contrary to popular belief you cannot robustly recover depth from every pixel n the image! There is no evidence that the human vision system does it (without knowledge of the object) so why are people trying it? Even if you ataempt it you are going to need some way of telling which data is more accurate than not in order to start using the results. Edges are your best bet and I didn't see any evidence of preprocessing described in their system (although to be fair I only read it breifly).

I appreciate that this is supposed to be a cheap system and thus its limitations are probably to be expected. Might be fun to play with for a hundred Euros or so.

For more state of the art look at what is possible you could do better than take a look at TINA an open source machine vision system with a very sophisticated stereo depth estimation algorithm (we even built a chip to accelerate it!)

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!

The Intel computer vision library is not the only such resource available. The TINA machine vision system has been developed since 1986 and provides functionality for the machine vision researcher at both the infrastructure level (datastructures and functions for an enormous range of mathematical, statistical and image processing tasks) as well as state-of-the-art solutions to many machine vision problems. These include low-level feature extraction, robust primitive fitting, object tracking, 2D object recognition and 3D object location. Indeed the stereoscopic subsystems in TINA (PMF, Stretch Correlation) have been viewed for many years as the standard for edge based stereo. TINA is almost unique as a resource and living archive of over 70 man years of research and over 200 peer reviewed publications in machine vision and medical image analysis. Functionality in TINA has practical utility in several industrial contexts.
For the past 5 years TINA has been provided as open source under an LGPL license and development is now based at the University of Manchester, UK.

Whilst I am very pleased that Intel recognise the importance of machine vision research and can only commend them on their open source approach I have some reservations regarding the use of OpenCV by the research community at large. Certainly their motives are business orientated (and one cannot argue with this). Therefore, however, the contents of their library are ultimately dictated by what Intel want not necessarily what the research community might need or indeed what is even possible (such as dense estimates of stereo).

Open Source software is vital in research disciplines where there is a significant software component. What better way to disseminate your results than to encapsulate your entire experimental apparatus in a tar file! Why should others in the field waste time reimplementing your algorithms (probably incorrectly) in order to duplicate your results. A process which sits at the very heart of any scientific endeavour.

TINA has recently received direct funding from the European Union for developed as the open source environment for machine vision and medical image analysis research. For more details of TINA visit the website at http://www.tina-vision.net

Sorry to rant a bit but it is not often I read something on here that I know so much about!