Stefano Tommesani

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Building Installshield LE 2012 setups from the command line for CI

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In Visual Studio 2012, after installing Installshield LE 2012, you can include setup projects into your solution, as shown below:

AutoSetupSolution

Adding the Setup project to the solution, and including it into the Release configuration, makes for a quick, 1-click compilation procedure of both the main app and the installer. Still, what if we want to use a Continuous Integration software (e.g. Jenkins and countless others) and run the compilation of the setup from an external app? The ISL file extension used by InstallShield LE does not seem to be directly supported by CI softwares, and using ISCmdBld.exe as specified in Flexera's docs is not feasible, as that part of Installshield is missing from the LE edition (the L stands for Limited, so no surprises here). However, this specific project type can be compiled within VS IDE, so we can use MSBuild to compile it from the command line.

Last Updated on Thursday, 31 October 2013 12:45
 

Integrating Visual Studio unit testing with release management software

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AskWatchABS1In a previous post, Unit testing with Visual C++ 2012, the unit testing features of Visual Studio 2012 were shown to be an effective way to improve software quality by introducing various tests. Still, instead of having to run these tests inside the Visual Studio IDE, it would be even better to integrate these tests into a release management software, so that they are automatically performed during each build of the system.

For running these unit tests outside the Visual Studio IDE, from this MSDN page we find out that there are three ways to run them from the command line:

Last Updated on Sunday, 13 October 2013 15:37
 

Background subtraction: ViBe

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The ViBe algorithm

In the paper "ViBe: A universal background subtraction algorithm for video sequences", Olivier Barnich and Marc Van Droogenbroeck introduce several innovative mechanisms for motion detection:

ViBeSearchingPixelsPixel model and classification process

Let's denote by v(x) the value in a given Euclidean color space taken by the pixel located at x in the image, and by vi a background sample value with an index i. Each background pixel x is modeled by a collection of N background sample values
M(x) = {v1, v2, . . . , vN}
taken in previous frames. To classify a pixel value v(x) according to its corresponding model M(x), we compare it to the closest values within the set of samples by defining a sphere SR(v(x)) of radius R centered on v(x). The pixel value v(x) is then classified as background if the cardinality of the set intersection of this sphere and the collection of model samples M(x) is larger than or equal to a given threshold. The classification of a pixel value v(x) involves the computation of N distances between v(x) and model samples, and of N comparison with a thresholded Euclidean distance R.
The accuracy of the ViBe model is determined by two parameters only: the radius R of the sphere and the minimal cardinality. Experiments have shown that a unique radius R of 20 (for monochromatic images) and a cardinality of 2 are appropriate. There is no need to adapt these parameters during the background subtraction nor to change them for different pixel locations. 

Last Updated on Tuesday, 15 October 2013 11:50
 

Comparing background subtraction algorithms

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The bgslibrary by Andrews Sobral includes over 30 background subtraction algorithms, a common C++ framework for comparing them, and an handy C++/MFC or Java app to see them running on video files or live feed from a webcam.

I have run all the background subtraction algorithms against a test sequence that is really hard, as the camera is slightly shaking and the trees are waving due to the strong wind, so building a reliable estimation of the background of this scene is definitely an hard task. The video sequence was also cropped so that it starts with moving objects already in the scene, to check how quickly the background subtraction algorithms react to permanent changes after the initialization. No filtering was performed on the results, as these videos are meant only to compare the relative performance of background subtraction algorithms.

BGSScreenShotEvery video highlighting an algorithm has the screen divided in quadrants:

  • the top-left quadrant shows the input video to be processed by the algorithm
  • the top-right quadrant shows the foreground mask (in white)
  • the bottom-left quadrant shows the estimated background; not every algorithm has an estimated background that can be displayed, so this quadrant may be blank
  • the bottom-right quadrant shows the name of the algortihm in the bgslibrary and the frame number
Last Updated on Wednesday, 18 September 2013 23:26
 

AskWatch: navigation

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After adding your kid, AskWatch will start grabbing data about your son for a few seconds, or a bit more depending on the speed of your internet connection. In the main window, a new tab with the name of your son will be added, and the new screen contains all the information about your son, in order:

ProfileDescription

Last Updated on Wednesday, 07 August 2013 22:59
 

AskWatch: adding your kids

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Start AskWatch from the Start screen (in Windows 8) or from the Start menu (in Windows 7). The following, mostly empty screen appears:

AskWatch43

Last Updated on Wednesday, 07 August 2013 23:00
 
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Latest Articles

Building Installshield LE 2012 setups from the command line for CI
In Visual Studio 2012, after installing Installshield LE 2012, you can include setup projects into your solution, as shown below: Adding the Setup project to the solution, and including it into the Release
Integrating Visual Studio unit testing with release management software
In a previous post, Unit testing with Visual C++ 2012, the unit testing features of Visual Studio 2012 were shown to be an effective way to improve software quality by introducing various tests. Still, instead of having to run
Background subtraction: ViBe 18 September 2013, 14.28 Video
Background subtraction: ViBe
The ViBe algorithm In the paper "ViBe: A universal background subtraction algorithm for video sequences", Olivier Barnich and Marc Van Droogenbroeck introduce several innovative mechanisms for motion detection: Pixel model
Background subtraction: neural and neuro-fuzzy methods
Performance map Adaptive SOM of Maddalena and Petrosino (2008) paper link {youtube}o7VdgyoxPgA{/youtube}  Detection of moving objects in video streams is the first relevant step of information extraction in many
Background subtraction: methods based on eigen features
Performance map Eigenbackground / SL-PCA of Oliver et al (2000) paper link {youtube}4c1lLY3ta08{/youtube} We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in

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