Please bear in mind that responding to comments on my YouTube channel for specific errors is not possible in many cases. In addition to working a full time job (actually often full time and then some), I am often working on new projects, making the videos for new projects, and updating my website, all of which are very time consuming.

If I was preparing demo projects and videos for a company's products as a full time paying job I could provide troubleshooting assistance to all users, however currently I'm doing this as an unpaid hobby side-thing in addition to many other time commitments, therefore due to time constraints I am not able to provide specific troubleshooting assistance to every user.

That said, if you have followed this tutorial and encountered an obscure error at some point, I can offer the following suggestions:

1) Please watch the video a second time if you have not already and verify that all steps have been followed as stated. The computer I used to make the video is a standard off the shelf Windows 7 computer, and all configuration steps are shown and explained. If your hardware and operating system are the same or similar and an error is encountered, most likely either a seemingly minor step was accidentally skipped or not done identically to the video, or a minor typo was entered at some point.


2) If #1 does not resolve the concern, please proceed with the instructions in the 3rd OpenCV tutorial to compile OpenCV from source on your computer, then follow the configuration steps in the video again for that build of OpenCV, making sure to compile the program at the end with the same compiler as was used to compile OpenCV from source.

3) If #2 does not resolve the concern, please follow the steps in OpenCV tutorial 2, 4, or 5 to use OpenCV 2.x functions, or OpenCV in Qt, or Emgu CV in Visual Studio. It is extremely unlikely that none of these will work unless there is something severely wrong with your computer hardware or operating system install.

4) If none of the above resolved the concern, please repeat #1, #2, and #3 on a different computer.

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Histogram of Oriented Gradients (HOG) are feature descriptors used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy.

Navneet Dalal and Bill Triggs, researchers for the French National Institute for Research in Computer Science and Control (INRIA), first described Histogram of Oriented Gradient descriptors in their June 2005 CVPR paper. In this work they focused their algorithm on the problem of pedestrian detection in static images, although since then they expanded their tests to include human detection in film and video, as well as to a variety of common animals and vehicles in static imagery.

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En géométrie, le tesseract, aussi appelé 8-cellules ou octachore, est l'analogue quadridimensionnel du cube (tri-dimensionnel), où le mouvement le long de la quatrième dimension est souvent une représentation pour des transformations liées du cube à travers le temps. Le tesseract est au cube ce que le cube est au carré ; ou, plus formellement, le tesseract peut être décrit comme un 4-polytope régulier convexe dont les frontières sont constituées par huit cellules cubiques.

Une généralisation du cube aux dimensions plus grandes que trois est appelée un “hypercube”, “n-cube” ou “polytope de mesure”. Le tesseract est l'hypercube quadridimensionnel ou 4-cube. C'est un polytope régulier. C'est aussi un cas particulier de parallélotope : un hypercube est un parallélotope droit dont les arêtes sont de même longueur.

Selon l'Oxford English Dictionary, le mot « tesseract » a été conçu et utilisé pour la première fois en 1888 par Charles Howard Hinton dans son livre A New Era of Thought, à partir du τεσσερες ακτινες (« quatre rayons ») ionique grec, faisant référence aux quatre droites à partir de chaque sommet vers les autres sommets. De manière alternative, d'autres personnes ont appelé la même figure un “tétracube”.

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In this video we perform face and eye detection using Emgu CV, Visual Basic, and the default Haar classifier xlm files.

I've got more planned for the near future, stay tuned!

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OpenCV tutorial 5: Emgu CV with C#

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