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I have a set of simple rigid 3D objects that I wish to detect and recognize from an image (let's say 5 to 10 classes). The objects are simple in sense that they are cylinders in one color or rectangles with simple patterns (stripes for example) or some similarly simple shape. The objects are significantly different from one another (there aren't for example two classes where one is a large cylinder and another one is the same but smaller cylinder). Because the textures are pretty simple (solids and/or simple patterns), bag-of-words approach fails (they do not contain significant number of unique edges).

While one possible approach is coding manually each classifier (manual feature extraction etc), is there a simple data driven approach (Haar/LBP classifier for example) that would work? If Haar or LBP are good for solving this problem, how would one solve the problem of unknown relative viewpoint (and by such perspective distortion, rotation, etc)? Would just providing positive images from all possible viewpoints for an object converge or is there something else that's usually done? The detection and recognition should run in real-time.

Damjan Dakic
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Based on your description of your problem, I see several drawbacks of a Haar or LBP-based detector. First, these features do not use color, which seems to be important here. Second, a classifier using Haar or LBP features is sensitive to in-plane and out-of-plane rotation. If your objects can be in any 3D orientation, you would need to discretize the range of 3D rotations and train a separate detector for each one. For example, for face detection you typically use two detectors: one for frontal faces, and one for profile faces. Finally, if there is not enough texture for bag-of-words, there also may not be enough texture for Haar or LBP.

Since your objects are simple 3D shapes, I would start by trying to detect straight lines and circles using the Hough transform, and trying to group them to form the object's outlines.

Dima
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  • Yes, that's how I thought of handling orientation for Haar/LBP. Regarding your last sentence, how would one approach the grouping part? – Damjan Dakic Dec 08 '14 at 18:41
  • That's a hard question. There are lots of research papers on the subject. Google "perceptual grouping". Maybe in your case, color would be a good cue. – Dima Dec 09 '14 at 16:26