I'm new to OpenCV (am actually using Emgu CV C# wrapper) and am attempting to do some object detection. I'm attempting to determine if an object matches a predefined set of objects (that I will have to define). The background is well lit and does not move. My objects that I am starting with are bottles and cans. My current approach is: Do absDiff with a previously taken background image to separate the background. Then dilate 4x to make the lighter areas (in labels) shrink. Then I do a binary threshold to get a big blog, followed by finding contours in this image. I then take the largest contour and draw it, which becomes my shape to either save to the accepted set or compare with the accepted set. Currently I'm using cvMatchShapes, but the double return value seems to vary widely. I'm guessing it is because it doesn't take into account rotation. Is this approach a good one? It isn't working well for glass bottles since the edges are hard to find... I've read about haar classifiers, but thinking that might be overkill for my task.