Fig. 7.

Examples of incorrect detection (left image in each panel) and their correction (right images). Some detection failures can be fixed by adjusting the detection threshold (i.e., a–c) but others require more extensive adjustments. In (d), the mouse is grooming its tail, with the nose positioned close to the tail base. Such cases are difficult to detect consistently in static images, but are apparent when viewed in the context of a movie. Although it is easy to modify the parameters to achieve correct detection in this frame, it is challenging to generate an algorithm that will reliably identify the nose under such cases. In some cases, application of another algorithm is required. For example, algorithm 7 (Additional file 1) is suitable when the tail is not included in the thresholded image. This indeed is the remedy for the examples in (d–g), sometimes combined with a modified threshold. In (f), the left image shows an obvious failure with the tail detected as nose. Detection is improved when the algorithm is changed, yet is still not perfect, since the shadow cast by the nose is detected as the nose. This problem is also beyond the scope of the built-in algorithms, as the shadow is darker than the nose and just as sharp