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A Generative Statistical Algorithm for Automatic Detection of Complex Postures

Fig 1

(A) Left: detected edges in an image of a wild-type animal exhibiting a (rare) coiled posture. The red/blue colors indicate the two possible edge polarities. Right: Detected body (yellow) and head (cyan) features with indicators of the corresponding orientations. (B) Graphical representations of the four head features and the four body features. Each feature corresponds to a spatial arrangement of areas with elevated probabilities for specific edge types. The red and blue shade colors represent areas with increased probabilities for positive and negative polarity edges, respectively. Green areas represent an overlap of red and blue areas. Red and blue dashed lines represent the orientation of positive and negative polarity edges, respectively. These masks introduce invariance to the exact location of the edges and are hence robust to variations in the width of the worm. (C) Left: a detected subinstantiation at an intermediate search step. The magenta blocks indicate which coarse locations have been visited so far during the search for the body. Right: Final detection of the posture using the coarse detection algorithm. Scale bars represent 100 μm.

Fig 1