Shape recognition: convexities, concavities and things in between

Previous studies on shape recognition have drawn different conclusions regarding the importance of specific object features, such as convexities, concavities and intermediate points. Some studies found evidence for a predominant role of convexities, whereas others favored concavities or intermediate parts. However, most studies have employed familiar objects or simple geometric shapes not necessarily containing curves (polygons) as their stimuli. Here we present a novel set of shapes with well-defined convexities, concavities and points between convexities and concavities. The shapes were composed of the sum of three different radial frequency (RF) components with random phases, segmented to remove all but variable lengths of contour centred on the feature of interest. Observers were required to match the segmented test shape to one of two subsequently presented whole-contour re-scaled test shapes. Observers were never presented with the same shape twice. Results show that for short (dot-sized) segment lengths, performance was significantly higher for convexities than for either concavities or intermediate points. For the convexities, performance remained constant as a function of segment length, and although performance improved with segment length for concavities and intermediate points, it only reached convexity performance at the largest lengths tested. No significant differences between concavities and intermediates were found. We present a model by which positions of convexities are extracted and connected to form shape primitives (polygons) that are matched to the test shapes. These results indicate that these closed shapes are encoded from the positions of convexities, rather than from the positions of either concavities or intermediates. Meeting abstract presented at VSS 2015.