Perceptual classification boundaries between 2D shapes

Abstract

One of the key variables that have been used for identifying objects in two-dimensional imagery is shape. Humans have the ability to discriminate between shapes and can perceive an imperfect shape as belonging to a particular object class. Each object class has a boundary where a human perceives an object as belonging to one class or the other. Perceptual classification boundaries define the human perception that classifies a shape as belonging to a particular object class. In this paper, the perceptual difference between several primitive two-dimensional object shapes is examined. Unlike the human, computer recognition algorithms are typically designed to recognize a finite number of classes of objects. This paper focuses on two-class and three-class recognition problems using simple primitive shapes consisting of a single-filled, closed loop contour. To determine the perceptual classification boundary, one primitive shape is morphed into another, and a group of persons are used to quantify where the perceived boundary is located between objects. Various shape measures are then applied to the primitive shapes to determine how well some current measures can quantify the perceived classification boundary. The addition of gaussian noise to the primitive two-dimensional shapes is also examined along with quantitative and perceived human results. The results suggest that the tested quantitative measures do not provide results similar to human perception. Some measures are better than others at achieving perceptual classification. The paper demonstrates that an approximate perceptual classification measure can be achieved by using human observer perceptual thresholds along with a quantitative measure.