Object Recognition From Haptic Glance at Visually Salient Locations

Reproducing the visuo-haptic interaction demonstrated by humans to sense and interpret visual and tactile stimuli is the main motivation of this research on tactile object recognition. An enhanced model of visual attention, consisting of information about color, contrast, curvature, entropy, symmetry, intensity, and orientation is developed to assist in selection of only relevant regions to be probed over the surface of an object. We aim to demonstrate that this visual information can be successfully employed to recognize the object from local tactile data, following the idea of haptic glance exhibited by humans (i.e., object recognition by a limited number of static contacts between the object and a finger). Due to the time-consuming nature of real tactile data acquisition, the process is first simulated and tested over a set of virtual objects prior to testing it on real rigid objects. A series of classifiers is trained and tested for the object recognition task and their performance is compared in terms of accuracy. The k-nearest neighbor classifier is shown to be the most promising algorithm among the tested classifiers for the object recognition task, both for simulated data (85.42% for four objects and 76.37% for six objects) and for real tactile sensor data (66.76% for four objects and 58.97% for six objects) when single imprints are used as a basis for recognition. Employing multiple imprints for object recognition yields a 100% accuracy for support vector machines (SVM) and k-nearest neighbors (kNN) classifiers when at least four imprints are considered.