Abstract
Visual inputs to the human brain are very rich, originating from about 130 million photoreceptors. Despite this high resolution, little information is actually necessary for recognizing objects. For example, just a few fragments of an object’s outline are usually required for its recognition. It is largely unknown which type of fragments provide the largest amount of information for object recognition. Past research has either tested mid-level vision fragments, such as curved segments, or only simple dots placed along the object’s contours. So far, mid- and low-level fragments in object recognition have never been compared. Here, we hypothesize that observers need a smaller number of curved segments than dots to recognize objects while keeping luminance, fragment’s size and number of fragments comparable. To test this hypothesis, we developed a novel object encoding algorithm based on contours’ extraction and on convolutions between the object’s contours and low- as well as mid-level fragments. Ten human observers identified thirty fragmented objects (curved segments vs. dots) generated by our algorithm. Participants were able to recognize every object both expressed as low- and mid-level fragments. The odds of recognizing objects were greater, the more fragments were presented. In line with our hypothesis, the interaction effect (fragment type x number of fragments) shows that with an increasing number of fragments, indeed less curved segments are needed than dots to recognize the objects. These results suggest that additional local information provided by the mid-level fragments are used by observers, which facilitates object recognition. Our results are important both for basic vision research as well as visual neuroprostheses where the number of electrodes on the retinal or cortical implant is strongly limited.