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Daniele Benedetti, Federica Cello, Rachele Agostini, Giovanni Punzi, Maria Michela Del Viva; How much contrast information is needed for reliable fast image recognition?. Journal of Vision 2011;11(11):1065. doi: 10.1167/11.11.1065.
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The visual system faces the problem of extracting biologically-relevant information from a large flux of input data. This can be obtained by summarizing complex scenes to extract meaningful features (Barlow, 1959; Marr, 1976) by using image primitives (edges, bars), encoded physiologically by specific configurations of receptive fields (Hubel & Wiesel, 1962). This work follows a pattern-filtering approach, based on the principle of efficient information coding under real-world limitations (Punzi & Del Viva, VSS-2006). The model, applied to black and white images predicts from very general principles the structure of early visual filters and identifies salient features (edges, lines) providing highly compressed “primal sketches” of visual scenes (Del Viva & Punzi VSS-2008). Human subject are able to identify such sketches (2AFC procedure) in rapid identification tasks (10–20 ms), with very high accuracy (up to 90%), comparable to that for fully detailed original images (Del Viva et al., VSS-2010). Here, we extended previous computational and psychophysical experiments to gray-level images to investigate whether this early visual image processing can make use of a larger amount of input information. Results with 4 gray level images (2 bits) provided sketches with a lesseror equal level of compression, and comparable information content to those obtained with 1 bit. Performance in recognizing image sketches did not improve by increasing input information either. Our results provide support to the idea that only a very limited contrast information is used for fast image recognition, and that this is fully explained by our model of efficient information within constraints.
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