Visual metrics: discriminative power through flexibility.

T J Janssen; F J Blommaert

doi:10.1068/p2951

Home/Archive/PubMed

PubMed · 11145088

Visual metrics: discriminative power through flexibility.

T J Janssen·F J Blommaert

View original source ↗Export metadata ↓

Abstract

An important stage in visual processing is the quantification of optical attributes of the outside world. We argue that the metrics used for this quantification are flexible, and that this flexibility is exploited to optimise the discriminative power of the metrics. We derive mathematical expressions for such optimal metrics and show that they exhibit properties resembling well-known visual phenomena. To conclude, we discuss some of the implications of flexible metrics for visual identification.

Explore related subjects

Discrimination, Psychological Humans Lighting Memory Models, Psychological Psychometrics Visual Perception Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

T J Janssen, F J Blommaert. 2000. Visual metrics: discriminative power through flexibility.. https://doi.org/10.1068/p2951

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2006

Discrimination training alters object representations in human extrastriate cortex.

Visual object recognition relies critically on learning. However, little is known about the effect of object learning in human visual cortex, and in particular how the spatial distribution of training effects relates to the distribution of object and face selectivity across the cortex before training. We scanned human subjects with high-resolution functional magnetic resonance imaging (fMRI) while they viewed novel object classes, both before and after extensive training to discriminate between exemplars within one of these object classes. Training increased the strength of the response in visual cortex to trained objects compared with untrained objects. However, training did not simply induce a uniform increase in the response to trained objects: the magnitude of this training effect varied substantially across subregions of extrastriate cortex, with some showing a twofold increase in response to trained objects and others (including the right fusiform face area) showing no significant effect of training. Furthermore, the spatial distribution of training effects could not be predicted from the spatial distribution of either pretrained responses or face selectivity. Instead, training changed the spatial distribution of activity across the cortex. These findings support a dynamic view of the ventral visual pathway in which the cortical representation of an object category is continuously modulated by experience.

Discrimination, Psychological↗

PubMed2006

3D shape discrimination using relative disparity derivatives.

Three-dimensional (3D) shape discrimination could be achieved using relative disparity signals or it could be achieved using a higher-order disparity derivative detector. Two 3D shape discrimination tasks were used to distinguish between these possibilities: a within-shape task and a between-shape task. Disparity thresholds were larger when discriminating within the same shape than when discriminating between shapes. More importantly, within-shape discriminations were dependent on the pedestal disparity (distance from fixation) whereas between-shape discriminations were not. The results suggest that a mechanism sensitive to higher-order disparity derivatives can achieve discrimination between different 3D shapes.

Discrimination, Psychological↗

PubMed2006

Learning alters local face space geometry.

The effects of learning on the geometry of face space were investigated by measuring thresholds for discrimination and recognition of synthetic faces. This was based on a novel experimental technique that permitted measurement of psychometric functions for face recognition. Two major results were obtained. First, thresholds for face recognition were significantly better than thresholds for discrimination among novel faces. Second, rapid discrimination in the neighborhood of learned faces was better than discrimination near novel faces. Control experiments showed that this discrimination improvement occurred only with learned faces, and it could not be explained by generalized discrimination learning. Thus, face learning selectively alters or distorts face space in the vicinity of learned faces. This alteration may be due to an improvement in the signal/noise ratio as a result of face learning.

Discrimination, Psychological↗