Biomedical subjects

F Cutzu

Publications and source records attributed to F Cutzu.

4 recordsLinked to original sources

PubMed1999

Inferring perceptual saliency fields from viewpoint-dependent recognition data.

We present an algorithm for computing the relative perceptual saliencies of the features of a three-dimensional object using either goodness-of-view scores measured at several viewpoints or perceptual similarities among several object views. This technique addresses the inverse, ill-posed version of the direct problem of predicting goodness-of-view scores or viewpoint similarities when the object features are known. On the basis of a linear model for the direct problem, we solve the inverse problem using the method of regularization. The critical assumption we make to regularize the solution is that perceptual salience varies slowly on the surface of the object. The salient regions derived using this assumption empirically indicate what object structures are important in human three-dimensional object perception, a domain where theories typically have been based on somewhat ad hoc features.

Algorithms↗

PubMed1998

Representation of object similarity in human vision: psychophysics and a computational model.

We report results from perceptual judgment, delayed matching to sample and long-term memory recall experiments, which indicate that the human visual system can support metrically veridical representations of similarities among 3D objects. In all the experiments, animal-like computer-rendered stimuli formed regular planar configurations in a common 70-dimensional parameter space. These configurations were fully recovered by multidimensional scaling from proximity tables derived from the subject data. We show that such faithful representation of similarity is possible if shapes are encoded by their similarities to a number of reference (prototypical) shapes, as in the computational model that accompanies the psychophysical data.

Computer Simulation↗

PubMed1996

Faithful representation of similarities among three-dimensional shapes in human vision.

Efficient and reliable classification of visual stimuli requires that their representations reside a low-dimensional and, therefore, computationally manageable feature space. We investigated the ability of the human visual system to derive such representations from the sensory input-a highly nontrivial task, given the million or so dimensions of the visual signal at its entry point to the cortex. In a series of experiments, subjects were presented with sets of parametrically defined shapes; the points in the common high-dimensional parameter space corresponding to the individual shapes formed regular planar (two-dimensional) patterns such as a triangle, a square, etc. We then used multidimensional scaling to arrange the shapes in planar configurations, dictated by their experimentally determined perceived similarities. The resulting configurations closely resembled the original arrangements of the stimuli in the parameter space. This achievement of the human visual system was replicated by a computational model derived from a theory of object representation in the brain, according to which similarities between objects, and not the geometry of each object, need to be faithfully represented.

Brain↗

PubMed1994

Canonical views in object representation and recognition.

Human performance in the recognition of 3-D objects, as measured by response times and error rates, frequently depends on the orientation of the object with respect to the observer. We investigated the dependence of response time (RT) and error rate (ER) on stimulus orientation for a class of random wire-like objects. First, we found no evidence for universally valid canonical views: the best view according to one subject's data was often hardly recognized by other subjects. Second, a subject by subject analysis showed that the RT/ER scores were not linearly dependent on the shortest angular distance in 3D to the best view, as predicted by the mental rotation theories of recognition. Rather, the performance was significantly correlated with an image-plane feature by feature deformation distance between the presented view and the best (shortest-RT and lowest-ER) view. Our results suggest that measurement of image-plane similarity to a few (subject-specific) feature patterns is a better model than mental rotation for the mechanism used by the human visual system to recognize objects across changes in their 3-D orientation.

Computer Graphics↗