Biomedical subjects

Cornelia Fermüller

Publications and source records attributed to Cornelia Fermüller.

4 recordsLinked to original sources

PubMed2006

Depth estimation using the compound eye of dipteran flies.

In the neural superposition eye of a dipteran fly every ommatidium has eight photoreceptors, each associated with a rhabdomere, two central and six peripheral, which altogether result in seven functional light guides. Groups of eight rhabdomeres in neighboring ommatidia have largely overlapping fields of view. Based on the hypothesis that the light signals collected by these rhabdomeres can be used individually, we investigated the feasibility of estimating 3D scene information. According to Pick (Biol Cybern 26:215-224, 1977) the visual axes of these rhabdomeres are not parallel, but converge to a point 3-6 mm in front of the cornea. Such a structure theoretically could estimate depth in a very simple way by assuming that locally the image intensity is well approximated by a linear function of the spatial coordinates. Using the measurements of Pick (Biol Cybern 26:215-224, 1977) we performed simulation experiments to find whether this is practically possible. Our results indicate that depth estimation at small distances (up to about 1.5-2 cm) is reasonably accurate. This would allow the insect to obtain at least an ordinal spatial layout of its operational space when walking.

Animals↗

PubMed2006

Noise causes slant underestimation in stereo and motion.

This paper discusses a problem, which is inherent in the estimation of 3D shape (surface normals) from multiple views. Noise in the image signal causes bias, which may result in substantial errors in the parameter estimation. The bias predicts the underestimation of slant found in psychophysical and computational experiments. Specifically, we analyze the estimation of 3D shape from motion and stereo using orientation disparity. For the case of stereo, we show that bias predicts the anisotropy in the perception of horizontal and vertical slant. For the case of 3D motion we demonstrate the bias by means of a new illusory display. Finally, we discuss statistically optimal strategies for the problem and suggest possible avenues for visual systems to deal with the bias.

Form Perception↗

PubMed2005

Motion segmentation using occlusions.

We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).

Algorithms↗

PubMed2004

Uncertainty in visual processes predicts geometrical optical illusions.

It is proposed in this paper that many geometrical optical illusions, as well as illusory patterns due to motion signals in line drawings, are due to the statistics of visual computations. The interpretation of image patterns is preceded by a step where image features such as lines, intersections of lines, or local image movement must be derived. However, there are many sources of noise or uncertainty in the formation and processing of images, and they cause problems in the estimation of these features; in particular, they cause bias. As a result, the locations of features are perceived erroneously and the appearance of the patterns is altered. The bias occurs with any visual processing of line features; under average conditions it is not large enough to be noticeable, but illusory patterns are such that the bias is highly pronounced. Thus, the broader message of this paper is that there is a general uncertainty principle which governs the workings of vision systems, and optical illusions are an artifact of this principle.

Humans↗