1/9/2024 0 Comments Neural photo finishingRecent work with fruit flies has shown that they are in fact capable of performing object detection based on a variety of cues. Frye, personal communication), I assume for purposes of this study that this represents an attentional effect, and that whatever computational machinery allows insects to detect discrete objects is operative whether those objects are stationary or moving in the animals' field of view. While tracking responses to moving objects are generally more robust than to stationary ones (M. Interestingly, however, it has also long been known that flies can react to stationary, flickering objects ( Pick, 1974)-and it is also the case that motion-blind flies can still react to moving visual figures ( Bahl et al., 2013). It has often been assumed that the visual motion itself provides the primary cue that is used to discriminate object from background ( Götz, 1968 Reichardt and Poggio, 1979 Reichardt et al., 1983, 1989 Egelhaaf, 1985). Researchers have studied behavioral reactions to moving visual objects by flies, during both free and constrained flight, in experiments dating back decades. While anyone who has tried to swat a fly can attest to its ability to detect and avoid a looming hand, a range of behavioral reactions to visual objects can be found in the insects-which may vary from attractive (as occurs during pursuit of prey or mates, or approach toward objects on which a flying insect intends to land) to avoidant (as with predators or other objects that might collide with the animal). It is clear to even a casual observer that many types of insects can detect the presence of discrete objects appearing in their visual fields. The resources required (numbers of neurons and visual signals) are realistic relative to those available in the insect second optic ganglion, where the bulk of such processing would be likely to take place. The model can discriminate the presence of edges, stationary or moving, at rates far higher than chance. Input data are derived from natural imagery and feature both static and moving edges between regions with moving texture, flickering texture, and static patterns in all possible combinations. absence of an edge based on the array output signals. It is followed by an artificial neural network trained to discriminate the presence vs. This is replicated for a number of photoreceptors in a small linear array. The first is an early vision module inspired by insect visual processing, which implements adaptive photoreception, ON and OFF channels with transient and sustained characteristics, and delayed and undelayed signal paths. Evidence suggests that edge detection is an integral part of this capability, and this study examines the ability of a bio-inspired processing model to detect the presence of boundaries between two regions of a one-dimensional visual field, based on general differences in image dynamics. This includes but is not limited to relative motion. Insects can detect the presence of discrete objects in their visual fields based on a range of differences in spatiotemporal characteristics between the images of object and background. Computational Science Research Center, San Diego State University, San Diego, CA, United States.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |