title: Applications of neural networks in human shape visual perception.
abstract: Advances in optical and electronic technology can immensely reduce noise in images and greatly enhance human visual recognition. However, it is still difficult for human eyes to identify low-resolution thermal images, due to the limits imposed by psychological and physiological factors. In addition, changes in monitor brightness and lens resolution may also interfere with visual recognition abilities. To overcome these limitations, we devised a suitable and effective recognition method which may help the military in revising the shape parameters of long-range targets. The modulation transfer function was used as a basis to extend the visual characteristics of the human visual model and a new model was produced through the incorporation of new shape parameters. The new human visual model was next used in combination with a backpropagation neural network for better recognition of low-resolution thermal images. The new model was then tested in experiments and the results showed that the accuracy rate of recognition steadily rose by over 95%.
authors.forename: Bo-Wen
authors.initials: BW
authors.lastname: Wu
authors.forename: Yi-Chin
authors.initials: YC
authors.lastname: Fang
authors.forename: David Pei-Cheng
authors.initials: DP
authors.lastname: Lin
mesh_terms: D003198:Computer Simulation; D005556:Form Perception; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D012160:Retina / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception