We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and noisy three-band color image example.
Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex--the PCNN or Pulse Coupled Neural Network--performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the 3D PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting 2D (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter- term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single 2D pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.
Collective self-organization effects and chaos are commonly observed in optics. We describe examples in a particular kind of nonlinear optical material: photorefractive crystals. In particular, we show different effects that arise when photorefractive crystals are illuminated by one laser beam, two laser beams, and three laser beams.
In the many years that pattern recognition has been of interest, there have ben many clever advances. One recent advance is the pulse-coupled neural network (PCNN). Due to recent developments in PCNNs, it is becoming increasingly possible to recognize images in space regardless of scale, rotation, translation. A continuation of this has been investigated which will allow images to be recognized audibly. In this paper, a general method for converting 2D spatial patterns into pattern-specific sound patterns will be discussed, along with some background information on PCNNs, and projections for his image-to-sound conversion.
The features and operation of an electro-optically switched binary optical time delay system are discussed. THe system based on polarization switching using the low cost ferro- electric liquid crystal and polarizing beam splitters provides compactness, low complexity, low insertion loss and arbitrary time delay. We present the design, component selection, fabrication, testing, and evaluation of a prototype.
Solid Optics (SO) is 3D optics without air spaces. In some respects it is a 3D version of Integrated Optics (IO), but it shares many features with Conventional Optics (CO) that IO cannot share. After a general comparison of SO, IO, and CO; we note that for many purposes SO is the best choice. We then discuss general approaches, geometries, and figures of merit for SO systems. The final main topic is a review of the two main types of SO system: linear and right-angle.
The design and demonstration of a quantum optical genetic algorithms computer is described. We show that by avoiding the tedious computations of conventional genetic algorithms, time and energy could be saved. The role of quantum indeterminacy as a major component of the operation of this porcessor is emphasized.
Double exposure holograms taken at different wavelengths can be replayed at one wavelength. This is usually called `two wavelength holography.'' With photorefractive holography, the two wavelengths can directly interfere. We explore this phenomenon here
The most plausible possible uses of nonlinear optics as the bases for interconnections among complex optical modules are evaluated, with a view to such applications as neural networks that entail large numbers of interconnections and numerous stages. Optical interconnection allows such a system to be composed of many modules as well as to incorporate switching- and amplification-function optical nonlinearities. While it is possible to achieve a pixel-by-pixel, diffraction-limited flat-field relay with nonlinearity, where the interconnect allows for cascadability, the wave-particle duality is destroyed between stages.