We examine the effects of wavelet compression on target detection algorithms when the targets are single-pixel point
sources modulated by the point-spread of an optical system. The experimental data combines frames collected from a
multispectral sensor with simulated targets based on an Airy function. We studied several different types of wavelets
and found that the Daubechies 2 wavelet resulted in the best overall target detection and fewest false alarms with
increasing compression. Results show that wavelet compression may decrease pixel intensities, increase target signal-to-noise
ratio, and reduce false detections. Consequently it may negatively affect target detection unless the detector is
designed to take the decreased intensity into account.
In recent years, several researchers have constructed novel neural network models based on lattice algebra. Because of computational similarities to operations in the system of image morphology, these models are often called morphological neural networks. One neural model that has been successfully applied to many pattern recognition problems is the single-layer morphological perceptron with dendritic structure (SLMP). In this model, the fundamental computations are performed at dendrites connected to the body of a single neuron. Current training algorithms for the SLMP work by enclosing the target patterns in a set of hyperboxes orthogonal to the axes of the data space. This work introduces an alternate model of the SLMP, dubbed the synaptic morphological perceptron (SMP). In this model, each dendrite has one or more synapses that receive connections from inputs. The SMP can learn any region of space determined by an arbitrary configuration of hyperplanes, and is not restricted to forming hyperboxes during training. Thus, it represents a more general form of the morphological perceptron than previous architectures.
Conference Committee Involvement (4)
Mathematics of Data/Image Pattern Coding, Compression, and Encryption with Applications XIV
21 August 2011 | San Diego, California, United States
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption, with Applications XIII
3 August 2010 | San Diego, California, United States
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI
12 August 2008 | San Diego, California, United States
Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications
26 August 2007 | San Diego, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.