John L. Johnson

Bio: John L. Johnson is an academic researcher from United States Department of the Army. The author has contributed to research in topics: Artificial neural network & Image processing. The author has an hindex of 12, co-authored 54 publications receiving 1463 citations.

PCNN models and applications

Abstract: Pulse coupled neural network (PCNN) models are described. The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model. Applications and implementations of PCNNs are reviewed. Application based variations and simplifications are summarized. The PCNN image decomposition (factoring) model is described in detail.

Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images

Abstract: The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

Observation of periodic waves in a pulse-coupled neural network.

Abstract: A pulse-coupled neural network was implemented, for the first time to our knowledge, in a hybrid electro-optical laboratory demonstration system. Dynamic coherent traveling-wave patterns were observed that repeated their spatial patterns at each locality with a period that depended on the local input pattern and strength. Coherence and periodicity were maintained far beyond the physical limits of the linking receptive fields, suggesting a new mechanism for information transmission in a network with limited local connectivity. With no linking, the output became chaotic because the relative phases increased linearly in time.

Pulse coupled neural networks for image processing

Abstract: Studies of cat's and monkey's visual cortex has led to the development of pulse coupled neurons which are significantly different from the conventional artificial neurons Pulse coupled neural networks (PCNN) are modeled to capture the essence of recent understanding of image interpretation process in biological neural systems It is shown that pulse coupled neural networks are capable of image smoothing, image segmentation and feature extraction

Pulse-coupled neural networks

Abstract: A pulse-coupled neural network using the Eckhorn linking field coupling [1] is shown to contain invariant spatial information in the phase structure of the output pulse trains. The time domain signals axe directly related to the intensity histogram of an input spatial distribution and have complex phase factors that specify the spatial location of the histogram elements. Two time scales are identified. On the fast time scale the linking produces dynamic, quasi-periodic, fringe-like traveling waves [2] that can carry information beyond the physical limits of the receptive fields. These waves contain the morphological connectivity structure of image elements. The slow time scale is set by the pulse generator, and on that scale the image is segmented into multineuron time-synchronous groups. These groups act as giant neurons, firing together, and by the same linking field mechanism as for the linking waves can form quasi-periodic pulse structures whose relative phases encode the location of the groups with respect to one another. These time signals are a unique, object-specific, and roughly invariant time signature for their corresponding input spatial image or distribution [3]. The details of the model are discussed, giving the basic Eckhorn linking field, extensions, generation of time series in the limit of very weak linking, invariances from the symmetries of the receptive fields, time scales, waves, and signatures. Multirule logical systems are shown to exist on single neurons. Adaptation is discussed. The pulse-coupled nets are compatible with standard nonpulsed adaptive nets rather than competitive with them in the sense that any learning law can be used. Their temporal nature results in adaptive associations in time as well as over space, and they are similar to the time-sequence learning models of Reiss and Taylor [4]. Hardware implementations, optical and electronic, are reviewed. Segmentation, object identification, and location methods are discussed and current results given. The conjugate basic problem of transforming a time signal into a spatial distribution, comparable in importance to the transformation of a spatial distribution into a time signal, is discussed. It maps the invariant time signature into a phase versus frequency spatial distribution and is the spatial representation of the complex histogram. A method of generating this map is discussed. Image pattern recognition using this network is shown to have the power of syntactical pattern recognition and the simplicity of statistical pattern recognition.

Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches

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License Plate Recognition From Still Images and Video Sequences: A Survey

Abstract: License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them Issues such as processing time, computational power, and recognition rate are also addressed, when available Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment

PCNN models and applications

Abstract: Pulse coupled neural network (PCNN) models are described. The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model. Applications and implementations of PCNNs are reviewed. Application based variations and simplifications are summarized. The PCNN image decomposition (factoring) model is described in detail.