Mr. Timothy S. Khuon Profile

Timothy S. Khuon

Research Staff at National Geospatial-Intelligence Agency

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Publications (5)

SPIE Journal Paper | 19 July 2016 Open Access

Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

Robert Rand, Timothy Khuon, Eric Truslow

OE, Vol. 55, Issue 07, 073101, (July 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.7.073101

KEYWORDS: Neural networks, Image segmentation, Sensors, Scanning electron microscopy, LIDAR, Expectation maximization algorithms, Image fusion, Clouds, Image classification, Data fusion

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Proceedings Article | 20 June 2014 Paper

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

Timothy Khuon, Charles Kershner, Arnel Alverio, Robert Rand, Enrico Mattei

Proceedings Volume 9091, 90911F (2014) https://doi.org/10.1117/12.2064801

KEYWORDS: Clouds, Neural networks, Sensors, Feature extraction, LIDAR, Detection and tracking algorithms, 3D acquisition, Automatic target recognition, Super resolution, Neurons

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Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Proceedings Article | 10 May 2012 Paper

Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

Timothy Khuon, Robert Rand

Proceedings Volume 8407, 840702 (2012) https://doi.org/10.1117/12.918514

KEYWORDS: Neural networks, LIDAR, Sensors, Sensor fusion, Scanning electron microscopy, Expectation maximization algorithms, Data fusion, Image fusion, Image classification, Clouds

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Proceedings Article | 16 December 1992 Paper

Training-set-based performance measures for data-adaptive decisioning systems

Robert Levine, Timothy Khuon

Proceedings Volume 1766, (1992) https://doi.org/10.1117/12.130857

KEYWORDS: Stochastic processes, Neural networks, Neurons, Image processing, Signal processing, Binary data, Polonium, Brain mapping, Palladium, Systems modeling

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Proceedings Article | 30 April 1992 Paper

Neural networks for distributed sensor data fusion: the Firefly experiment

Timothy Khuon, Robert Levine

Proceedings Volume 1611, (1992) https://doi.org/10.1117/12.57912

KEYWORDS: Sensors, Sensor fusion, Image fusion, Neural networks, Neurons, Radar, Infrared imaging, Data fusion, Doppler effect, X band

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