Dr. Walter H. Delashmit Profile

Dr. Walter H. Delashmit

Deceased at WHD Consulting

SPIE Involvement:

Author

Area of Expertise:

Neural Networks , Automatic Target Recognition , Image Processing , Signal Processing , Correlation Analyses , Program/Project Management

Websites:

Company Website

Dr. Walter H. Delashmit retired from Lockheed Martin Missiles and Fire Control on 1 January 2007 after 25 years at Lockheed and 39 years in the aerospace industry. He is presently doing independent consulting. In addition from August 2007 until June 2009, Walter was an Adjunct Faculty Member at the University of North Texas teaching Advanced Electrical Engineering Courses.
Walter has a BSEE from Christian Brothers University (1966) graduating cum laude, a MSEE from with a minor in mathematics from the University of Tennessee (1968) and a Ph.D. in electrical engineering from the University of Texas at Arlington (2003).
Walter worked at Lockheed Martin Missiles and Fire Control (1982-2007) developing "smart" missile systems, at the Pennsylvania State University Applied Research Laboratory (1976-1982) developing advanced torpedo systems, at Martin Marietta Aerospace (1972-1976) developing advanced cruise missile technology and at TRW Systems (1969-1972) working on the Apollo and Skylab Programs, including Apollo 13.
Walter received a copy of the Presidential Medal of Freedom from NASA for his work on Apollo 13 and received the Lockheed Martin Missiles and Fire Control Presidents Performance Award for his work on developing and implementing Improved Software Processes. He has also received many other awards.
Walter is a Life Senior Member (LSM) of the Institute of Electrical and Electronics Engineers (IEEE), a member of Eta Kappa Nu (Electrical Engineering Honor Society) and Tau Beta Pi (Engineering Honor Society). Walter has 40 peer reviewed technical publications in advanced Technical Journals and Conferences. He also regularly reviews articles for consideration for publication in advanced technical journals.
Walter has an extensive background in Program/Project Management, Process Improvement, Neural Networks, Automatic Target Recognition, Image Processing, Signal Processing and Correlation Analysis.

Proceedings Article | 7 May 2009 Paper

Infrared-based object tracking

Jon Gervais, Austin Youngblood, Walter Delashmit

Proceedings Volume 7298, 72983V (2009) https://doi.org/10.1117/12.818267

KEYWORDS: Receivers, Signal detection, Servomechanisms, Light emitting diodes, Field programmable gate arrays, Microcontrollers, Control systems, Infrared search and track, Transmitters, Infrared radiation

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Proceedings Article | 2 May 2009 Paper

Photographic-based target models for LADAR applications

James Jack, Walter Delashmit

Proceedings Volume 7323, 73230D (2009) https://doi.org/10.1117/12.818303

KEYWORDS: 3D modeling, Photography, LIDAR, 3D acquisition, Solid modeling, Data modeling, 3D image processing, Automatic target recognition, Calibration, Model-based design

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Proceedings Article | 19 May 2006 Paper

Target detection in LADAR range images using shadow analysis

Govindaraj Kuntimad, Walter Delashmit

Proceedings Volume 6214, 621405 (2006) https://doi.org/10.1117/12.663767

KEYWORDS: Target detection, LIDAR, Sensors, Target recognition, Image analysis, Automatic target recognition, Solids, Detection and tracking algorithms, Missiles, Feature extraction

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Proceedings Article | 15 November 1993 Paper

General common database structure for generating synthetic imagery

Walter Delashmit, Phillip Thompson

Proceedings Volume 1938, (1993) https://doi.org/10.1117/12.161553

KEYWORDS: Databases

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The development of reliable techniques for mission planning and mission rehearsal require the availability of a large number of images from a variety of sensors so that the system operators and pilots can adequately plan and rehearse the missions under realistic scenarios. Conversely, the development of databases for evaluating the performance of algorithms developed to perform automatic target recognition or terminal homing require a significant number of images, but fewer and covering less area than the imagery required for mission training and mission rehearsal. However, the target recognition and terminal homing images must be of significantly higher quality to avoid introducing artifacts into the imagery which would make the algorithms perform better than would be the case against real-world imagery. This paper will define a general conunon database (COB) structure which can be used to generate imagery for a variety of sensors. The sensors for which general structures will be defined include thermal infrared (TIR), synthetic aperture radar (SAR), millimeter wave (MMW) radar and laser radar (LADAR). These general structures have been used to convert color infrared (CIR) imagery to TIR and SAR for a mission rehearsal application. This structure development is initiated by defining the three main considerations of application area, speed of processing and image quality. These three considerations are highly interrelated and impose constraints on the types of algorithm architectures which are feasible for developing the required database. The top-level structure for the COB is composed of both dynamic and stationary files. The stationary files are fixed for each scenario and the dynamic files may change under the control of the event control module. The main components of the dynamic files, stationary files and event control module will be defined with specific examples presented for each of these modules. The COB structure is developed in a modular nature so that additional information and new sensors concepts can be easily added as system and mission requirements change. In addition, the issues of validation of the CDB will be addressed as it applies to the expected usage of the COB.

Proceedings Article | 27 August 1993 Paper

Feature-classification techniques applicable to common database development for mission rehearsal and training

Phillip Thompson, Walter Delashmit

Proceedings Volume 1961, (1993) https://doi.org/10.1117/12.150958

KEYWORDS: Feature extraction, Image classification, Roads, Image processing, Image analysis, Image segmentation, Visual information processing, Hough transforms, Databases, Infrared imaging

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To accurately model imagery for converting a single image source to generate imagery for another sensor, it is necessary to develop feature classification techniques that can define significant features in the imagery so that the scattering properties of the incident radiation can be used as a technique to model the desired bands of the electromagnetic spectrum. This paper wifi be concerned with the extraction of features where either color infrared (CIR) or electro-optical (EO) imagery are available as the baseline source materials. The feature extraction process will be initiated using first order techniques for the iiiital classification. This initial classification will be followed by using higher order, more computationally intensive methods. Since higher order methods are usually specific to certain features, a battery of higher order methods will be required to classify the features in an entire scene. These various classified features will be linked using techniques of image analysis. These techniques have been used to generate a sequence of images, where CIR imagery was converted to thermal infrared (TIR) and synthetic aperture radar (SAR), for mission simulation and planning. The input images are processed initially to define regions based on some measure of homogeneity within regions of the image. This processing could be based on texture or measures of signature content within different bands of a niultispectral image. Both automatic and manual classification techniques, including synergistic coinbiiiatioiis, are applicable for this stage of processing. The precise form of the processing should also be guided by whether the regions being processed consist of nianmade or natural regions. This information is very useful since manmade structures usually consist of regularly shaped, rigid regions, whereas natural objects are less well defined and usually exhibit more randomness. Thus, for manmade object feature extraction, it is appropriate to use techniques for extracting lines, regions, ellipses/circles, or other regular-shaped regions with some regular periodicity occurring within selected, larger subregions. On the other hand, naturally occurring features could be more accurately extracted using texture methods or metrics defined based on the energy content of different bands of the electromagnetic spectrum. The feature extraction techniques discussed in this paper are hierarchical in nature to reduce the computational requirements imposed on developing the large set of images required for realistic mission training. The first-order classification is most efficiently implemented by using the three bands of a CIR image which can be transformed from red, green, and blue to intensity, hue and saturation. This transformation has the twofold effect of iuakiiig the process independent of the total received intensity, while at the same time reducing the three parameter lookup table to a two parameter lookup table. The details and accuracy of these techniques, which have been implemented for mission simulation and planning, will be presented in the paper. Combining these techniques with texture methods, which are based on regularity measures of regions, leads to a refined classification. The final step in the procedure is to perform image analysis as a refinement procedure for the classification. Based on the scenes being analyzed and some known a priori scene content, detailed feature extraction procedures can be developed for specific features. Analysis of these features allows decision rules to be constructed based on the features and their interrelationships. These decision rules allow the system developer to encode the proper constraints into the algorithmic processes to determine when the feature actually exists and its limiting boundaries. As more practice and expertise is developed iii the image conversion process, these image analysis techniques will become more and more automated.

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