@conference {
	title = {Wavelet-Based Hierarchical Organization of Large Image Databases:~ ISAR and Face Recognition},
	author = {Baras, John S., Wolk, Sheldon I.},
	year = {1998},
	month = {04/1998},
	abstract = {We present a method for constructing efficient hierarchical organization of image databases for fast recognition and classification. The method combines a wavelet preprocessor with a Tree-Structured-Vector-Quantization for clustering. We show results of application of the method to ISAR data from ships and to face recognition based on photograph databases. In the ISAR case we show how the method constructs a multi-resolution aspect graph for each target.},
	address = {Orlando, Florida},
	booktitle = {SPIE 12th International Symposium on Aerospace, Defense Sensing, Simulation and Control},
	pages = {pp. 546-558}
}
@conference {
	title = {Wavelet Based Progressive Classification with Learning: Applications to Radar Signals},
	author = {Baras, John S., Wolk, Sheldon I.},
	year = {1995},
	month = {04/1995},
	abstract = {In this paper we investigate the problem of fast and accurate classification of naval targets from radar returns. The algorithms can be applied to both 1-D and 2-D data (i.e. high range resolution and imaging radar returns). We describe the structure of these algorithms and report experimental results on their performance with synthetic returns from ships.

We have successfully addressed the problem of reducing the target model representations with respect to viewpoint variations and other Sensor parametric variations. Our method can be viewed as a quantization of the space of sensing Operations. The resulting multiresolution aspect graph is a (relational) graph representation of this quantization. Aspect graphs of target radar returns are generated algorithmically. Since our off-line model/parameter tuning methods are based on general vector quantization, our methods extend naturally and efficiently to multi-sensor data: LADAR, TV, mmWave, SAR, etc.. We describe new results on the {\textquotedblleft}continuity{\textquotedblright} of the aspect graph, new properties and improvements of our algorithmic constructions.

Our basic classification algorithm utilizes a cascade of a wavelet preprocessor followed by a tree-structured clustering algorithm: a learning mode can be also added. We develop a high performance parallel progressive classification algorithm and report On its performance and Complexity. We show experiments illustrating that the parallel algorithm Outperforms a Compound version (which is the more intuitive choice) and that it has performance close to Bayes optimal classification (via comparison to Learning Vector Quantization). We Outline an analytical framework for establishing these results theoretically. We also discuss similar experiments from face recognition and medical image classification problems.},
	address = {Orlando, Florida},
	booktitle = {The SPIE 1995 International Symposium on Aerospace/ Defense Sensing and Dual-Use Photonics},
	pages = {pp. 339-350}
}
@conference {
	title = {Model Based Automatic Target Recognition from High Range Resolution Radar Returns},
	author = {Baras, John S., Wolk, Sheldon I.},
	year = {1994},
	month = {04/1994},
	abstract = {Economic descriptions of objects are essential for model-based ATR. We develop economic target descriptions based on high range resolution target returns, utilizing wavelet multiresolution representations and Tree Structured Vector Quantization, in its clustering mode. The algorithm automatically constructs the multi-scale aspect graph of the target, which is a most efficient model that guides the on-line ATR algorithm. This results in a progressive coding of the target model information and in an extremely efficient, hierarchical indexing of the stored target models. As a final outcome we obtain extremely fast recovery, search and matching during the on-line ATR operation. In addition, these scale space (or multiresolution) representations can be used to generate target fingerprints across a range of scales, unavailable heretofore, which aid substantially in target recognition even with occluded or spurious data.

We have Successfully addressed the problem of reducing the target model representations with respect to viewpoint variations and other sensor parametric variations. Our method can be viewed as a quantization of the space of sensing operations. The resulting aspect graph is a (relational) graph representation of this quantization. Aspect graphs of target radar returns are generated algorithmically. Since our off-line model/parameter tuning methods are based on general vector quantization, our methods extend naturally and efficiently to multi-sensor data: LADAR, TV, mmWave, SAR, etc..

We also investigate~the so-called new target insertion problem in a fielded ATR system, and the required fast re-programmability of the ATR system. We compare the performance and cost (both computational and hardware) of ATR algorithms based on the parallel use of single target aspect graphs vs ATR algorithms using the combined aspect graph for the group of targets under consideration. We show that efficient real-time ATR algorithms can be constructed using the aspect graph of each target in a parallel computation. The resulting architecture includes wavelet preprocessing with neural networks postprocessing. We use synthetic radar returns from ships as the experimental data to demonstrate the performance of the resulting ATR algorithm.},
	address = {Orlando, FL},
	booktitle = {The SPIE International Symposium on Intelligent Information Systems},
	pages = {pp. 57-66}
}
@conference {
	title = {Wavelet Based Progressive Classification of High Range Resolution Radar Returns},
	author = {Baras, John S., Wolk, Sheldon I.},
	year = {1994},
	month = {04/1994},
	abstract = {We investigate the problem of fast and accurate classification of high range resolution radar returns from ships. In addition, we investigate the problem of efficient organization of large databases of pulsed high resolution radar returns from multiple targets in order to economize memory requirements and minimize search time. We use synthetic radar returns from ships as the experimental data. We develop a novel algorithm for hierarchically organizing the database which utilizes a multiresolution wavelet representation working in synergy with a Tree Structured Vector Quantizer (TSVQ), utilized in its clustering mode. The tree structure is induced by the multiresolution decomposition of the radar returns. The TSVQ design algorithm is of the "greedy{\textquotedblright} type. We demonstrate that our algorithm automatically computes the aspect graph (i.e. the simultaneous representation of compressed pulses as functions of aspect and elevation) for a single target or for a group of targets. We also develop a novel optimization framework for the simultaneous design of the wavelet basis, the Tree-Structured Vector Quantizer and the Classification rule. We describe an efficient and promising implementation consisting of an adaptive Wavelet Transform - Tree Structured Vector Quantization with Learning. We show experimental results which indicate that the combined algorithm executes orders of magnitude faster data search time, with negligible performance degradation (as measured by rate-distortion curves).},
	address = {Orlando, FL},
	booktitle = {The SPIE International Symposium on Intelligent Information Systems},
	pages = {pp. 967-977}
}
@conference {
	title = {Efficient Organization of Large Ship Radar Databases Using Wavelets and Structured Vector Quantization},
	author = {Baras, John S., Wolk, Sheldon I.},
	year = {1993},
	month = {11/1993},
	abstract = {We investigate the problem of efficient representations of large databases of pulsed radar returns froTn naval vessels in order to economize memory and minimize search time. We use synthetic radar returns from ships as the experimental data. The results extend to real ISAR returns. We develop a novel algorithm for organizing the database, which utilizes a ~multiresolution wavelet representation working in synergy with a Tree Structured Vector Quantizer (TSWQ), utilized in its clustering mode. The tree Structure is induced by the multiresolution decomposition of the pulses. The TSVQ design algorithm is of the "greedy" type.~Our experiments todate indicate that the combined algorithm results in orders of magnitude faster data search time, with negligible performance degradation from the full search vector quantization. The combined algorithm provides an efficient indexing scheme (with respect to variations in aspect, elevation and pulse width) for radar data which can facilitate the development of ATR, surveillance and multi-sensor fusion systems.},
	address = {Pacific Grove, California},
	booktitle = {The 27th Annual Asilomar Conference on Signals, Systems and Computers},
	pages = {pp. 491-498}
}
