Mr. Karthik Mahesh Varadarajan Profile

Karthik Mahesh Varadarajan

at Technische Univ Wien

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Publications (4)

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PROCEEDINGS ARTICLE | May 8, 2010

Cognitive object recognition system (CORS)

Proc. SPIE. 7692, Unmanned Systems Technology XII

KEYWORDS: 3D image enhancement, Detection and tracking algorithms, Image processing, Robotics, Navigation systems, Object recognition, Image enhancement, Algorithm development, Robotic systems, 3D image processing

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We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

PROCEEDINGS ARTICLE | April 20, 2010

An incremental knowledge assimilation system (IKAS) for mine detection

Proc. SPIE. 7678, Ocean Sensing and Monitoring II

KEYWORDS: Target detection, Detection and tracking algorithms, Image processing, Mining, Image filtering, Image classification, Environmental sensing, Land mines, Submerged target detection, Classification systems

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In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

PROCEEDINGS ARTICLE | May 5, 2009

Incremental learning in automatic target recognition

Proc. SPIE. 7335, Automatic Target Recognition XIX

KEYWORDS: Gold, Infrared imaging, Databases, Chromium, Surveillance, Reconstruction algorithms, Target recognition, Automatic target recognition, Missiles, Binary data

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ATR in two dimensional images is valuable for precision guidance, battlefield awareness and surveillance applications. Current ATR methods are largely data-driven and as a result, their recognition accuracy relies on the quality of training dataset. These methods fail to reliably recognize new target types and targets in new backgrounds and/or atmospheric conditions. Thus, there is a need for an ATR solution that can constantly update itself with information from new data samples (samples may belong to existing classes, background clutter or new target classes). In the paper, this problem is addressed in two steps: 1) Incremental learning with Fully Adaptive Approximate Nearest Neighbor Classifier (FAAN) - A novel data structure is designed to allow incremental learning in approximate nearest neighbor classifier. New data samples are assimilated at reduced complexity and memory without retraining on existing data samples, 2) Data Categorization using Data Effectiveness Measure (DEM) - DEM of a data sample is a degree to which each sample belongs to a local cluster of samples. During incremental learning, DEM is used to filter out redundant samples and outliers, thereby reducing computational complexity and avoiding data imbalance issues. The performance of FAAN is compared with proprietary Bagging-based Incremental Decision Tree (ABAFOR) implementation. Tests performed on Army ATR database with over 37,000 samples shows that while classification accuracy of FAAN is comparable to ABAFOR (both close to 95%), the process of incremental learning is significantly quicker.

PROCEEDINGS ARTICLE | April 28, 2009

Adaptive pattern-based image compression for ultra-low bandwidth weapon seeker image communication

Proc. SPIE. 7341, Visual Information Processing XVIII

KEYWORDS: Infrared imaging, Weapons, Image compression, Visual process modeling, Data modeling, Image segmentation, Computer programming, Image quality, Image transmission, Missiles

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The effectiveness of autonomous munitions systems can be enhanced by transmitting target images to a man-in-the-loop (MITL) as the system deploys. Based on the transmitted images, the MITL could change target priorities or conduct damage assessment in real-time. One impediment to this enhancement realization is the limited bandwidth of the system data-link. In this paper, an innovative pattern-based image compression technology is presented for enabling efficient image transmission over the ultra-low bandwidth system data link, while preserving sufficient details in the decompressed images for the MITL to perform the required assessments. Based on a pattern-driven image model, our technology exploits the structural discontinuities in the image by extracting and prioritizing edge segments with their geometric and intensity profiles. Contingent on the bit budget, only the most salient segments are encoded and transmitted, therefore achieving scalable bit-streams. Simulation results corroborate the technology efficiency and establish its subjective quality superiority over JPEG/JPEG2000 as well as feasibility for real-time implementation. Successful technology demonstrations were conducted using images from surrogate seekers in an aircraft and from a captive-carry test-bed system. The developed technology has potential applications in a broad range of network-enabled weapon systems.

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