Dr. Ashish Karmacharya Profile

Dr. Ashish Karmacharya

at i3mainz

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Proceedings Article | 21 June 2015 Paper

Knowledge guided object detection and identification in 3D point clouds

A. Karmacharya, F. Boochs, B. Tietz

Proceedings Volume 9528, 952804 (2015) https://doi.org/10.1117/12.2184801

KEYWORDS: Clouds, Data processing, Data modeling, Detection and tracking algorithms, Data acquisition, Laser scanners, Algorithm development, Photogrammetry, Sensors, Video

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Modern instruments like laser scanner and 3D cameras or image based techniques like structure from motion produce huge point clouds as base for further object analysis. This has considerably changed the way of data compilation away from selective manually guided processes towards automatic and computer supported strategies. However it’s still a long way to achieve the quality and robustness of manual processes as data sets are mostly very complex. Looking at existing strategies 3D data processing for object detections and reconstruction rely heavily on either data driven or model driven approaches. These approaches come with their limitation on depending highly on the nature of data and inability to handle any deviation. Furthermore, the lack of capabilities to integrate other data or information in between the processing steps further exposes their limitations. This restricts the approaches to be executed with strict predefined strategy and does not allow deviations when and if new unexpected situations arise. We propose a solution that induces intelligence in the processing activities through the usage of semantics. The solution binds the objects along with other related knowledge domains to the numerical processing to facilitate the detection of geometries and then uses experts’ inference rules to annotate them. The solution was tested within the prototypical application of the research project “Wissensbasierte Detektion von Objekten in Punktwolken für Anwendungen im Ingenieurbereich (WiDOP)”. The flexibility of the solution is demonstrated through two entirely different USE Case scenarios: Deutsche Bahn (German Railway System) for the outdoor scenarios and Fraport (Frankfort Airport) for the indoor scenarios. Apart from the difference in their environments, they provide different conditions, which the solution needs to consider. While locations of the objects in Fraport were previously known, that of DB were not known at the beginning.

Proceedings Article | 23 May 2013 Paper

Automatic object detection in point clouds based on knowledge guided algorithms

Hung Truong, Ashish Karmacharya, Waldemar Mordwinzew, Frank Boochs, Celeste Chudyk, Adlane Habed, Yvon Voisin

Proceedings Volume 8791, 87910B (2013) https://doi.org/10.1117/12.2019468

KEYWORDS: Clouds, Detection and tracking algorithms, 3D modeling, Data modeling, Robots, Robotics, Cognitive modeling, Object recognition, Neodymium, Ions

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The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strategies. The goal of the present work is to select and guide algorithms following adaptive and intelligent manners for detecting objects in point clouds. Results show that our approach succeeded in identifying the objects of interest while using various data types.

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