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- Alex T. Pang, Craig M. Wittenbrink, Suresh K. Lodha
- The Visual Computer
- 1997

Visualized data often have dubious origins and quality Di erent forms of uncertainty and er rors are also introduced as the data are derived transformed interpolated and nally rendered In the absence of integrated presentation of data and uncertainty the analysis of the visualization is incomplete at best and often leads to inaccurate or incorrect… (More)

- Craig M. Wittenbrink, Alex T. Pang, Suresh K. Lodha
- IEEE Trans. Vis. Comput. Graph.
- 1996

Environmental data have inherent uncertainty which is often ignored in visualization. Meteorological stations and doppler radars, including their time series averages, have a wealth of uncertainty information that traditional vector visualization methods such as meteorological wind barbs and arrow glyphs simply ignore. We have developed a new vector glyph… (More)

- Srikumar Ramalingam, Suresh K. Lodha, Peter F. Sturm
- Computer Vision and Image Understanding
- 2006

We introduce a generic structure-from-motion approach based on a previously introduced, highly general imaging model, where cameras are modeled as possibly unconstrained sets of projection rays. This allows to describe most existing camera types including pinhole cameras, sensors with radial or more general distortions, catadioptric cameras (central or… (More)

- Srikumar Ramalingam, Peter F. Sturm, Suresh K. Lodha
- 2005 IEEE Computer Society Conference on Computer…
- 2005

We consider the problem of calibrating a highly generic imaging model, that consists of a non-parametric association of a projection ray in 3D to every pixel in an image. Previous calibration approaches for this model do not seem to be directly applicable for cameras with large fields of view and non-central cameras. In this paper, we describe a complete… (More)

- Suresh K. Lodha, Richard Franke
- Scientific Visualization
- 1997

- Amin P. Charaniya, Roberto Manduchi, Suresh K. Lodha
- 2004 Conference on Computer Vision and Pattern…
- 2004

In this work, we classify 3D aerial LiDAR height data into roads, grass, buildings, and trees using a supervised parametric classification algorithm. Since the terrain is highly undulating, we subtract the terrain elevations using digital elevation models (DEMs, easily available from the United States Geological Survey (USGS)) to obtain the height of… (More)

- Suresh K. Lodha, Alex T. Pang, Robert E. Sheehan, Craig M. Wittenbrink
- IEEE Visualization
- 1996

Uncertainty or errors are introduced in fluid flow data as the data is acquired, transformed and rendered. Although researchers are aware of these uncertainties, little has been done to incorporate them in the existing visualization systems for fluid flow. In the absence of integrated presentation of data and its associated uncertainty, the analysis of the… (More)

We consider the self-calibration problem for the generic imaging model that assigns projection rays to pixels without a parametric mapping. In this paper, we consider the central variant of this model, which encompasses all camera models with a single effective viewpoint. Self-calibration refers to calibrating a camera’s projection rays, purely from matches… (More)

- Suresh K. Lodha, Jose C. Renteria, Krishna M. Roskin
- IEEE Visualization
- 2000

We present an algorithm for compressing 2D vector fields that preserves topology. Our approach is to simplify the given data set using constrained clustering. We employ different types of global and local error metrics including the earth mover’s distance metric to measure the degradation in topology as well as weighted magnitude and angular errors. As a… (More)

- Suresh K. Lodha, Darren N. Fitzpatrick, David P. Helmbold
- Sixth International Conference on 3-D Digital…
- 2007

We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass… (More)