Multirate and event driven Kalman filters for helicopter passive ranging

Sridhar, Banavar; Smith, P. N.; Suorsa, Raymond E.; Hussien, Bassam

doi:10.1109/cca.1992.269745

Cited by 8 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future we propose to compute the disparity using the points where a normal from the image origin intersects the given line. These measured points can then be used in a Kalman Filter based algorithm such as [13,14], to estimate range to that particular feature point.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 4 shows two line segments that might be considered for linking. The decision to link the two segments is based on the following inequalities: d1+d4-2d3>O (14) d2>d1-i-d4-2d3 (15) The first condition in eq. 14 insures that the lines are not overlapped, overlapped lines will not be linked since, most likely, they are not part of the same edge.…”

Section: Line Linkingmentioning

confidence: 99%

See 1 more Smart Citation

<title>Robust line extraction and matching algorithm</title>

Hussien

Sridhar

1993

SPIE Proceedings

View full text Add to dashboard Cite

This paper presents an algorithm for extracting straight lines from intensity images and describes a line matching algorithm for solving the line correspondence problem. The line extraction process begins by detecting edges in the intensity image. Next, line support regions are formed where image points (pixels) have similar gradient orientation. A line fitting algorithm is then used to fit a line to the points in the line support region based on a least means square fitting algorithm. Finally, line segments are linked together to form the final lines by using an adaptive line linking method, this results in a much stronger lines and a smaller set of lines to be considered. Once the lines are detected in a sequence of images, a line matching algorithm is used to match lines in one image to the lines in the other image. The images are either from a motion or stereo sequence. The matched lines may then be used with the sensor position and orientation data to estimate range to objects corresponding to the lines. We present results based on applying the line extraction and line matching algorithms to a synthetic image and an outdoor scene captured by a camera on a helicopter.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Line Linkingmentioning

confidence: 99%

<title>Robust line extraction and matching algorithm</title>

Hussien

Sridhar

1993

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…As an example, the raw GHI obtained on June 4, 2011, is shown in Figure 1.14(b). The GHI recorded every second is normalized with respect to the clear-sky GHI obtained from the NSRDB database at a nearby location [41]. The maximum spatial separation among sensors is 0.7 mile, resulting in the minimum spatial correlation coefficient of 0.5.…”

Section: Subtask 121: Low-dimensional Visualizationmentioning

confidence: 99%

Opportunistic Hybrid Communications Systems for Distributed PV Coordination

Zhang

Alam

Florita

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In feature based methods (Sridhar B. et al, 1993), significant features are detected from the images and matching between features are made from frame to frame using normalized correlation. But since only features are used, the choice of the feature set becomes very critical for the success of the method.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Hazard Detection

Luk¹,

Zaveri²,

Hammerstrom³

et al. 2006

Intelligent Engineering Systems Through Artificial Neural Networks, Volume 16

View full text Add to dashboard Cite

Advances in sensor and GPS technologies make possible better guidance tools for vehicle/aerial navigation. In the work reported here we look at the detection of hazards, such as aircraft or other objects, on or near the target runway. Our system consists of two modules: 1) regions of interest (ROI) detection, and 2) hazard recognition. One of the harder problems in object recognition is to segment the target object from a cluttered background. In this system we use a "poor man's" segmentation, by taking advantage of the fact that we have an approximate reference that we can differentiate from. The regions of interest are defined as significant differences between the input image and the reference image. Since this differencing can be complex due to the fact that the images may not be precisely registered, we employed a novel histogram method which is reasonably invariant to spatial transformations for ROI detection.

show abstract

Multirate and event driven Kalman filters for helicopter passive ranging

Cited by 8 publications

References 6 publications

<title>Robust line extraction and matching algorithm</title>

<title>Robust line extraction and matching algorithm</title>

Opportunistic Hybrid Communications Systems for Distributed PV Coordination

Vision-Based Hazard Detection

Contact Info

Product

Resources

About