Local Single-Patch Features for Pose Estimation Using the Log-Polar Transform

Viksten, Fredrik; Moe, Anders

doi:10.1007/11492429_6

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…In the setting of pose estimation from a single image we expand upon previous publications in the following ways: 1. we include 14 descriptors from [3], [4], [7], [8], [9], [20], [21], [11], most of them untested in the setting. 2. the tests are made more extensive by using 16 different objects with sufficiently high sampling density and additionally they include a cluttered background.…”

Section: B Contributionsmentioning

confidence: 99%

“…Log-polar sampled patches are related to geometric blur [25] but were designed specifically for pose estimation [4]. In [4] it is stated that each detected IP can be seen as a point of fixation for a steerable camera that then uses foveal sampling as a means of focusing processing in the area close to that point.…”

Section: Log-polar Sampled Patches (Lp and Lpsi)mentioning

confidence: 99%

“…In [4] it is stated that each detected IP can be seen as a point of fixation for a steerable camera that then uses foveal sampling as a means of focusing processing in the area close to that point. After a patch with edge information in double angle representation [24] is sampled using the log-polar sample pattern it is normalized.…”

Section: Log-polar Sampled Patches (Lp and Lpsi)mentioning

confidence: 99%

“…Pose estimation, or estimation of the 6 degree-of-freedom geometrical state from a single 2D image is an important problem that has received considerable attention over the years [1], [2], [3], [4], [5], [6], [7]. Applications include industrial automation such as bin picking (see figure 1), support systems for augmented reality as well as a whole range of consumer products including toys and house-hold appliances.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of local image descriptors for full 6 degree-of-freedom pose estimation

Viksten

Forssén

Johansson³

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the web to allow future comparison with novel algorithms.

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Section: B Contributionsmentioning

confidence: 99%

Section: Log-polar Sampled Patches (Lp and Lpsi)mentioning

confidence: 99%

Section: Log-polar Sampled Patches (Lp and Lpsi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of local image descriptors for full 6 degree-of-freedom pose estimation

Viksten

Forssén

Johansson³

et al. 2009

2009 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

show abstract

“…The estimation of such properties is related to detection of pointsof-interest [13,19,21]. In the last decade, local descriptors [24,17,1,20,15] have seen much interest, mostly due to its success in recognition [17,20] and similar areas such as pose estimation [15,30,31].…”

Section: Introductionmentioning

confidence: 99%