29,810 research outputs found
How good are detection proposals, really?
Current top performing Pascal VOC object detectors employ detection proposals
to guide the search for objects thereby avoiding exhaustive sliding window
search across images. Despite the popularity of detection proposals, it is
unclear which trade-offs are made when using them during object detection. We
provide an in depth analysis of ten object proposal methods along with four
baselines regarding ground truth annotation recall (on Pascal VOC 2007 and
ImageNet 2013), repeatability, and impact on DPM detector performance. Our
findings show common weaknesses of existing methods, and provide insights to
choose the most adequate method for different settings
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
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