Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.
@cite_20 evaluated several interest point detectors and feature descriptors for visual tracking. The effect of motion blur was also studied. Fast Hessian @cite_12 and Difference of Gaussian (DoG) @cite_17 detectors were found to be the most tolerant against motion blur as they provided the highest repeatability. Nevertheless, the performance of all detectors clearly degraded as motion blur increased. Neither of the detectors worked particularly well with strong motion blur. The evaluation of descriptors showed a similar decrease in performance, especially when matching blurred and non-blurred images. In such case, the SIFT @cite_2 outperformed the other descriptors.
@cite_2 @cite_12 @cite_20 @cite_17
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This paper describes a visual feature detector and descriptor scheme designed to address the specific problems of humanoid robots in the tasks of visual odometry, localization, and SLAM (Simultaneous Localization And Mapping). During walking, turning, and squatting movements, the camera of a humanoid robot moves in jerky and sometimes unpredictable way. This causes an undesired motion blur in the images grabbed by the robot camera, that negatively affects the performance of the image processing algorithms. Indeed, the classical features detector and descriptor filtering techniques, that proved to work so well for wheeled robots, do not perform so reliably in humanoid robots. This paper presents a method to detect image interest points (invariant to scale transformation and rotations) robust to motion-blur introduced by the camera motion. Our approach is based on a preprocessing step to estimate the point spread function (PSF) of the motion blur. The PSF is used to deconvolve the image reducing the blur. Then, we apply a feature detector inspired by SURF approach and the feature descriptor from SIFT. Experiments performed on standard datasets corrupted with motion blur and on images taken by a camera mounted on a small humanoid robot show the effectiveness of the proposed technique. Our approach presents higher performances and higher reliability in matching features in the different images of a sequence affected by motion-blur. We explore the performance of a number of popular feature detectors and descriptors in matching 3D object features across viewpoints and lighting conditions. To this end we design a method, based on intersecting epipolar constraints, for providing ground truth correspondence automatically. These correspondences are based purely on geometric information, and do not rely on the choice of a specific feature appearance descriptor. We test detector-descriptor combinations on a database of 100 objects viewed from 144 calibrated viewpoints under three different lighting conditions. We find that the combination of Hessian-affine feature finder and SIFT features is most robust to viewpoint change. Harris-affine combined with SIFT and Hessian-affine combined with shape context descriptors were best respectively for lighting change and change in camera focal length. We also find that no detector-descriptor combination performs well with viewpoint changes of more than 25---30?. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance. Where feature points are used in real-time frame-rate applications, a high-speed feature detector is necessary. Feature detectors such as SIFT (DoG), Harris and SUSAN are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Here we show that machine learning can be used to derive a feature detector which can fully process live PAL video using less than 7 of the available processing time. By comparison neither the Harris detector (120 ) nor the detection stage of SIFT (300 ) can operate at full frame rate. Clearly a high-speed detector is of limited use if the features produced are unsuitable for downstream processing. In particular, the same scene viewed from two different positions should yield features which correspond to the same real-world 3D locations [1]. Hence the second contribution of this paper is a comparison corner detectors based on this criterion applied to 3D scenes. This comparison supports a number of claims made elsewhere concerning existing corner detectors. Further, contrary to our initial expectations, we show that despite being principally constructed for speed, our detector significantly outperforms existing feature detectors according to this criterion.