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.
A feature descriptor robust to different types of image blur was proposed in @cite_13 . The descriptor is build by considering integral projections along different angular directions. According to the paper, the method outperforms the traditional descriptors. However, it still uses a feature detector that is not robust to motion blur. We argue in favor of deblurring, which allows us to use the existing feature detectors and descriptors that are known to perform well under various conditions.
@cite_13
2541136605 2119605622 1677409904 1990063939
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. This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF 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 (specifically, 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 encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision. 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. One of the principal causes for image quality degradation is blur. This frequent phenomenon is usually a result of misfocused optics or camera motion, and it is very difficult to undo. Beyond the impaired visual quality, blurring causes problems to computer vision algorithms. In this paper, we present a simple yet powerful image descriptor, which is robust against the most common image blurs. The proposed method is based on quantizing the phase information of the local Fourier transform and it can be used to characterize the underlying image texture. We show how to construct several variants of our descriptor by varying the technique for local phase estimation and utilizing the proposed data decorrelation scheme. The descriptors are assessed in texture and face recognition experiments, and the results are compared with several state-of-the-art methods. The difference to the baseline is considerable in the case of blurred images, but also with sharp images our method gives a highly competitive performance. Highlights? We present a new blur insensitive texture descriptor. ? Descriptors are constructed by quantizing local frequency data. ? The method is assessed in texture and face classification experiments. ? We show clear improvements over LBP, Gabor, VZ-MR8, VZ-joint, and BIF columns. ? Descriptors give the best results in the case of both sharp and blurred images.