1,721,003 research outputs found
Guest editorial: Local image descriptors in computer vision
Full text link...This Special Issue includes seven original research papers that cover diverse and significant aspects of local image descriptor research. In particular, the order in which papers appear reflects the main phase they address, in an ideal computational pipeline starting with the localisation of salient points in an image and ending with the incorporation of spatial and temporal features in descriptor construction...
Rethinking the sGLOH descriptor
Full text linksGLOH (shifting GLOH) is a histogram-based keypoint descriptor that can be associated to multiple quantized rotations of the keypoint patch without any recomputation. This property can be exploited to define the best distance between two descriptor vectors, thus avoiding computing the dominant orientation. In addition, sGLOH can reject incongruous correspondences by adding a global constraint on the rotations either as an a priori knowledge or based on the data. This paper thoroughly reconsiders sGLOH and improves it in terms of robustness, speed and descriptor dimension. The revised sGLOH embeds more quantized rotations, thus yielding more correct matches. A novel fast matching scheme is also designed, which significantly reduces both computation time and memory usage. In addition, a new binarization technique based on comparisons inside each descriptor histogram is defined, yielding a more compact, faster, yet robust alternative. Results on an exhaustive comparative experimental evaluation show that the revised sGLOH descriptor incorporating the above ideas and combining them according to task requirements, improves in most cases the state of the art in both image matching and object recognition
Dissecting and Reassembling Color Correction Algorithms for Image Stitching
Full text linkThis paper introduces a new compositional framework for classifying color correction methods according to their two main computational units. The framework was used to dissect fifteen among the best color correction algorithms and the computational units so derived, with the addition of four new units specifically designed for this work, were then reassembled in a combinatorial way to originate about one hundred distinct color correction methods, most of which never considered before. The above color correction methods were tested on three different existing datasets, including both real and artificial color transformations, plus a novel dataset of real image pairs categorized according to the kind of color alterations induced by specific acquisition setups. Differently from previous evaluations, special emphasis was given to effectiveness in real world applications, such as image mosaicing and stitching, where robustness with respect to strong image misalignments and light scattering effects is required. Experimental evidence is provided for the first time in terms of the most recent perceptual image quality metrics, which are known to be the closest to human judgment. Comparative results show that combinations of the new computational units are the most effective for real stitching scenarios, regardless of the specific source of color alteration. On the other hand, in the case of accurate image alignment and artificial color alterations, the best performing methods either use one of the new computational units, or are made up of fresh combinations of existing unit
Color Correction for Image Stitching by Monotone Cubic Spline Interpolation
No full textThis paper proposes a novel color correction scheme for image stitching where the color map transfer is modelled by a monotone Hermite cubic spline and smoothly propagated into the target image. A three-segments monotone cubic spline minimizing color distribution statistics and gradient differences with respect to both the source and target images is used. While the spline model can handle non-linear color maps, the minimization over the gradient differences limits strong alterations on the image structure. Adaptive heuristics are introduced to reduce the minimization search space and thus computational time. Experimental comparisons with respect to the state-of-the-art linear mapping models show the validity of the proposed method
Estimating the best reference homography for planar mosaics from videos
Full text linkThis paper proposes a novel strategy to find the best reference homography in mosaics from video sequences. The reference homography globally minimizes the distortions induced on each image frame by the mosaic homography itself. This method is designed for planar mosaics on which a bad choice of the first reference image frame can lead to severe distortions after concatenating several successive homographies. This often happens in the case of underwater mosaics with non-flat seabed and no georeferential information available. Given a video sequence of an almost planar surface, sub-mosaics with low distortions of temporally close image frames are computed and successively merged according to a hierarchical clustering procedure. A robust and effective feature tracker using an approximated global position map between image frames allows us to build the mosaic also between locally close but not temporally consecutive frames. Sub-mosaics are successively merged by concatenating their relative homographies with another reference homography which minimizes the distortion on each frame of the fused image. Experimental results on challenging real underwater videos show the validity of the proposed method
Super-resolution-based magnification of endothelium cells from biomicroscope videos of the cornea
Full text linkWe present a practical, robust, and effective pipeline to compute a high-resolution (HR) image of the corneal endothelium starting from a low-resolution (LR) video sequence obtained with a general purpose slit lamp biomicroscope. An image quality typical of dedicated and more expensive confocal microscopes is achieved via software magnification by exploiting information redundancy in the video sequence. In particular, the HR image is generated from the best LR frames, obtained by identifying the most suitable endothelium video subsequence using a support vector machine-based learning approach, followed by a robust graph-based frame registration. Results on long, real sequences show that the proposed approach is fast and produces better quality images than both classical multiframe super-resolution approaches and commercial state-of-the-art mosaicing software. Only low-cost equipment is required that makes the proposed method a valid diagnostic tool and an affordable resource for medical practice in both developed and developing countries
A Non-Parametric Parallel Harris-Affine Detector
No full textThis paper describes a parallel version
of a new automatic Harris-based corner
detector. A simple but effective client-server based
scheduler has been implemented in order to
dynamically distribute the workload on heterogeneous
parallel architectures such as Grid systems.
Results obtained on the COMETA Grid show the
effectiveness and the robustness of the proposed
approach
Selective visual odometry for accurate AUV localization
Full text linkIn this paper we present a stereo visual odometry system developed for autonomous underwater vehicle localization tasks. The main idea is to make use of only highly reliable data in the estimation process, employing a robust keypoint tracking approach and an effective keyframe selection strategy, so that camera movements are estimated with high accuracy even for long paths. Furthermore, in order to limit the drift error, camera pose estimation is referred to the last keyframe, selected by analyzing the feature temporal flow. The proposed system was tested on the KITTI evaluation framework and on the New Tsukuba stereo dataset to assess its effectiveness on long tracks and different illumination conditions. Results of a live archaeological campaign in the Mediterranean Sea, on an AUV equipped with a stereo camera pair, show that our solution can effectively work in underwater environments
Keypoint descriptor matching with context-based orientation estimation
No full textThis paper presents a matching strategy to improve the discriminative power of histogram-based keypoint descriptors by constraining the range of allowable dominant orientations according to the context of the scene under observation. This can be done when the descriptor uses a circular grid and quantized orientation steps, by computing or providing a global reference orientation based on the feature matches.
The proposed matching strategy is compared with the standard approaches used with the SIFT and GLOH descriptors and the recent rotation invariant MROGH and LIOP descriptors. A new evaluation protocol based on an approximated overlap error is presented to provide an effective analysis in the case of non-planar scenes, thus extending the current state-of-the-art results
Editorial for special issue “fine art pattern extraction and recognition”
Full text link: Cultural heritage, especially the fine arts, plays an invaluable role in the cultural, historical, and economic growth of our societies [...]
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