1,720,965 research outputs found
Pairwise Registration by Local Orientation Cues
Get PDFInspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast
Learning-To-rank approach to RGB-D visual search
No full textBoth color and depth information may be deployed to seek by content through RGB-D imagery. Previous works dealing with global descriptors for RGB-D images advocate a decision level merger in which color and depth representations, independently computed, are juxtaposed to pursue a search for similarities. Differently, we propose a "learning-To-rank" paradigm aimed at weighting the two information channels according to the specific traits of the task and data at hand, thereby effortlessly addressing the potential diversity across applications. In particular, we propose a method, referred to as "kNN-rank," which can learn the regularities among the outputs yielded by similarity-based queries. Another contribution concerns the "HyperRGBD" framework, a set of tools conceived to enable seamless aggregation of existing RGB-D datasets to obtain data featuring desired peculiarities and cardinality
Analysis of compact features for RGB-D visual search
No full textAnticipating the oncoming integration of depth sensing into mobile devices, we experimentally compare different compact features for representing RGB-D images in mobile visual search. Experiments on 3 state-of-the-art datasets, addressing both category and instance recognition, show how Deep Features provided by Convolutional Neural Networks better represent appearance information, whereas shape is more effectively encoded through Kernel Descriptors. Moreover, our evaluation suggests that learning to weight the relative contribution of depth and appearance is key to deploy effectively depth sensing in forthcoming mobile visual search scenarios
Learning to weight color and depth for RGB-D visual search
No full textBoth color and depth information may be deployed to seek by content through RGB-D imagery. Previous works dealing with global descriptors for RGB-D images advocate a decision level fusion whereby independently computed color and depth representations are juxtaposed to pursue similarity search. Differently, in this paper we propose a learning-to-rank paradigm aimed at weighting the two information channels according to the specific traits of the task and data at hand, thereby effortlessly addressing the potential diversity across applications. In particular, we propose a novel method, referred to as kNN-rank, which can learn the regularities among the outputs yielded by similarity-based queries. A further novel contribution of this paper concerns the HyperRGBD framework, a set of tools conceived to enable seamless aggregation of existing RGB-D datasets in order to obtain new data featuring desired peculiarities and cardinality
RGB-D Visual Search with Compact Binary Codes
No full textAs integration of depth sensing into mobile devices is likely forthcoming, we investigate on merging appearance and shape information for mobile visual search. Accordingly, we propose an RGB-D search engine architecture that can attain high recognition rates with peculiarly moderate bandwidth requirements. Our experiments include a comparison to the CDVS (Compact Descriptors for Visual Search) pipeline, candidate to become part of the MPEG-7 standard, and contribute to elucidate on the merits and limitations of joint deployment of depth and color in mobile visual search
Traffic sign detection via interest region extraction
No full textMobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of mandatory, prohibitory and danger traffic signs, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data
Automatic detection of pole-like structures in 3D urban environments
No full textThis work aims at automatic detection of man-made pole-like structures in scans of urban environments acquired by a 3D sensor mounted on top a moving vehicle. Pole-like structures, such as e.g. roadsigns and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation, facility damage detection, city planning and maintenance. Yet, due to the characteristic thin shape, detection of man-made pole-like structures is significantly prone to both noise as well as occlusions and clutter, the latter being pervasive nuisances when scanning urban environments. Our approach is based on a ``local'' stage, whereby local features are classified and clustered together, followed by a ``global'' stage aimed at further classification of candidate entities. The proposed pipeline turns out effective in experiments on a standard publicly available dataset as well as on a challenging dataset acquired during the project for validation purposes
Creating and Recognizing 3D Objects
No full textThis thesis aims at investigating on 3D Computer Vision, a research topic which is gathering even increasing attention thanks to the more and more widespread availability of affordable 3D visual sensor, such as, in particular consumer grade RGB-D cameras.
The contribution of this dissertation is twofold. First, the work addresses how to compactly represent the content of images acquired with RGB-D cameras. Second, the thesis focuses on 3D Reconstruction, key issue to efficiently populate the databases of 3D models deployed in object/category recognition scenarios.
As 3D Registration plays a fundamental role in 3D Reconstruction, the former part of the thesis proposes a pipeline for coarse registration of point clouds that is entirely based on the computation of 3D Local Reference Frames (LRF). Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, RGB-D and stereo cameras) as well as on quantitative comparison with respect to three other methods.
Driven by the ever-lower costs and growing distribution of 3D sensing devices, we expect broad-scale integration of depth sensing into mobile devices to be forthcoming.
Accordingly, the thesis investigates on merging appearance and shape information for Mobile Visual Search and focuses on encoding RGB-D images in compact binary codes.
An extensive experimental analysis on three state-of-the-art datasets, addressing both category and instance recognition scenarios, has led to the development of an RGB-D search engine architecture that can attain high recognition rates with peculiarly moderate bandwidth requirements.
Our experiments also include a comparison with the CDVS (Compact Descriptors for Visual Search) pipeline, candidate to become part of the MPEG-7 standard
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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