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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Salarian, Mahdi Ansari, Rashid Univ Illinois Elect & Comp Engn Dept Chicago IL 60607 USA

ISBN: (纸本)9781538646588

Finding the accurate location of a mobile device based on images it acquires usually requires applying structure from motion (SFM) for 3D camera position reconstruction. Since the convergence of SFM depends on effectively selecting among the multiple retrieved images, we propose an optimization framework to do make the selection using the criterion of the highest intra-class similarity among images returned from retrieval pipeline. The selection process should consider only images with distinct GPS-tags. The selected images along with the query can be used to reconstruct a 3D scene and obtain relative camera positions. Experimental results demonstrate our method achieves a higher convergence rate in the SFM processing.

关键词： image-based localization BOF Retrieval

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Optimal Selection of Subset of images with Highest Intra-Class Similarity For 3D Scene Reconstruction

Optimal Selection of Subset of Images with Highest Intra-Cla...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mahdi Salarian Rashid Ansari University of Illinois at Chicago Electrical and Computer Engineering Department Chicago Illinois 60607 United States

ISBN: (纸本)9781538646595

关键词： image-based localization BOF Retrieval images Basic oxygen converters retrieval Space Frequency Modulation Process selection optimal selection Subset Converge

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Real-time monocular image-based 6-DoF localization

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2015年第4-5期34卷 476-492页

作者： Lim, Hyon Sinha, Sudipta N. Cohen, Michael F. Uyttendaele, Matt Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 151742 South Korea Microsoft Res Redmond WA USA

In this paper we present a new real-time image-based localization method for scenes that have been reconstructed offline using structure from motion. From input video, our method continuously computes six-degree-of-freedom camera pose estimates by efficiently tracking natural features and matching them to 3D points reconstructed by structure from motion. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. Our 2D-to-3D matching scheme avoids the need for online extraction of scale-invariant features. Instead, offline we construct an indexed database containing multiple DAISY descriptors per 3D point extracted at multiple scales. The key to the efficiency of our method is invoking DAISY descriptor extraction and matching sparingly during localization, and in distributing this computation over a temporal window of successive frames. This enables the system to run in real-time and achieve low per-frame latency over long durations. Our algorithm runs at over 30Hz on a laptop and at 12Hz on a low-power computer suitable for onboard computation on a mobile robot such as a micro-aerial vehicle. We have evaluated our method using ground truth and present results on several challenging indoor and outdoor sequences.

关键词： image-based localization 2D-to-3D feature matching structure from motion pose estimation

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Multi-descriptor retrieval in digitalized photographs collections

Multi-descriptor retrieval in digitalized photographs collec...

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作者： Neelanjan Bhowmik Universite Paris-Est

学位级别：博士

Content-based image Retrieval (CBIR) is a discipline of Computer Science which aims at auto- matically structuring image collections according to some visual criteria. The offered function- alities include the efficient access to images in a large database of images, or the identification of their content through object detection and recognition tools. They impact a large range of fields which manipulate this kind of data, such as multimedia, culture, security, health, scientific research, etc. To index an image from its visual content first requires producing a visual summary of this content for a given use, which will be the index of this image in the database. From now on, the literature on image descriptors is very rich; several families of descriptors exist and in each family, a lot of approaches live together. Many descriptors do not describe the same information and do not have the same properties. Therefore it is relevant to combine some of them to better describe the image content. The combination can be implemented differently according to the involved descriptors and to the application. In this thesis, we focus on the family of local descriptors, with application to image and object retrieval by example in a collection of images. Their nice properties make them very popular for retrieval, recognition and categorization of objects and scenes. Two directions of research are investigated: Feature combination applied to query-by-example image retrieval: The core of the thesis rests on the proposal of a model for combining low-level and generic descriptors in order to obtain a descriptor richer and adapted to a given use case while maintaining genericity in order to be able to index different types of visual contents. The considered application being query-by- example, another major difficulty is the complexity of the proposal, which has to meet with reduced retrieval times, even with large datasets. To meet these goals, we propose an approach based on the fus

关键词： Content-based image retrieval (CBIR) Feature extraction Interest points Feature combination Bag-of-Features (BoF) Inverted index Spatial complementarity Cultural heritage photography Data linking image exploration Cross-domain image retrieval image-based localization

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Where am I in the dark: Exploring active transfer learning on the use of indoor localization based on thermal imaging

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NEUROCOMPUTING 2016年第Part1期173卷 83-92页

作者： Lu, Guoyu Yan, Yan Ren, Li Saponaro, Philip Sebe, Nicu Kambhamettu, Chandra Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Univ Trento Dept Informat Engn & Comp Sci Trento Italy

Indoor localization is one of the key problems in robotics research. Most current localization systems use cellular base stations and Wifi signals, whose localization accuracy is largely dependent on the signal strength and is sensitive to environmental changes. With the development of camera-based technologies, image-based localization may be employed in an indoor environment where the GPS signal is weak. Most of the existing image-based localization systems are based on color images captured by cameras, but this is only feasible in environments with adequate lighting conditions. In this paper, we introduce an image-based localization system based on thermal imaging to make the system independent of light sources, which are especially useful during emergencies such as a sudden power outage in a building. As thermal images are not obtained as easily as color images, we apply active transfer learning to enrich the thermal image classification learning, where normal RGB images are treated as the source domain, and thermal images are the target domain. The application of active transfer learning avoids random target training sample selection and chooses the most informative samples in the learning process. Through the proposed active transfer learning, the query thermal images can be accurately used to indicate the location. Experiments show that our system can be efficiently deployed to perform indoor localization in a dark environment. (C) 2015 Elsevier B.V. All rights reserved.

关键词： image-based localization Active transfer learning Thermal imaging

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Towards efficient mobile image-guided navigation through removal of outliers

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EURASIP JOURNAL ON image AND VIDEO PROCESSING 2016年第1期2016卷 1页

作者： Sirazitdinova, Ekaterina Jonas, Stephan M. Lensen, Jan Kochanov, Deyvid Houben, Richard Slijp, Hans Deserno, Thomas M. Uniklin RWTH Aachen Dept Med Informat Pauwelsstr 30 D-52057 Aachen Germany Appl Biomed Syst Oxfordlaan 55 NL-6229 Maastricht Netherlands

A novel approach for positioning using smartphones and image processing techniques is developed. Using structure from motion, 3D reconstructions of given tracks are created and stored as sparse point clouds. Query images are matched later to these 3D models. High computational costs of image matching and limited storage require compressing point clouds without loss of positioning performance. In this work, localization is improved and memory and storage requirements are minimized. We assumed that the computational speed and, at the same time, storage requirements benefit from reducing the number of points with appropriate outlier detection. In particular, our hypothesis was that positioning accuracy is maintained while reducing outliers in a reconstructed model. To evaluate the hypothesis, three methods were compared: (i) density-based (Sotoodeh, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-5, 2006), (ii) connectivity-based (Wang et al. Comput Graph Forum 32(5):207-10, 2013), and (iii) our distance-based approach. In tenfold cross-validation, applied to a pre-reconstructed reference 3D model, localization accuracy was measured. In each new model, the positions of test images were identified and compared to the according positions in the reference model. We observed that outlier removal has a positive impact on matching run-time and storage requirements, while there are no significant differences in the localization error within the methods. That confirmed our initial hypothesis and allows mobile application of image-based positioning.

关键词： image-based localization Mobile navigation Structure from motion 3D point clouds Outlier removal

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Technologies for Under-Ice AUV Navigation

Technologies for Under-Ice AUV Navigation

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IEEE OES Joint Symposium/Workshop on Autonomous Underwater Vehicles (AUV)

作者： Bandara, Doupadi Leong, Zhi Nguyen, Hung Jayasinghe, Shantha Forrest, Alexander L. Univ Tasmania Australian Maritime Collage Hobart Tas 7001 Australia Univ Calif Davis Civil & Environm Engn Davis CA 95616 USA

ISBN: (纸本)9781509024421

Approximately 12% of the world's oceans are covered by ice. Understanding the physical processes, ecosystem structure, mixing dynamics and the role of these inaccessible environments in the context of global climate change is extremely important. Autonomous Underwater Vehicles (AUVs) play a major role in the potential exploration of these water systems due to the challenges of human access and relatively high associated risk. That said, AUV navigation and localization is challenging in these environments due to the unavoidable growth of navigational drift associated with inertial navigation systems, especially in long range missions under ice where surfacing in open water is not possible. While acoustic transponders have been used, they are time consuming and difficult to deploy. Terrain Relative Navigation (TRN) and Simultaneous localization and Mapping (SLAM) based technologies are emerging in recent years as promising navigation solutions as they neither require deploying navigational aids or calculating the distance travelled from a reference point to determine location. One of the key challenges of underwater or under-ice image based localization results from the unstructured nature and lack of significant features in underwater environments. This issue has motivated the review presented in this paper, which outlines a potential area of under ice AUV navigation and localization by combining TRN and SLAM with image matching methods for navigation in featureless environments.

关键词： Autonomous underwater vehicle (AUV) image-based localization featureless environment Simultaneous localization and Mapping (SLAM) Under-ice navigation

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IMAGO: image-guided navigation for visually impaired people

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2015年第5期7卷 679-692页

作者： Jonas, Stephan M. Sirazitdinova, Ekaterina Lensen, Jan Kochanov, Deyvid Mayzek, Humaam de Heus, Tjeu Houben, Richard Slijp, Hans Deserno, Thomas M. Uniklin RWTH Aachen Dept Med Informat Aachen Germany Appl Biomed Syst Maastricht Netherlands FileFlow De Heus Beeldvorming Sittard Netherlands I Cane Social Technol Sittard Netherlands

Blind and visually impaired persons face many challenges due to isolation, the most important is lacking education due to social immobility. Yet, since the development of the well-known white-red cane, only few advances have been made to increase the mobility of blind people. GPS systems are often used for pedestrian navigation but lack precision, foremost in rural areas where navigation is most often needed. The aim of the IMAGO project is therefore the development of an inexpensive and unobtrusive navigation method for blind and visually impaired persons. Navigation is performed using structure from motion and image-based localization techniques. Route models are created as 3D point clouds through several steps: (i) image acquisition along the routes;(ii) bulk-transfer of images to a server;(iii) feature extraction;(iv) feature matching between images;(v) creation of 3D point cloud with structure from motion. Similarly, the navigation chains seven steps: (a) image acquisition while walking a route;(b) immediate transfer of image to a server;(c) feature extraction;(d) feature matching between image and 3D route model;(e) localization/camera matrix calculation;(f) navigation/calculation of direction based on localization;(g) transfer of direction to user. Current smartphones are used as devices both for recording of routes as well as navigation. Thereby, a high level of dissemination without additional costs is possible, both, within blind people for navigation, as well as seeing people for route creation. Additionally, haptic feedback can be used via a smart cane to reduce auditive feedback. The proposed system yields a high positioning accuracy of 80% of samples being located within 1.6 m. Thus, the system is usable for pedestrian navigation, especially for visually impaired persons.

关键词： Visually impaired navigation structure from motion image-based localization

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Global localization from Monocular SLAM on a Mobile Phone

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2014年第4期20卷 531-539页

作者： Ventura, Jonathan Arth, Clemens Reitmayr, Gerhard Schmalstieg, Dieter Graz Univ Technol A-8010 Graz Austria

We propose the combination of a keyframe-based monocular SLAM system and a global localization method. The SLAM system runs locally on a camera-equipped mobile client and provides continuous, relative 6DoF pose estimation as well as keyframe images with computed camera locations. As the local map expands, a server process localizes the keyframes with a pre-made, globally-registered map and returns the global registration correction to the mobile client. The localization result is updated each time a keyframe is added, and observations of global anchor points are added to the client-side bundle adjustment process to further refine the SLAM map registration and limit drift. The end result is a 6DoF tracking and mapping system which provides globally registered tracking in real-time on a mobile device, overcomes the difficulties of localization with a narrow field-of-view mobile phone camera, and is not limited to tracking only in areas covered by the offline reconstruction.

关键词： image-based localization monocular SLAM real-time tracking global positioning mobile augmented reality

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GAIL: Geometry-aware automatic image localization

GAIL: Geometry-aware automatic image localization

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8th International Conference on Computer Vision Theory and Applications, VISAPP 2013

作者： Benedetti, Luca Corsini, Massimiliano Dellepiane, Matteo Cignoni, Paolo Scopigno, Roberto ISTI-CNR Pisa Italy

ISBN: (纸本)9789898565471

The access and integration of the massive amount of information, that can be provided by the web, can be of great help in a number of fields, including tourism and advertising of artistic sites. A "virtual visit" of a place can be a valuable experience before, during and after the experience on-site. For this reason, the contribution from the public could be merged to provide a realistic and immersive visit of known places. We propose an automatic image localization system, which is able to recognize the site that has been framed, and calibrate it on a pre-existing 3D representation. The system is characterized by very high accuracy and it is able to validate, in a completely unsupervised manner, the result of the localization. Given an unlocalized image, the system selects a relevant set of pre-localized images, performs a Structure from Motion partial reconstruction of this set and then obtain an accurate camera calibration of the image with respect to the model by minimizing distances between projections on the model surface of corresponding image features. The accuracy reached is enough to seamlessly view the input image correctly super-imposed in the 3D scene.

关键词： 2D/3D registration image-based localization

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