Unmanned Aerial systems (UAS) have raised a great concern on privacy recently. A practical method to protect privacy is needed for adopting UAS in civilian airspace. This paper examines the privacy policies, filtering...
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ISBN:
(纸本)9781479923779
Unmanned Aerial systems (UAS) have raised a great concern on privacy recently. A practical method to protect privacy is needed for adopting UAS in civilian airspace. This paper examines the privacy policies, filtering strategies, existing techniques, then proposes a novel method based on the encrypted video stream and the cloud-based privacy servers. In this scheme, all video surveillance images are initially encrypted, then delivered to a privacy server. The privacy server decrypts the video using the shared key with the camera, and filters the image according to the privacy policy specified for the surveyed region. The sanitized video is delivered to the surveillance operator or anyone on the Internet who is authorized. In a larger system composed of multiple cameras and multiple privacy servers, the keys can be distributed using Kerberos protocol. With this method the privacy policy can be changed on demand in real-time and there is no need for a costly on-board processing unit. By utilizing the cloud-based servers, advanced imageprocessingalgorithms and new filtering algorithms can be applied immediately without upgrading the camera software. This method is cost-efficient and promotes video sharing among multiple subscribers, thus it can spur wide adoption.
The paper describes current results of a research and development (R&D) project aimed to create an advanced prototype of an Enhanced and Synthetic vision System (ESvS). The paper contains functional requirements f...
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ISBN:
(纸本)9781479915361;9781479915385
The paper describes current results of a research and development (R&D) project aimed to create an advanced prototype of an Enhanced and Synthetic vision System (ESvS). The paper contains functional requirements for the ESvS prototype and discusses the main principles of its development. Brief overview of ESvS hardware and platform is given. The following imageprocessingalgorithms of ESvS are described: image enhancement based on modification of the Retinex algorithm, image fusion based on a morphological approach, runway detection based on the original modification of Hough transform - Projection Hough Transform, and runway obstacles detection based on photogrammetric method. The main features of the synthetic vision function of ESvS are described which provide computationally efficient creation of a realistic three-dimensional (3D) out-the-cabin view using terrain, objects, and texture databases.
Early diagnosis is an important aspect of successful treatment for breast cancer. Mammogram is the most reliable imaging technique available. It is a challenging task for radiologists to detect the abnormalities in th...
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ISBN:
(纸本)9781785610301
Early diagnosis is an important aspect of successful treatment for breast cancer. Mammogram is the most reliable imaging technique available. It is a challenging task for radiologists to detect the abnormalities in the mammograms. Computing helps the radiologists in diagnosing the abnormalities in the mammogram. Computer Aided Diagnosis System involves computerized biomedical image analysis to classify the mammography into benign or malign. In a decade of research work number of algorithms had been proposed to classify the image that employ data mining techniques, imageprocessing methods, machine learning methods and pattern recognition. In this paper such algorithms in previous research work is studied and their performance is discussed.
Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral im...
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Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images are extremely high-dimensional, and require advanced on-board processingalgorithms able to satisfy near real-time constraints in applications such as wildland fire monitoring, mapping of oil spills and chemical contamination, etc. One of the most widely used techniques for analyzing hyperspectral images is spectral unmixing, which allows for sub-pixel data characterization. This is particularly important since the available spatial resolution in hyperspectral images is typically of several meters, and therefore it is reasonable to assume that several spectrally pure substances (called endmembers in hyperspectral imaging terminology) can be found within each imaged pixel. In this paper we explore the role of hardware accelerators in hyperspectral remote sensing missions and further inter-compare two types of solutions: field programmable gate arrays (FPGAs) and graphics processing units (GPUs). A full spectral unmixing chain is implemented and tested in this work, using both types of accelerators, in the context of a real hyperspectral mapping application using hyperspectral data collected by NASA's Airborne visible Infra-Red Imaging Spectrometer (AvIRIS). The paper provides a thoughtful perspective on the potential and emerging challenges of applying these types of accelerators in hyperspectral remote sensing missions, indicating that the reconfigurability of FPGA systems (on the one hand) and the low cost of GPU systems (on the other) open many innovative perspectives toward fast on-board and on-the-ground processing of remotely sensed hyperspectral images. (C) 2012 Elsevier B.v. All rights reserved.
This paper deals with the problem of the fundamental matrix identification on the basis of corresponding points on stereo images. It is one of the main problems in a scene reconstruction, using stereo images. This pro...
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ISBN:
(纸本)9789898565501
This paper deals with the problem of the fundamental matrix identification on the basis of corresponding points on stereo images. It is one of the main problems in a scene reconstruction, using stereo images. This problem is commonly solved by the error-adaptive algorithm RANSAC. In this research, this problem is approached in accordance with a conformed identification principle. The method we propose in this paper ensures higher accuracy of the 3D scene reconstruction.
The development of measurement tools for assessing the rock fragmentation induced by blasting has always been constrained by the technology available at the time of development. As computer hardware and software impro...
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ISBN:
(纸本)9780415621403
The development of measurement tools for assessing the rock fragmentation induced by blasting has always been constrained by the technology available at the time of development. As computer hardware and software improves, so too do the opportunities to adapt new and evolving technology to the measurement of blast results. One such area of technology that has developed in recent years is desktop photogrammetry. The author began applying photogrammetry (stereo imaging) to the measurement of rock fragmentation on a conveyor in 2005. This technology has recently been adapted for application on an operating face shovel for the fully automated measurement of the rock fragmentation in the muckpile. The use of synchronised stereo cameras for generation of live 3D images has a number of advantages for automated image segmentation algorithms and autonomous image triggering algorithms. This paper discusses the advantage this approach has over standard monocular based fragmentation measurement systems. Also discussed are some of the environmental aspects associated with mounting systems on a shovel. These factors include high vibration and shock, extreme temperatures and weather conditions, and high volumes of dust, all of which are prevalent on an operating shovel in remote and sometimes extreme locations. These environmental concerns have to be addressed to ensure the installed system survives for extended periods without the need for constant maintenance.
Rainfall data is often collected by measuring the amount of precipitation collected in a physical container at a site. Such methods provide precise data for those sites, but are limited in granularity to the number an...
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ISBN:
(纸本)9780769550121
Rainfall data is often collected by measuring the amount of precipitation collected in a physical container at a site. Such methods provide precise data for those sites, but are limited in granularity to the number and placement of collection devices. We use radar images of storm systems that are publicly available and provide rainfall estimates for large regions of the globe, but at the cost of loss of precision. We present a moving object database called Storm DB that stores decibel measurements of rain clouds as moving regions, ie., we store a single rain cloud as a region that changes shape and position over time. Storm DB is a prototype system that answers rain amount queries over a user defined time duration for any point in the continental United States. In other words, a user can ask the database for the amount of rainfall that fell at any point in the US over a specified time window. Although this single query seems straightforward, it is complicated due to the expected size of the dataset: storm clouds are numerous, radar images are available in high resolution, and our system will collect data over a large timeframe;thus, we expect the number and size of moving regions representing storm clouds to be large. To implement our proposed query, we bring together the following concepts: (i) imageprocessing to retrieve storm clouds from radar images, (ii) interpolation mechanisms to construct moving regions with infinite temporal resolution from region snapshots, (iii) transformations to compute exact point in moving polygon queries using 2-dimensional rather than 3-dimensional algorithms, (iv) GPU algorithms for massively parallel computation of the duration that a point lies inside a moving polygon, and (v) map/reduce algorithms to provide scalability. The resulting prototype lays the groundwork for building big data solutions for moving object databases.
This volume constitutes the thoroughly refereed conference proceedings of the 26th International Conference on Industrial Engineering and Other Applications of Applied Intelligence systems, IEA/AIE 2013, held in Amste...
ISBN:
(数字)9783642385773
ISBN:
(纸本)9783642385766;9783642385773
This volume constitutes the thoroughly refereed conference proceedings of the 26th International Conference on Industrial Engineering and Other Applications of Applied Intelligence systems, IEA/AIE 2013, held in Amsterdam, The Netherlands, in June 2013. The total of 71 papers selected for the proceedings were carefully reviewed and selected from 185 submissions. The papers focus on the following topics: auctions and negotiation, cognitive modeling, crowd behavior modeling, distributed systems and networks, evolutionary algorithms, knowledge representation and reasoning, pattern recognition, planning, problem solving, robotics, text mining, advances in recommender systems, business process intelligence, decision support for safety-related systems, innovations in intelligent computation and applications, intelligent image and signal processing, and machine learning methods applied to manufacturing processes and production systems.
Facial recognition, especially with time-varying facial expressions and/or disguises, is crucial in many homeland security applications. The recent Boston Marathon attack is one example reminder of the importance of d...
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ISBN:
(纸本)9781479915354;9781479939633
Facial recognition, especially with time-varying facial expressions and/or disguises, is crucial in many homeland security applications. The recent Boston Marathon attack is one example reminder of the importance of developing accurate and reliable facial recognition algorithms. While various face recognition algorithms have been proposed in the literature, unfortunately many of them still remain in their infancy. This is mainly due to their lack of high recognition rates in the presence of varying image face artifacts and conditions. In order to develop more accurate facial recognition systems there is a primary need to identify and, as much as possible, derive some of the causes that may affect some face recognition accuracy rates. The main contribution of this paper is the investigation and analysis of how and what factors, other than illumination noise, and occlusion, may affect the recognition accuracy rate of some of the most popular and currently widely used face recognition algorithms, namely, Eigenface-based, Fisher-facebased and Direct Correlation-based ones. In particular, in this work we show the effects, on these facial recognition accuracy, of facial reasonable registration with or without off-the-plane face rotation, the type and number of individual's face template(s) selection, and the type and increasing amount of partial facial information contained in face images. Finally experimental results are presented to demonstrate the potential value and importance of each of these proposed factors on facial recognition.
This paper presents new and efficient algorithms for matching stellar catalogues, where the transformation between the coordinate systems of the two catalogues is unknown and might include shearing. The discovery of a...
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This paper presents new and efficient algorithms for matching stellar catalogues, where the transformation between the coordinate systems of the two catalogues is unknown and might include shearing. The discovery of a given object, whether a star or asterism, from a first catalogue in a second catalogue is logarithmic in time rather than polynomial, which yields a dramatic speed-up relative to a naive implementation. Both the acceleration of the matching algorithm and the ability to solve for arbitrary affine transformations will not only allow the registration of stellar catalogues and images that are now impossible to use, but will also find applications in machine vision and other imaging applications.
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