64,144 research outputs found
High-resolution optical and SAR image fusion for building database updating
This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of DempsterâShafer evidence theory
Video analysis based vehicle detection and tracking using an MCMC sampling framework
This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences
Design and implementation of an integrated surface texture information system for design, manufacture and measurement
The optimised design and reliable measurement of surface texture are essential to guarantee the functional performance of a geometric product. Current support tools are however often limited in functionality, integrity and efficiency. In this paper, an integrated surface texture information system for design, manufacture and measurement, called âCatSurfâ, has been designed and developed, which aims to facilitate rapid and flexible manufacturing requirements. A category theory based knowledge acquisition and knowledge representation mechanism has been devised to retrieve and organize knowledge from various Geometrical Product Specifications (GPS) documents in surface texture. Two modules (for profile and areal surface texture) each with five components are developed in the CatSurf. It also focuses on integrating the surface texture information into a Computer-aided Technology (CAx) framework. Two test cases demonstrate design process of specifications for the profile and areal surface texture in AutoCAD and SolidWorks environments respectively
ScaRR: Scalable Runtime Remote Attestation for Complex Systems
The introduction of remote attestation (RA) schemes has allowed academia and
industry to enhance the security of their systems. The commercial products
currently available enable only the validation of static properties, such as
applications fingerprint, and do not handle runtime properties, such as
control-flow correctness. This limitation pushed researchers towards the
identification of new approaches, called runtime RA. However, those mainly work
on embedded devices, which share very few common features with complex systems,
such as virtual machines in a cloud. A naive deployment of runtime RA schemes
for embedded devices on complex systems faces scalability problems, such as the
representation of complex control-flows or slow verification phase.
In this work, we present ScaRR: the first Scalable Runtime Remote attestation
schema for complex systems. Thanks to its novel control-flow model, ScaRR
enables the deployment of runtime RA on any application regardless of its
complexity, by also achieving good performance. We implemented ScaRR and tested
it on the benchmark suite SPEC CPU 2017. We show that ScaRR can validate on
average 2M control-flow events per second, definitely outperforming existing
solutions.Comment: 14 page
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