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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是171-180 订阅

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A Parallel Framework for Object Detection and Recognition for Secure Vehicle Parking 17

A Parallel Framework for Object Detection and Recognition fo...

引用

2015 IEEE 17th International conference on High Performance Computing and Communications (HPCC)

作者： Mahmood, Zahid Khan, Muhammad Usman Shahid Jawad, Muhammad Khan, Samee U. Yang, Laurence T. North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58102 USA St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

ISBN: (纸本)9781479989379

image analytics, biometrics access control, security, and surveillance applications utilize complex machine learning and computer vision algorithms, such as face detection and recognition. Speedup and accuracy are two important factors that need to be addressed in all such complex applications. Parallel computing breaks down the complex tasks into discrete fragments to be solved concurrently on multiple processors. the parallel computing procedure significantly reduces the execution time with improved speedup. this paper presents a parallel framework for object detection and recognition for a secure vehicle parking. the proposed framework is divided in to three steps: (1) vehicle detection at the parking entry junction, (2) driver's face detection, and (3) identification of driver's face from the huge database of stored facial images. On successful identification of authorized person, vehicle is allowed to enter in the parking-lot. the adaptive boosting algorithm is used for vehicle and face detection, while Eigenfaces based approach is employed for face recognition. Moreover, the scalability comparison of parallely executed driver face recognition algorithm indicates high speedup compared to serial execution. Furthermore, the results of the proposed framework reveal promising performance and encourage outcomes to be deployed in real-time at entrance/exits of the public/private vehicle parking areas.

关键词： Eigenfaces Face Recognition Haar Classifiers Parallel processing

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A high frequency 3D LiDAR with enhanced measurement density via Papoulis-Gerchberg 17

A high frequency 3D LiDAR with enhanced measurement density ...

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17th International conference on Advanced Robotics, ICAR 2015

作者： Özbay, Bengisu Kuzucu, Elvan Gül, Mustafa Öztürk, Dilan Taşci, Muhittin Arisoy, A. Mansur Şirin, Halil Onur Uyanik, Ismail Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Guidance and Unmanned Systems Division MGEO Sector ASELSAN A. Ş. Ankara06011 Turkey

ISBN: (纸本)9781467375092

Light Detection and Ranging (LiDAR) devices are gaining more importance for obtaining sensory information in mobile robot applications. However, existing solutions in literature yield low frequency outputs with huge measurement delay to obtain 3D range image of the environment. this paper introduces the design and construction of a 3D range sensor based on rotating a 2D LiDAR around its pitch axis. Different than previous approaches, we adjust our scan frequency to 5 Hz to support its application on mobile robot platforms. However, increasing scan frequency drastically reduces the measurement density in 3D range images. therefore, we propose two post-processing algorithms to increase measurement density while keeping the 3D scan frequency at an acceptable level. To this end, we use an extended version of the Papoulis-Gerchberg algorithm to achieve super-resolution on 3D range data by estimating the unmeasured samples in the environment. In addition, we propose a probabilistic obstacle reconstruction algorithm to consider the probabilities of the estimated (virtual) points and to obtain a very fast prediction about the existence and shape of the obstacles. © 2015 IEEE.

关键词： Robot applications

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Optimal Performance Prediction of ADAS algorithms on Embedded Parallel Architectures

Optimal Performance Prediction of ADAS Algorithms on Embedde...

引用

IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Romain Saussard Boubker Bouzid Marius Vasiliu Roger Reynaud Renault S.A.S Guyancourt France Instutut d'Electronique Fondamentale Université Paris Sud Orsay France

ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy image processing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. these SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of image processing algorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.

关键词： Kernel Computer architecture image processing Graphics processing units Prediction algorithms Parallel processing Computational modeling

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A Parallel Framework for Object Detection and Recognition for Secure Vehicle Parking

A Parallel Framework for Object Detection and Recognition fo...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Zahid Mahmood Muhammad Usman Shahid Khan Muhammad Jawad Samee U. Khan Laurence T. Yang Department of Electrical & Computer Engineering North Dakota State University USA North Dakota State University Fargo ND US Department of Computer Science St. Francis Xavier University Antigonish NS Canada

image analytics, biometrics access control, security, and surveillance applications utilize complex machine learning and computer vision algorithms, such as face detection andrecognition. Speedup and accuracy are two important factors that need to be addressed in all such complex applications. Parallel computing breaks down the complex tasks into discrete fragments to be solved concurrently on multiple processors. the parallel computing procedure significantly reduces the executiontime with improved speedup. this paper presents a parallel framework for object detection and recognition for a secure vehicle parking. the proposed framework is divided in to three steps: (1) vehicle detection at the parking entry junction, (2)driver's face detection, and (3) identification of driver's face from the huge database of stored facial images. On successful identification of authorized person, vehicle is allowed to enter inthe parking-lot. the adaptive boosting algorithm is used for vehicle and face detection, while Eigenfaces based approach is employed for face recognition. Moreover, the scalability comparison of parallely executed driver face recognition algorithm indicates high speedup compared to serial execution. Furthermore, the results of the proposed framework reveal promising performance and encourage outcomes to be deployed in real-time at entrance/exits of the public/private vehicle parking areas.

关键词： Vehicles Face Face recognition Training Face detection Object detection Parallel processing

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Snowfall detection under bad visibility scene 17

Snowfall detection under bad visibility scene

引用

IEEE 17th International conference on Intelligent Transportation systems (ITSC)

作者： Kawarabuki, Hiroshi Onoguchi, Kazunori Hirosaki Univ Grad Sch Sci & Technol Hirosaki Aomori 0368561 Japan

ISBN: (纸本)9781479960781

An effect of weather is very critical in outdoor camera surveillance applications. So, we focus on bad weather by snow and we propose an algorithm of detecting snowfall from surveillance camera images. the proposed algorithm increases a contrast of an image by haze removal. A contrast of hazy image is reduced by haze color of atmosphere. When several haze colors exist in an image, the degree of contrast degradation is different for each haze color. To deal with this problem, the proposed method performs segmentation of an image for every haze color and estimates the airlight, the transmission, and the weight of dehaze in each segmentation area individually. Most of haze removal algorithms require a tone curve correction as post processing. However, it is very difficult to select an optimal tone curve correction for any image. To deal with this problem, we propose a novel algorithm that does not require any tone curve correction. the general haze removal algorithm treats only haze image. However, in the field of outdoor camera surveillance, it's desirable to realize the algorithm which can be applied to various weather conditions. Our algorithm can detect snowfall at high speed and stably not only in bad weather but also in good weather.

关键词： image processing snowfall detection haze removal segmentation background subtraction

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Modeling Arbitrarily Oriented Slanted Planes for Efficient Stereo Vision based on Block Matching 17

Modeling Arbitrarily Oriented Slanted Planes for Efficient S...

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IEEE 17th International conference on Intelligent Transportation systems (ITSC)

作者： Ranft, Benjamin Strauss, Tobias FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany Karlsruhe Inst Technol Dept Measurement & Control Syst D-76131 Karlsruhe Germany

ISBN: (纸本)9781479960781

Stereo cameras enable a 3D reconstruction of viewed scenes and are therefore well-suited sensors for many advanced driver assistance systems and autonomous driving. Modern algorithms for estimating distances for every image pixel achieve high-quality results, but their real-time capability is very limited. In contrast, window-based local methods can be implemented very efficiently but are more prone to errors. this is particularly true for spatial changes of distance within the matching window, most prominently on surfaces such as the road which are not parallel to but rather slanted towards the image plane. In this paper we present a method to compensate the impact of this effect for arbitrarily oriented sets of planes. It does not depend on any modifications to the actual distance estimation. Instead, it only applies specific transformations to input images and intermediate results. By combining this approach with existing implementations which efficiently use either multi-core or graphics processors, we were able to significantly increase quality while maintaining real-time throughputs on a compact target system.

关键词： driver information systems image matching image reconstruction stereo image processing advanced driver assistance systems arbitrarily oriented slanted planes autonomous driving block matching image pixel image transformation scene reconstruction stereo vision Cameras Estimation Roads Standards Table lookup three-dimensional displays Transforms block matching three-dimensional displays Stereoviews Stereo image processing Table lookup driver information systems graph transformation advanced driver assistance systems (ADAS) Road image matching Transform Cameras image reconstruction

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A New Pixel-based Fusion Framework to Enhance Object Detection in Automotive Applications 17

A New Pixel-based Fusion Framework to Enhance Object Detecti...

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17th International conference on Information Fusion (FUSION)

作者： thomanek, Jan Wanielik, Gerd Ingenieurgesellsch Auto & Verkehr GmbH D-10587 Berlin Germany Tech Univ Chemnitz D-09107 Chemnitz Germany

ISBN: (纸本)9781479916344

Modern cooperative Advanced Driver Assistance systems (ADASs) require efficient algorithms and methods for the real-time processing of all sensor data. In particular, systems for combination of different sensors are gaining increasing importance. this paper proposes an early fusion technique to combine the advantages of two or more vision sensors. It creates a single composite image that will be more comprehensive for further computer vision tasks, e.g. Object Detection. After image registration, the presented pixel-based fusion framework transforms the registered sensor images into a common representational format by a multiscale decomposition. A denoising and a multiscale edge detection are applied on the transformed data. Only data with a high Activity Level are considered for the fusion process that based on a probabilistic approach. Finally, the fused image can be the input for a subsequent feature extraction task. the proposed fusion technique is examined on a Pedestrian Detection System based on an infrared and a visible light camera.

关键词： Cameras Feature extraction image edge detection Object detection Wavelet domain Wavelet transforms Object detection Edge detection Wavelet domain Cameras wavelet transforms Feature extraction Automotive applications images Fusion reactions FUSION fusion (melting) Fusion, biological Sensor Data COMPOSITE imageS vision sensors

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Real-Time Obstacle Detection using Stereo Vision for Autonomous Ground Vehicles: A Survey 17

Real-Time Obstacle Detection using Stereo Vision for Autonom...

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IEEE 17th International conference on Intelligent Transportation systems (ITSC)

作者： Bernini, Nicola Bertozzi, Massimo Castangia, Luca Patander, Marco Sabbatelli, Mario Univ Parma VisLab Dipartimento Ingn Informaz I-43100 Parma Italy

ISBN: (纸本)9781479960781

One of the most important features for any intelligent ground vehicle is based on how is reliable and complete the perception of the environment and the capability to discriminate what an obstacle is. Obstacle Detection (OD) is one of the most widely discussed topics in literature. Many approaches have been presented for different application fields and scenarios;in last years most of them have been revisited using stereo vision or 2D/3D sensor technologies. In this paper we present a brief survey about Obstacle Detection techniques based on stereo vision for intelligent ground vehicles, describing and comparing the most interesting approaches. In order to provide a generic overview of these techniques, it has been decided to focus the study only on the algorithms that have provided a major contribution through real-time experiments in unsupervised scenarios.

关键词： object detection road vehicles stereo image processing traffic engineering computing 2D sensor technologies 3D sensor technologies autonomous ground vehicles intelligent ground vehicle real-time obstacle detection stereo vision Equations Estimation Mathematical model Real-time systems Roads Stereo vision three-dimensional displays

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A Genetically Optimized Graph-based People extraction method for embedded transportation systems real conditions 17

A Genetically Optimized Graph-based People extraction method...

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IEEE 17th International conference on Intelligent Transportation systems (ITSC)

作者： Coniglio, Christophe Meurie, Cyril Lezoray, Olivier Berbineau, Marion Univ Lille Nord France F-59000 Lille France IFSTTAR LEOST F-59650 Villeneuve Dascq France Normandie Univ UNICAEN ENSICAEN GREYC UMR CNRS 6072 Caen France

ISBN: (纸本)9781479960781

In this paper, we present a new method for people extraction in complex transport environments. Many background subtraction methods exist in the literature but don't give satisfactory results on complex images acquired in moving trains that include several locks such as fast brightness changes, noise, shadow, scrolling background, etc. To tackle this problem, a new method for people extraction in images is proposed. It is based on an image superpixel segmentation coupled with graph cuts binary clustering, initialized by a state-of-the-art foreground detection method. the proposed strategy is composed of four blocks. A pre-processing block that uses filters and colorimetric invariants to limit the presence of artifacts in images. A foreground detection block that enables to locate moving people in images. A post-treatment block that removes shadow regions of no-interest. A people extraction block that segments the image into SLIC superpixels and performs a graph cut binary clustering to precisely extract people. Tests are realized on a real database of the BOSS European project and are evaluated with the standard F-measure criteria. Since many state-of-the-art methods can be considered in our three first blocks along with many associated parameters, a genetic algorithm is used to automatically find the best methods and parameters of the proposed approach.

关键词： feature extraction genetic algorithms graph theory image colour analysis image segmentation statistical analysis traffic engineering computing transportation BOSS European project SLIC superpixel background subtraction method colorimetric invariant embedded transportation system foreground detection method genetic algorithm genetically optimized graph graph cuts binary clustering image superpixel segmentation people extraction method standard F-measure Brightness Databases Genetic algorithms image color analysis image segmentation Noise Optimization image colour analysis Genetic Algorithm (GA) image segmentation brightness inspection methods Graph theory Database Feature extraction Noise transport transport system Persons images engineering computing Statistical methods

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Plenoptic camera image simulation for reconstruction algorithm verification 17

Plenoptic camera image simulation for reconstruction algorit...

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17th conference of Novel Optical systems Design and Optimization

作者： Schwiegerling, Jim Univ Arizona Coll Opt Sci Tucson AZ 85721 USA Univ Arizona Dept Ophthalmol & Vis Sci Tucson AZ 85721 USA

ISBN: (纸本)9781628412208

Plenoptic cameras have emerged in recent years as a technology for capturing light field data in a single snapshot. A conventional digital camera can be modified with the addition of a lenslet array to create a plenoptic camera. Two distinct camera forms have been proposed in the literature. the first has the camera image focused onto the lenslet array. the lenslet array is placed over the camera sensor such that each lenslet forms an image of the exit pupil onto the sensor. the second plenoptic form has the lenslet array relaying the image formed by the camera lens to the sensor. We have developed a raytracing package that can simulate images formed by a generalized version of the plenoptic camera. Several rays from each sensor pixel are traced backwards through the system to define a cone of rays emanating from the entrance pupil of the camera lens. Objects that lie within this cone are integrated to lead to a color and exposure level for that pixel. To speed processing three-dimensional objects are approximated as a series of planes at different depths. Repeating this process for each pixel in the sensor leads to a simulated plenoptic image on which different reconstruction algorithms can be tested.

关键词： Plenoptic Light field Depth of field image simulation

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