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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Hyperspectral Data Analysis for Arid Vegetation Species : Smart & Sustainable Growth

Hyperspectral Data Analysis for Arid Vegetation Species : Sm...

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International conference on Computing, Communication, and Intelligent systems (ICCCIS)

作者： S. L. Borana S.K. Yadav S.K. Parihar DL Jodhpur India Dept. of Mining JNV University Jodhpur India

ISBN: (纸本)9781728148274

Hyperspectral images are continuous narrow spectral bands provides a wealth of information which can be used in different applications. Advance developments in hyperspectral remote sensing technology since two decade have opened new opportunity to explore innovative ways to study vegetation species. In this research work, ground-based AISA Vis-NIR hyper spectral image system of 240 bands, wavelength range from 390 to 960 nm with 2.5 nm spectral resolution and 1 cm spatial resolution at a distance of 10 m was used for classification of prominent vegetation species (Cactus, Neem and Babool). Machine learning supervised classification algorithms are used to classifying the Hyperspectral data. In supervised classification, four methods have been used viz. Spectral Angle Mapper (SAM), Minimum Distance (MD), Support Vector Machine (SVM) and Spectral Information Divergence (SID) Classifier. Environment of Visualize images (ENVI) software is used for processing and analysis of hyperspectral images for classification of vegetation species in Jodhpur study area. Accuracy assessments were also carried out for classified output images and estimate the performance of a classifier. The overall accuracy for SVM classification algorithm is best (81.2%) when 237 hyperspectral bands were used and SAM classification algorithm has provided a better overall accuracy (76.6%) when maximum noise function (MNF) 11 bands were used. This research demonstrated the efficient use of contiguous fine bands of Hyperspectral data in discrimination and classification of vegetation species.

关键词： Hyperspectral image MNF Classification Remote sensing Accuracy Vegetation Species Taxonomy Data analysis hyperspectral imagery Sorting algorithm remote sensing Support Vector Network

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Accelerating Existing Non-Blind image Deblurring Techniques through a Strap-On Limited-Memory Switched Broyden Method

Accelerating Existing Non-Blind Image Deblurring Techniques ...

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15th IAPR International conference on Machine Vision Applications (MVA)

作者： Lahouli, Ichraf Haelterman, Robby Degroote, Joris Shimoni, Michal De Cubber, Geert Attia, Rabah Royal Mil Acad Brussels Belgium Royal Mil Acad Dept Mech Brussels Belgium Mil Acad Tunisia VRIT Res Grp Nabeul Tunisia Tunisia Polytech Sch SERCOM Lab Carthage Tunisia Univ Ghent Dept Flow Heat & Combust Mech Ghent Belgium

Video surveillance from airborne platforms can suffer from many sources of blur, like vibration, low-end optics, uneven lighting conditions, etc. Many different algorithms have been developed in the past that aim to recover the deblurred image but often incur substantial CPU-time, which is not always available on-board. This paper shows how a "strap-on" quasi-Newton method can accelerate the convergence of existing iterative methods with little extra overhead while keeping the performance of the original algorithm, thus paving the way for (near) real-time applications using on-board processing.

关键词： image deblurring quasi-Newton limited-memory switched Broyden method

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Discrete Configuration Space Methods for Determining Modular Connector Area of Acceptance in Higher Dimensions

Discrete Configuration Space Methods for Determining Modular...

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25th IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Eckenstein, Nick Yim, Mark Univ Penn GRASP Lab Philadelphia PA 19104 USA Univ Penn Dept Mech Engn & Appl Mech Philadelphia PA 19104 USA

ISBN: (纸本)9781538680940

Physical connectors with self-aligning geometry aid in the docking process for many robotic and automatic control systems such as robotic self-reconfiguration and air-to-air refueling. This self-aligning geometry provides a wider range of acceptable error tolerance in relative pose between the two rigid objects, increasing successful docking chances. We present a new method for computing the error range (or area of acceptance) for a pair of rigid connector objects with self-aligning geometry capable of higher dimensional analysis which was previously limited to three. The method is based on the configuration space obstacle model, which gives us a representation of the space of contact states between the two objects. Using an approach direction as analogous to gravity, and assuming the target docked configuration is stable, the set of misaligned points that lead to docking is the target configuration's watershed for an arbitrarily dimensioned configuration space obstacle. It is well known that the watershed of a height map on a discrete grid can be found using any number of algorithms from image segmentation. We present an implementation based on Meyer's flooding algorithm to determine this watershed and measure the AA for simple connectors in 2D and 3D. Results are presented for systems including unconstrained motion in SE(2) and motion constrained to four dimensions (ie. x, y, z, pitch) in SE(3).

关键词： Connectors Geometry Robots Contacts Three-dimensional displays Two dimensional displays image segmentation

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Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications 31

Ultra Power-Efficient CNN Domain Specific Accelerator with 9...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sun, Baohua Yang, Lin Dong, Patrick Zhang, Wenhan Dong, Jason Young, Charles Gyrfalcon Technol Inc 1900 McCarthy Blvd Milpitas CA 95035 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks (CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size should all be taken into consideration for mobile and embedded applications. Domain Specific Architecture (DSA) for CNN is the efficient and practical solution for CNN deployment and implementation. We designed and produced a 28nm Two-Dimensional CNN-DSA accelerator with an ultra power-efficient performance of 9.3TOPS/ Watt and with all processing done in the internal memory instead of external DRAM. It classifies 224x224 RGB image inputs at more than 140fps with peak power consumption at less than 300mW and an accuracy comparable to the VGG benchmark. The CNN-DSA accelerator is reconfigurable to support CNN model coefficients of various layer sizes and layer types, including convolution, depth-wise convolution, short-cut connections, max pooling, and ReLU. Furthermore, in order to better support real-world deployment for various application scenarios, especially with low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also designed algorithms to fully utilize the CNN-DSA accelerator efficiently by reducing the dependency on external accelerator computation resources, including implementation of Fully-Connected (FC) layers within the accelerator and compression of extracted features from the CNN-DSA accelerator. Live demos with our CNN-DSA accelerator on mobile and embedded systems show its capabilities to be widely and practically applied in the real world.

关键词： Engines Convolution Hardware Power demand Integrated circuits Process control image coding

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Perspective Correcting Visual Odometry for Agile MAVs using a Pixel Processor Array

Perspective Correcting Visual Odometry for Agile MAVs using ...

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25th IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Greatwood, Colin Bose, Laurie Richardson, Thomas Mayol-Cuevas, Walterio Chen, Jianing Carey, Stephen J. Dudek, Piotr Bristol Robot Lab Bristol Avon England Univ Bristol Fac Engn Aerosp & Comp Sci Bristol Avon England Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9781538680940

This paper presents a visual odometry approach using a Pixel Processor Array (PPA) camera, specifically, the SCAMP-5 vision chip. In this device, each pixel is capable of storing data and performing computation, enabling a variety of computer vision tasks to be carried out directly upon the sensor itself. In this work the PPA performs HDR edge detection, perspective correction and image alignment based odometry, allowing the position and heading of a MAV to be tracked at several hundred frames per second. We evaluate our PPA based approach by direct comparison with a motion capture system for a variety of trajectories. These include rapid accelerations that would incur significant motion blur at low frame rates, and lighting conditions that would typically lead to under or over exposure of image detail. Such challenging conditions would often lead to unusable images when relying on traditional image sensors.

关键词： Visual odometry Cameras Robot sensing systems Arrays Parallel processing Visualization Performance evaluation

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Characterizing Hyperspectral Data Layouts: Performance and Energy Efficiency in Embedded GPUs for PCA-based Dimensionality Reduction

Characterizing Hyperspectral Data Layouts: Performance and E...

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conference on Design of Circuits and Integrated Circuits (DCIS)

作者： Jaime Sancho Aragón Sergio Sánchez Ramírez Raquel Lazcano López Daniel Madroñal Quintín Rubén Salvador Perea Eduardo Juárez Martínez César Sanz Álvaro Universidad Politécnica de Madrid (UPM) Research Center on Software Technologies and Multimedia Systems

The increasing demand for HyperSpectral Imaging (HSI) applications has pushed the appearance of new, smaller and more affordable HS cameras. Their adoption in embedded systems with real-time and energy constraints open research questions given the huge amounts of data involved. Traditionally, this issue is tackled with dimensionality reduction algorithms as Principal Component Analysis (PCA). In this regard, a highly impacting factor in performance is the data layout used to store the 3D HS image into a 1D memory. In this paper, the impact of using the three main HS data layouts (BSQ, BIP and BIL) is analyzed in terms of performance and energy efficiency for two PCA methods in the embedded NVIDIA Jetson TX1 GPU-based accelerator. Results show the bottlenecks found for each layout along with architectural insights that explain the observed behaviours. Consequently, we provide a set of recommendations to select a suitable PCA method for best performance and energy efficiency, depending on the number of principal components required. Additionally, we observe how different orderings with similar results account for a much different developer's productivity given the involved coding complexity in each case.

关键词： Principal component analysis Jacobian matrices Layout Graphics processing units Dimensionality reduction Iterative methods Memory management

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Deep 3D Convolutional Neural Network Architectures for Alzheimer's Disease Diagnosis 10th

Deep 3D Convolutional Neural Network Architectures for Alzhe...

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10th Asian conference on Intelligent Information and Database systems (ACIIDS)

作者： Karasawa, Hiroki Liu, Chien-Liang Ohwada, Hayato Tokyo Univ Sci Dept Ind Adm Tokyo Japan Natl Ciao Tung Univ Dept Ind Engn & Management Hsinchu Taiwan

ISBN: (纸本)9783319754178;9783319754161

Dementia has become a social problem in the aging society of advanced countries. Currently, 46.8 million people have dementia worldwide, and that figure is predicted to increase threefold to 130 million people by 2050. Alzheimer's disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase dramatically in the future, due to the increasing number of patients as a result of the aging society. However, it is still very difficult to cure AD;thus, the detection of AD is crucial. This study proposes the use of machine learning to detect AD using brain image data, with the goal of reducing the cost of diagnosing and caring for AD patients. Most machine learning algorithms rely on good feature representations, which are commonly obtained manually and require domain experts to provide guidance. Feature extraction is a time-consuming and labor-intensive task. In contrast, the 3D Convolutional Neural Network (3DCNN) automatically learns feature representation from images and is not greatly affected by image processing. However, the performance of CNN depends on its layer architecture. This study proposes a novel 3DCNN architecture for MRI image diagnosis of AD.

关键词： Alzheimer's disease diagnosis 3D Convolutional Neural Network Deep residual network image processing Machine learning

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Secure QR-Code Based Message Sharing System Using Cryptography and Steganography 1

Secure QR-Code Based Message Sharing System Using Cryptograp...

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1st International conference on Secure Cyber Computing and Communications (ICSCCC)

作者： Mendhe, Abhijeet Gupta, Deepak Kumar Sharma, Krishna Pal Dr BR Ambedkar Natl Inst Technol Comp Sci & Engn Jalandhar Punjab India

ISBN: (纸本)9781538663738

Many cryptographic techniques are available for serving the purpose of information security over the web, servers and local systems. However, there is always demand of more security which may not be meet by such cryptographic algorithms alone because of known security attacks and mathematical complexity. Thus visualizing the strategic combination of cryptography and steganography techniques can provide a higher level of security. Quick Response (QR) codes are used extensively due to their beneficial characteristics. It includes robustness, readability, error correction capability, large data capacity than traditional barcodes etc. Thus, in this work, we propose a 3-layered architecture for securing message sharing mechanism by using QR code image in one layer. This architecture utilizes the empirical and strategic use of cryptography and steganography techniques. The proposed system provides the higher level of security on the basis of quantitative and qualitative results. Also, we evaluate our system against the performance evaluation criteria discussed in the paper.

关键词： Cryptography image Steganography 3DES RSA AES QR Codes

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Parallelizing and optimizing LHCb-Kalman for Intel Xeon Phi KNL processors 26

Parallelizing and optimizing LHCb-Kalman for Intel Xeon Phi ...

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26th Euromicro International conference on Parallel, Distributed, and Network-Based processing (PDP)

作者： Fernandez, Placido del Rio Astorga, David Dolz, Manuel F. Fernandez, Javier Awile, Omar Garcia, Daniel CERN EP LBC CH-1211 Geneva 23 Switzerland Univ Carlos III Madrid Dept Comp Sci Madrid Spain CERN IT CF FPP CH-1211 Geneva 23 Switzerland

ISBN: (纸本)9781538649756

Real time data processing is an important component of particle physics experiments with large computing resource requirements. As the Large Hadron Collider (LHC) at CERN is preparing for its next upgrade the LHCb experiment is upgrading its detector for a 30x increase in data throughput. In preparation for this upgrade the experiment is considering a number of architectural improvements encompassing both its software and hardware infrastructure. One of the hardware platforms under consideration is the Intel Xeon-Phi Knights Landing processor. Thanks to its on-package high-bandwidth memory and many-core architecture it offers an interesting alternative to more traditional server systems. We present a scalable, multi-threaded and NUMA-aware Kalman filter proto-application for particle track fitting expressed in terms of generic parallel patterns using the GRPPI interface. We show how code maintainability and readability improves, while maintaining comparable levels of performance to the baseline implementation. This is achieved by keeping the parallel algorithms in the underlying framework generic, but topology aware through the use of the Portable Hardware Locality (hwloc) library, which allows us to target different architectures with the same program. We measure the performance of our topology-aware GRPPI Kalman filter implementation on the Intel Xeon-Phi Knights Landing platform and conclude on the feasibility of integrating such high-level parallelization libraries in complex software frameworks such as LHCb's Gaudi framework.

关键词： NUMA affinity LHCb parallel patterns GRPPI Kalman filter knl multi-threading xeon phi

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Deep tracking using convolutional features and adaptive frame update 8

Deep tracking using convolutional features and adaptive fram...

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8th IEEE International conference on Consumer Electronics - Berlin, ICCE-Berlin 2018

作者： Kim, Yeongbin Park, Hasil Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781538660959

This paper presents a robust tracking algorithm using convolutional features. The proposed tracking algorithm consists of three steps: I) training correlation filters using features extracted by a convolutional neural network, ii) localizing the target position in the next frame by multiplying weight and correlation response of each layer, and iii) re-training correlation filters every 10-frame using the proposed position error score. Experimental results show that the proposed algorithm outperforms an existing deep learning-based tracker. © 2018 IEEE.

关键词： Convolution

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