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

共 15687 条记录，以下是4001-4010 订阅

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A Novel Forgery Detection Algorithm Based on Mantissa Distribution in Digital images

A Novel Forgery Detection Algorithm Based on Mantissa Distri...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Arman Parnak Yasser Baleghi Javad Kazemitabar Faculty of Electrical & Computer Engineering Babol Noshirvani University of Technology Babol Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

Nowadays, digital image forgery detection is one of important topics in research world. In this paper, we propose a novel forgery detection algorithm using the logarithmic basis of Benford's law which states the mantissa of the logarithm of all practical numbers should be uniformly distributed. Based on this fact, the proposed method uses extracted features from mantissa distribution of discrete cosine transform (DCT) coefficients in JPEG images. Support vector machine (SVM) is used for classification to detect authentic and forged images based on these features. Results show that our proposed algorithm has the highest mean accuracy (99.78%), sensitivity (99.77%) and specificity (99.79%) in comparison with previous works on CASIA V1.0 dataset.

关键词： Support vector machines Digital images Signal processing algorithms Feature extraction Forgery Discrete cosine transforms Detection algorithms

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Pollen Grain Classification using Geometrical and Textural Features

Pollen Grain Classification using Geometrical and Textural F...

引用

IEEE International conference on Imaging systems and Techniques (IST) . IEEE International School on Imaging

作者： Manikis, G. C. Marias, K. Alissandrakis, E. Perrotto, L. Savvidaki, E. vidakis, N. Fdn Res & Technol Hellas Inst Comp Sci Computat Biomed Lab Iraklion Greece Hellen Mediterranean Univ Sch Agr Sci Dept Agr Lab Food Safety & Qual Iraklion Greece Enseirb Matmeca Dept Telecommun Bordeaux France Hellen Mediterranean Univ Dept Elect & Comp Engn Iraklion Greece

ISBN: (纸本)9781728138688

This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images. Pollen grain classification has received notable attention concerning a wide range of applications such as paleontology and honey certification, forecasting of allergies caused of airborne pollen and food technology. It requires an extensive qualitative process that is mostly performed manually by an expert. Although manual classification shows satisfactory performance, it may suffer from intra and inter-observer variability and it is time consuming. This study benefits from the advances of image processing and machine learning and proposes a fully-automated analysis pipeline aiming to: a) calculate morphological characteristics from the images using a cost-effective microscope, and b) classify images into 6 pollen classes. A private dataset from the Department of Agriculture of the Hellenic Mediterranean University in Crete containing 564 images was used in this study. A Random Forest (RF) classifier was utilized to classify images. A repeated nested cross-validation (nested-CV) schema was used to estimate the generalization performance and prevent overfitting. image preprocessing, extraction of geometric and textural characteristics and feature selection were implemented prior to the assessment of the classification performance and a mean accuracy of 88.24% was reported.

关键词： Random Forests Multiclass Classification image processing Pollen Grain images Texture Analysis Microscopy images

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Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization

Active Sampling for Pairwise Comparisons via Approximate Mes...

引用

International conference on Pattern Recognition

作者： Aliaksei Mikhailiuk Clifford Wilmot Maria Perez-Ortiz Dingcheng Yue Rafał K. Mantiuk University of Cambridge Cambridge United Kingdom University College London London United Kingdom

Pairwise comparison data arise in many domains with subjective assessment experiments, for example in image and video quality assessment. In these experiments observers are asked to express a preference between two conditions. However, many pairwise comparison protocols require a large number of comparisons to infer accurate scores, which may be unfeasible when each comparison is time-consuming (e.g. videos) or expensive (e.g. medical imaging). This motivates the use of an active sampling algorithm that chooses only the most informative pairs for comparison. In this paper we propose ASAP, an active sampling algorithm based on approximate message passing and expected information gain maximization. Unlike most existing methods, which rely on partial updates of the posterior distribution, we are able to perform full updates and therefore much improve the accuracy of the inferred scores. The algorithm relies on three techniques for reducing computational cost: inference based on approximate message passing, selective evaluations of the information gain, and selecting pairs in a batch that forms a minimum spanning tree of the inverse of information gain. We demonstrate, with real and synthetic data, that ASAP offers the highest accuracy of inferred scores compared to the existing methods. We also provide an open-source GPU implementation of ASAP for large-scale experiments.

关键词： Message passing Graphics processing units Approximation algorithms Sampling methods Real-time systems Computational efficiency Quality assessment

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The Development of a Multi-Band Handheld Fusion Camera 16

The Development of a Multi-Band Handheld Fusion Camera

引用

conference on Electro-Optical and Infrared systems - Technology and Applications Xvi

作者： Hickman, Duncan L. Tektonex Ltd Argyll Rd Kilcreggan Argyll Scotland Tektonex Ltd Argyll Rd Kilcreggan Bute Scotland

ISBN: (数字)9781510630222

ISBN: (纸本)9781510630222

A longwave infrared (LWIR) handheld surveillance camera has been modified through the addition of a second sensor which provides both visible (RGB) and near-infrared (NIR) image streams. The challenges and constraints imposed on the development process of this Handheld Fusion Camera (HHFC) are described, and the approach to the dual and tri-band image fusion processing schemes is presented. The physical characteristics of the existing camera acted as a major constraint on the HHFC design with the Size, Weight, and Power (SWaP) requirements restricting the choice of both the additional sensor as well as the processor engine available within the camera. The primary use of the HHFC is in ground-based security and surveillance operations which is challenging in terms of variability in the scene content. Establishing an effective processing architecture is critical to both image interpretability by the user, and operational effectiveness. The HHFC allows the user to view different image streams including enhanced single-band image data as well as both dual and tri-band fused imagery. Such flexibility allows the user to select the best imagery for their immediate requirements. Power consumption and latency figures have been minimised by the use of relatively simple arithmetical fusion algorithms combined with an Adaptive Weight Map (AWM) for regional-based optimisation. In practice, the potential performance gain achieved is necessarily limited by the required performance robustness, and this trade-off was critical to the HHFC design and the final image processing solution.

关键词： Tri-band image fusion multispectral camera adaptive weight map handheld fusion camera multi-sensor camera design surveillance system image enhancement colour imagery real-time image processing

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Pedestrian Detection and Classification for Autonomous Train

Pedestrian Detection and Classification for Autonomous Train

引用

International image processing, Applications and systems conference (IPAS)

作者： Ankur Mahtani Wael Ben-Messaoud Abdelmalik Taleb-Ahmed Smail Niar Clément Strauss FCS Railenium Famars France IEMN Valenciennes France

ISBN: (数字)9781728175744

ISBN: (纸本)9781728175751

In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) The creation of a new dataset for workers wearing orange vests in a railway environment context. (b) A deep learning supervised YOLO object detector for persons detection combined with a linear SVM (Support Vector Machine) classifier for persons classification into workers wearing orange vests or travelers. (c) A realtime vision-based technique for the environment monitoring in a driverless train application. Experimental results evaluate the parameters of our two stages detection approach and show that our algorithm is robust in detecting and classifying railway workers for a real-time implementation on an embedded system. Our implementation on an embedded system allows a detection with a correct classification rate of 98.5 % of accuracy and a classification time of 1 ms per frame.

关键词： Support vector machines Location awareness Embedded systems Rail transportation Real-time systems Classification algorithms Monitoring

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Neural Network-based Prediction of visual Quality for Noisy images 15

Neural Network-based Prediction of Visual Quality for Noisy ...

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15th IEEE International conference on the Experience of Designing and Application of CAD systems (CADSM)

作者： Rubel, Andrii Rubel, Oleksii Lukin, Vladimir Natl Aerosp Univ KhAI Dept Informat & Commun Technol Kharkiv Ukraine

ISBN: (纸本)9781728100531

Noise is one of the main factors that degrade image visual quality. Assessment of perceptual quality of noisy images is of critical importance for imaging systems and image processing application. In this paper, we propose a framework that employs multi-layer neural networks to predict visual quality for noisy images. In particular, the proposed method utilizes quite simple features extracted in discrete cosine transform (DCT) domain for predicting values of full-reference visual quality metrics for noisy images. The neural networks are trained and tested on noisy images corrupted by additive white Gaussian noise (AWGN). The experimental results show that prediction is quite accurate and fast. Source codes of the proposed method and datasets will be available at https://***/viA-RiVaL/Blind-Noisy-image-visual-Quality-Prediction.

关键词： image quality artificial neural networks multilayer perceptrons prediction algorithms additive white Gaussian noise (AWGN) blind assessment of noisy image quality

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Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networks

Improving the Successful Robotic Grasp Detection Using Convo...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Hamed Hosseini Mehdi Tale Masouleh Ahmad Kalhor Electrical & Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

Robotic grasp should be carried out in a real-time manner by proper accuracy. Perception is the first and significant step in this procedure. This paper proposes an improved pip line model trying to detect grasp as a rectangle representation for different seen or unseen objects. It helps the robot to start control procedures from nearer to the proper part of the object. The main idea consists in the pre-processing, output normalization, and data augmentation to improve accuracy by 4.3 percent without making the system slow. Also, a comparison has been conducted over different pre-trained models like AlexNet, ResNet, Vgg19, which are the most famous feature extractors for image processing in object detection. Although AlexNet has less complexity than other ones, it outperformed them, which helps the real-time property.

关键词： Training Signal processing algorithms Robot sensing systems Feature extraction Real-time systems Time measurement Time factors

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A Design of Mobile Partition Piles System in Urban Road

A Design of Mobile Partition Piles System in Urban Road

引用

IEEE International conference on Power, Intelligent Computing and systems (ICPICS)

作者： Hang Yuan Shangshang Nie Fahai Zhong Ronghui Luo School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198750

As it is known to all, more and more traffic pressure lead to traffic jams. Sometimes one-way traffic jams happened in two-way streets. This system is designed to solve this problem. It is a mobile partition piles system, which contains three parts. They are information acquisition module, image processing module and automatic control module. This system can monitor the condition of roads in real time by using machine vision technology. It can also move partition piles according to the traffic condition, which can ease traffic jams. Besides, partition piles will be back to original position when the problem is solved. The hardware and software of control system and mechanical structure has been accomplished. Several tests verified the characteristics of this system, such as distinguish accurately, react rapidly, high intellect, low cost and run stably.

关键词： Roads Control systems Cameras Partitioning algorithms Wireless communication image processing Real-time systems

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Benefit and cost analysis for utilizing machine learning in geospatial projects

Benefit and cost analysis for utilizing machine learning in ...

引用

4th Smart Cities Symposium (SCS 2021)

作者： E. A. Alburshaid M. A. Mangoud National Space Science Agency P.O. Box 51115 Kingdom of Bahrain College of Engineering University of Bahrain Sukhair Campus P.O. Box 32028 Kingdom of Bahrain

This paper aims to study the feasibility of applying deep learning techniques in the geospatial application domain. Two geospatial projects were economically analysed. The first project was to produce land use maps from satellite imagery for the current and past years using three machine learning algorithms. Based on the produced historical maps, scenario predictions of the land use maps were generated [1]. The second geospatial project used a deep learning object detection technique to count the total numbers of palm trees automatically and develop a geospatial database that has the exact coordinate location per each palm tree distributed in the Kingdom of Bahrain [2]. The technical, administrative, and financial costs were identified as well as the benefit in the monetary value per each project. As a result, benefit-cost analysis was conducted to evaluate both projects economically.

关键词： cost-benefit analysis geographic information systems land use planning object detection remote sensing visual databases geophysical image processing smart cities deep learning (artificial intelligence)

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Aplicaions of Digital Signal processing in all sciences

Aplicaions of Digital Signal Processing in all sciences

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International conference on Advanced Science and Engineering (ICOASE)

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Digital signal processing is the use of digital processing tools and processors such as computers or microprocessor manufactured in a special purposes for signlas processing. The processing is based on dealing with the sampled signals after the inputing the signal into samplers and *** amplitudes of these samples represents the amplitudes of these signals in the required intanses. Digital signal processing and analog one are considered as subfields of signal processing and there applications are in audio and speech processing ,array signal proseeing in sonar and radar . Also image processing,encoding,compression and adaptive techniques also are considered as a parts of signal processing. However control systems and telecommunications have so many operations based on signal processing algorithms. Nowadays avery big field of applications of signal processing in biomedical engineering and seismology. In our researches nowadays also,we apply the signal processing besed on FFT and eigenstructure decomposition in identification and verifications.

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