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

共 15618 条记录，以下是4101-4110 订阅

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Preliminary results of a proposed CNN framework for use in motorway applicable detection systems

Preliminary results of a proposed CNN framework for use in m...

引用

South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media conference (SEEDA-CECNSM)

作者： George Sisias Sotirios Kontogiannis Myrto Konstantinidou Michael F. Dossis National Centre for Scientific Research Demokritos Aghia Paraskevi Athens Greece Laboratory Team of Distributed MicroComputer Systems University of Ioannina Ioannina Greece Dept. of Computer Science University of Western Macedonia Kastoria Greece

ISBN: (数字)9781728164458

ISBN: (纸本)9781728164465

This paper presents the preliminary results of the first stage of a proposed framework of existing image detection convolutional algorithms, and sets the image portable detector requirements, focusing on portable detection devices. Such devices perform image segmentation and transmit monitoring meta-data. Four pre-trained CNNs have been used to classify images of vehicles passing through toll posts, and the results are presented and compared. At subsequent stages of the presented framework certain types of vehicles will be detected and categorized, based on the type of cargo they carry. The proposed framework is wrapped by an appropriate system architecture which interacts with incident response or facility management systems, and tries to achieve precise speed/memory/accuracy balance for each targeted detection application accordingly. To this end, various ways are investigated and evaluated, to trade accuracy for speed and memory usage in modern mobile convolutional algorithm driven detection systems monitoring specific regions of interest. Such tradeoffs provide increased detection capabilities to incident response systems that interact with such detectors in real-time, or in post-processing with facility management systems for accurate and automated decisions adaptation.

关键词： Convolutional neural networks image segmentation distributed and embedded systems incident response systems facility management systems image segmentation Detection systems incident response vehicle Facilities Management image detection detection device preliminary data Frameworks Freeway trees (mathematics) cargo

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Signature image Improvement with Gradient Adaptive Morphology 18th

Signature Image Improvement with Gradient Adaptive Morpholog...

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18th International conference on Computer Information systems and Industrial Management

作者： Sarnacki, Kacper Adamski, Marcin Saeed, Khalid Warsaw Univ Technol Warsaw Poland Bialystok Tech Univ Bialystok Poland

ISBN: (纸本)9783030289560;9783030289577

The paper presents a method for improving signature images, by using a directional field guided morphology. The method adapts a circular or linear structural element and its orientation. There is presented the comparison of the algorithm with the results of binarization process and with the existing post-processing algorithms, such as the morphological opening, closing and median filtering. The experiments show that the authors' method significantly improves the quality of images by removing unwanted artifacts.

关键词： Signature quality improvement Gradient adaptive morphology Directional field

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Hand Gesture Recognition system for physically challenged people using IoT

Hand Gesture Recognition system for physically challenged pe...

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International conference on Advanced Computing and Communication systems (ICACCS)

作者： Rajit Nair Dileep Kumar Singh Ashu Shivam Yadav Sourabh Bakshi Jagran Lakecity University Bhopal India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

One of the rising fields of research are hand gesture recognition. Being an important part of non-verbal interaction is a critical part of our everyday life. Hand management recognition systems allow us to communicate with the machine, which is more familiar to the human being, creative, normal, user-friendly. Hand gesture recognition covers a wide range of applications including contact with the human machine, sign language, immersive game technology etc. To perform this we have implemented a method of hand recognition and extraction of features by means of a web camera in real time. The picture is captured in this method through the system's webcam. The initial processing of the input image and thresholding are used to remove image noise and smooth the image. Following this, apply region filling in the gesture or object of interest to fill holes. The “HU moments” are used for feature extraction and classification using KNN algorithm. This helps in improving the classification though machine learning and recognition step. We have tested 43 gestures which include 26 gestures of English alphabets and achieved a confidence of 99.9%.

关键词： Feature extraction Gesture recognition Classification algorithms Computers Training Communication systems Assistive technology

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image processing and machine learning applications in mining industry: Mine 4.0

Image processing and machine learning applications in mining...

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Intelligent systems and Advanced Computing Sciences (ISACS),International conference on

作者： Hamid Ouanan El Hassan Abdelwahed National School of Applied Sciences Sultan Moulay Slimane University Beni Mellal Morocco CSEHS Mohammed VI Polytechnic University Marrakesh Morocco

ISBN: (数字)9781728148137

ISBN: (纸本)9781728148144

Recently, image processing (IP) and Machine learning (ML) algorithms have been successfully used in a wide variety of industry sectors. In this paper, we first provide mining engineers with the state of the art about IP and ML applications in the mining industry. furthermore, we propose a real time application for froth image analysis in the context of mineral processing. Finally, we propose some future directions, including suggestions on mining data collection and development of an exploration tool based on IP and ML algorithms by taking advantage of cloud computing technology to analyze geoscience data in order identify target areas with high prospectively potential.

关键词： Minerals Machine learning algorithms Data mining Machine learning Machine vision Materials processing

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The Design and Implementation of an image processing Framework with a Graphical Programming Interface for Low-Ended FPGAs 4

The Design and Implementation of an Image Processing Framewo...

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4th IEEE International conference on image, vision and Computing, ICIVC 2019

作者： Narkthong, Nuntipat Prom-On, Santitham King Mongkut's University of Technology Thonburi Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9781728123257

image processing and computer vision algorithms are very computationally intensive and could not be implemented on low power microcontroller used in embedded and small IoT devices. Performing computation on the cloud is not practical for always-on real-time operations and deploying a high-end microcontroller or a microprocessor consume too much power and are generally too expensive to fit into these small systems. In this paper, we propose a new programming workflow using reusable hardware modules and a graphical programming interface for implementing a complete image processing system on an FPGAs which overcomes the steep learning curve of tradition FPGAs design tool. The design can be deployed onto the low-ended FPGAs in a similar price point as a mid-range microcontroller. To demonstrate our proposed framework, we have implemented a number of image transformation operations on an FPGAs development board with Lattice iCE40 Ultra Plus FPGA and a tiny camera module. Results have shown that our designs can fit in a low-ended FPGAs while performing 5.5-345x faster and consuming 2.4-4x less power compared to current state-of-the-art microcontroller used in small embedded and IoT devices. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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The real-time reliable detection of the horizon line on high-resolution maritime images for unmanned surface-vehicle

The real-time reliable detection of the horizon line on high...

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International conference on Cyberworlds

作者： Uuganbayar Ganbold Takuya Akashi Graduate School of Engineering Iwate University Iwate Japan Faculty of Science and Engineering Iwate University Iwate Japan

ISBN: (数字)9781728164977

ISBN: (纸本)9781728164984

Horizon detection is useful in maritime image processing for various purposes, such as spatial orientation estimation of ship camera and detection of the significant region for post-processing. This paper proposed a novel realtime optimization-based method for detecting the horizon line in maritime images. Traditional methods defined the horizon line detection problem aim to detect the horizon line which perfectly divides the whole image into two regions as the sky and sea. Thus, the complication of traditional methods is the statistical distance metrics of distributions are calculated in the whole image by all combinations of the horizon line parameters. Moreover, traditional methods do not provide realtime processing to detect the horizon line from high-resolution images. To achieve real-time processing on high-resolution images, this study defines the local features of the horizon line using a vanishing line characteristic and the optimization criteria. In addition, the optimization process is improved by combining a genetic algorithm and a coarse-to-fine approach. Our method applied the Singapore marine dataset and the Buoy dataset. To verify the proposed method, our result is compared to the state of the art methods. We confirm the proposed method can accurately detect the horizon line under different scenarios in real-time.

关键词： Estimation Feature extraction Real-time systems Reliability Optimization videos Genetic algorithms

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Deep learning based in-loop filter for video coding 34

Deep learning based in-loop filter for video coding

引用

34th IEEE International conference on visual Communications and image processing, VCIP 2019

作者： Yu, Yue Yang, Xiuzhi Chen, Jian Huang, Bo Wu, Junyi Fuzhou University IMCL College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9781728137230

In lossy video coding, in-loop filter can improve the image reconstruction by reducing compression artifacts and distortions. Recently, several CNN-based in-loop filtering algorithms are proposed to improve HEVC. However, something deficient is still limiting their capability to further boost the coding efficiency. Specifically, things like prediction residuals and coding-unit boundaries, which is quite relevant to the compression artifacts, were usually neglected;moreover, the loss functions adopted in these methods usually haven't been regularized properly. To improve these inadequacies, we propose a deep learning based in-loop filter for HEVC to improve its rate-distortion performance. Firstly, prediction residuals, the compression of which is actually where the encoding noise directly results from, are skillfully considered to help filtering. Then, we unify a block-based measurement-reconstruction process and a neighborhood-based filtering process into one end-To-end all-convolutional architecture to catch the relevance between compression noise and the pixel's related position to the coding block boundaries. Finally, for network training, total-variation model is adopted to derive the loss function by MAP (maximum a posteriori) estimation for better regularization. Experimentally, our proposed in-loop filtering method brings average 6.5% and 4.3% BD-rate reduction under AI and RA configuration respectively. © 2019 IEEE.

关键词： image coding

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Structure relationship classification for the recognition of mathematical expression handwritten in Arabic

Structure relationship classification for the recognition of...

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International conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Ibtissem Hadj Ali Mohamed Ali Mahjoub LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingénieurs de Sousse Sousse Tunisie

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

An essential issue for the recognition of handwritten mathematical formulas is the identification of the structural relationships between each pairs of adjacent symbols that compose the entire mathematical formula. The classification of the structural relationship is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After the combination of extracted features, we classify the relationship into six different classes using four different classifiers in order to determine the most efficient. In our proposed system, a support vector machine (SVM) classifier, Random Forest, Adaboost and KNN are employed. Experimental results show that our features give promising results.

关键词： Histograms Support vector machines Handwriting recognition Feature extraction Vegetation Kernel Classification algorithms

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Review of Recent Deep Learning Based Methods for image-Text Retrieval

Review of Recent Deep Learning Based Methods for Image-Text ...

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IEEE conference on Multimedia Information processing and Retrieval (MIPR)

作者： Jianan Chen Lu Zhang Cong Bai Kidiyo Kpalma Univ Rennes INSA Rennes CNRS IETR - UMR 6164 Rennes France College of Computer Science Zhejiang University of Technology Hangzhou China

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

Cross-modal retrieval has drawn much attention in recent years due to the diversity and the quantity of information data that exploded with the popularity of mobile devices and social media. Extracting relevant information efficiently from large-scale multi-modal data is becoming a crucial problem of information retrieval. Cross-modal retrieval aims to retrieve relevant information across different modalities. In this paper, we highlight key points of recent cross-modal retrieval approaches based on deep-learning, especially in the image-text retrieval context, and classify them into four categories according to different embedding methods. Evaluations of state-of-the-art cross-modal retrieval methods on two benchmark datasets are shown at the end of this paper.

关键词： Learning systems Feature extraction Semantics Machine learning visualization Neural networks Classification algorithms

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YOLOv5 based Open-Source UAV for Human Detection during Search And Rescue (SAR)

YOLOv5 based Open-Source UAV for Human Detection during Sear...

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International conference on Advanced Computer Control, ICACC

作者： M S Sruthi Manal Jaleel Poovathingal V N Nandana S Lakshmi Mohamed Samshad V S Sudeesh Govt. Engineering College Thrissur Thrissur India ICFOSS Thiruvanathapuram India

ISBN: (纸本)9781665439206

Floods and earthquakes are among the most frequently occurring natural disasters. They account for high mortality rates due to their rapidness and uncertainty of occurrence. Inundated lands require a quick response for rapid evacuation, arresting fatalities, and consequential economic losses. People tend to seek shelter at dry and open lands at times of calamity. The manual Search and Rescue (SAR) operations have their shortcomings due to the difficulties in identifying the human presence. It requires a longer time for evacuation and therefore increased mortalities. This paper proposes a quadcopter for real-time monitoring of isolated places and automatically detecting stranded humans during floods using image processing techniques at affordable rates. Live video streaming is possible with a camera and a video transmission system attached to the quadcopter. The rescue centers automatically receive the location of humans in case of human detection. Our model integrates an Open-Source autopilot system model, APM 2.8 multicopter flight controller that efficiently stabilizes the flight, and a YOLOv5 object tracking convolutional neural network algorithm for faster detection of human beings. The model is trained using a dedicated dataset of more than 1000 images and attains 0.954 mAP. We have developed a drone using open-source hardware and software tools, conducted test flights to check its stability and the efficiency of the object detection algorithm. We also conducted a mini-survey of one of the most flood-prone areas of Thrissur district in Kerala, using Mission Planner open-source software to evaluate how quickly our drone can assess the entire area. The aim is to save more human lives by quick and efficient aerial assessment in the most cost-efficient manner.

关键词： Uncertainty Software algorithms Streaming media Cameras Real-time systems Hardware Floods

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