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

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Identification of Drowning Victims in Freshwater Bodies using Drift Prediction and image processing based on Deep Learning

Identification of Drowning Victims in Freshwater Bodies usin...

引用

International conference on Advanced Computer Control, ICACC

作者： Anjana Unnikrishnan A T Roshni P R Anusha Anju M Vinny C K Anuraj Toc H Institute of Science & Technology Ernakulam India

ISBN: (纸本)9781665439206

Year after year drowning deaths are increasing tremendously, making it the 3rd leading cause of unintentional injury deaths worldwide. Drift prediction methodology is typically not used in river ecosystems and conventional methods for human rescue do not account for feasible and faster human detection. Utilization of multiple sensor data in underwater human rescue applications can capacitate faster human detection. This paper discusses the design, implementation, and testing of such an underwater human detection system, which spots the victim drifting or drowning in freshwater ecosystems. The water flow sensor attached to this portable device can calculate drift distance to track down the victim. The ultrasonic sensor activates the underwater camera upon detecting an object, to facilitate real-time human localization. We performed real-time object detection on a custom dataset by applying DarkNet-53 pre-trained weights on YOLOv3 architecture and a mean Average Precision (mAP) of 98.0% was achieved. The system attained a detection depth of 5m. Combined action of drift distance calculator and YOLOv3 real-time detection model can speed up underwater human extrication.

关键词： Performance evaluation Location awareness Ecosystems Object detection Phase frequency detectors Prediction algorithms Real-time systems

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4th International conference on Cognitive systems and Information processing, ICCSIP 2018

4th International Conference on Cognitive Systems and Inform...

引用

4th International conference on Cognitive systems and Information processing, ICCSIP 2018

ISBN: (纸本)9789811379826

The proceedings contain 96 papers. The special focus in this conference is on Cognitive systems and Information processing. The topics include: How to Understand the Basic Graphic Analysis Method;image Elementary Manifold and Its Application in image Analysis;SAR image Fast Online ATR Based on Visual Attention and Scale Analysis;image Artistic Style Transfer Based on Color Distribution Preprocessing;depthwise Separable Convolution Feature Learning for Ihomogeneous Rock image Classification;an Efficient Network for Lane Segmentation;optimize Expert Knowledge Acquisition with Attribute Calculation: How to Understand Twin Turing Machine;an Unsupervised Joint Model for Claim Detection;how to Understand the Basic Structural Unit;a Novel Remote Sensing image Classification Method Based on Semi-supervised Fuzzy C-Means;algorithms for Maximum Lifetime of Covering Path;active Learning-Based Semi-supervised Spectral Clustering Algorithm;secondary Filtering Update Algorithm for Nonlinear systems—Based on Extended Kalman Filter;probabilistic Context-Aware Ambients;how to Understand Three Types of Cognitive Models;intelligent Measurement and Management of Aircraft Structural Deformation;Design and FPGA Implementation of an High Efficient XGBoost Based Sleep Staging Algorithm Using Single Channel EEG;how to Understand the Mathematical Basis of Two Basic Types of Computational Behavior;improved Direct Chaotic Nonlinear Filters;a Hotel Review Corpus for Argument Mining;vision-Based Joint Attention Detection for Autism Spectrum Disorders;the Formal Understanding Models;Application of PSO-LSSVM in Bias Correction of Shipborne Anemometer Measurement;two-Stage Weighted Regularized Extreme Learning Machine for Class Imbalanced Learning;covering Path with Minimum Movement: Complexity and algorithms;Intelligence’s Dilemma: AI algorithms May Cause Anti-Intelligence.

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Autonomy in robotic prostate biopsy through AI-assisted fusion

Autonomy in robotic prostate biopsy through AI-assisted fusi...

引用

International conference on Advanced Robotics (ICAR)

作者： Luigi Palladino Bogdan Maris Alessandro Antonelli Paolo Fiorini Altair Laboratory University of Verona Verona Italy Hospital Borgo Trento of Verona Verona Italy

ISBN: (纸本)9781665436854

Artificial intelligence (AI) and especially deep learning (DL) are applied in many fields and are the leading technologies towards robot autonomy. Proper DL algorithms and sufficient amount of medical data may be applied in medical robotics to increase autonomy, safety, accuracy and precision of a medical procedure. We investigate in this paper the implementation and the integration of DL techniques in a new robotic system for prostate biopsy-PROST. The DL algorithm, named PROST-Net, is a convolutional neural network (CNN) employed for the segmentation of the prostate in different types of medical images: pre-operative magnetic resonance (MRI) and intra-operative ultrasound (US). The US images come from different acquisition planes (axial and sagittal) with different alignments of the sensors (convex and linear) making the design of the CNN challenging. Tests on patient data produced an accuracy of 86% in US images and 77% in MRI and were estimated by using Dice similarity Coefficient (DC). The biopsy robot will use the output of PROST-Net for the initial fusion of pre-operative and intra-operative images to define the biopsy targets and the planning of the procedure. Real-time processing of the data with PROST-Net will empower dynamic update of the initial fusion by following the current position of the prostate with respect to the robotic reference frame.

关键词： image segmentation Ultrasonic imaging Magnetic resonance imaging Biopsy Transformers Real-time systems User experience

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Improvement of edge-tracking methods using Genetic algorithm and Neural network 5

Improvement of edge-tracking methods using Genetic algorithm...

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5th Iranian conference on Signal processing and Intelligent systems (ICSPIS)

作者： Shabankareh, Sajjad Ghazanfari Shabankareh, Saeid Ghazanfari Islamic Azad Univ Fac Elect Engn Shiraz Iran Aix Marseille Univ Fac Sci & Technol Marseille France

ISBN: (纸本)9781728153506

One of the most basic and important operations in the field of image processing is image extraction and detection. Edge recognition is very important for image clarity and image segmentation. The importance of edge detection is that the human eye can recognizes the existence of different objects by observing the edges. So it makes sense to do edge detection before interpreting images in automated systems. Edges are points in the image where the two pixels are two different values, or two values very large in numerical value. It can be said that one of the goals of edge reduction in data size in images is to preserve the original structure and shape of the images. Editing has various applications, including object recognition, segmentation and image coding, in a variety of medical images. One of the problems we encounter when editing images has noises. In this paper, a combination of several standard edge- matching algorithms, neural network and multi-objective genetic algorithm Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to edge detection. In the proposed method, we give the standard multi-edge finder to a forward propagating(FP) neural network as input. A multi-objective genetic algorithm with Non-Dominated Sorting Genetic Algorithm (NSGA-II was used to select the number of edge detectors to access the neural network and to select the most accurate ones. The genetic algorithm selects the least accurate number of edge detectors to enter the neural network. To evaluate the proposed method, TP, TN, FP, FN criteria were used to compare with other methods. Finally, by comparing the results obtained on different images using existing methods and the proposed method it is observed that the proposed method has better accuracy in detecting the edge of noise images.

关键词： Edge detection neural network multi-objective genetic algorithm with Non-Dominated Sorting Genetic Algorithm (NSGA-II) image processing

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Research and Improvement of image Encryption algorithms Based on Chaos 4

Research and Improvement of Image Encryption Algorithms Base...

引用

2019 4th International conference on Intelligent Computing and Signal processing, ICSP 2019

作者： Wang, Ruoyu Cao, Sanxing Collaborative Innovation Center Communication University of China Beijing100024 China

Due to user privacy protection and other demands, image encryption is always a hot spot in the research field. In view of these problems, this article proposes a chaotic image encryption algorithm, which is named permutation-diffusion algorithm. Specifically, the image of the pixels is permuted, so that the pixel position changes, and then spreads, to change the pixel value. It can be observed from the test results that the algorithm is better in terms of efficiency and safety, compared with the state of the art. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： image enhancement

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Protein image Classification based on Convolutional Neural Network and Recurrent Neural Network 2019

Protein Image Classification based on Convolutional Neural N...

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Proceedings of the 2019 3rd International conference on Computer Science and Artificial Intelligence

作者： Yuanying Qu Haowei Song Hai-Ning Liang Jieming Ma Wei Wang Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781450376273

Proteins are an essential component in the cell where the functions are executed to enable life. At present, the manual evaluation and classification of protein images is not practical given the current situation for generated images on a large scale. Hence, the requirement of automating protein image classification can be quite useful. Until now, classical machine learning and convolutional neural network algorithms have achieved results in image classification without the desired level of accuracy. Under the circumstances, the research aims to propose an accurate classified model for protein image classification by combining a convolutional neural network with a recurrent neural network.

关键词： image Classification Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) image processing

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Human Face Feature Extraction Based on a Partition Threshold Model

Human Face Feature Extraction Based on a Partition Threshold...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Yuqing Tian Siyu Wang Sheng Bi Ning xi The University of Hong Kong Pokfulam Hong Kong China

ISBN: (数字)9781728190105

ISBN: (纸本)9781728190112

image feature extraction technology has been applied to all aspects of our lives, such as science, education, aerospace, national defence, and industrial production. For example, a painting robot requires a face extraction method that meets human aesthetic standards. This paper mainly aims to solve the problem of conditional binary extraction of the human face against changeable and complex backgrounds, and under different lighting environments, in order to achieve better human face image feature extraction and higher image processing speeds - the average time to process one image is 3.3 seconds. Different from the traditional partition method, which only uses the pixel value information, our method combines a skin colour model, the edge structure of the face, and pixel information in the partition. The partition threshold model proposed in this paper is used to calculate the appropriate threshold value in each partition, so that each image partition gets the corresponding threshold value. Experimental results demonstrate the robustness of the scheme.

关键词： Skin image color analysis Faces Feature extraction Lighting Face recognition Colored noise

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Building 3D Map Based on Monte Carlo Localization and Feature Extraction

Building 3D Map Based on Monte Carlo Localization and Featur...

引用

IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Zhun Fan Ying Chen Chong Li Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou Guangdong China School of Electrical and Information Engineering Shantou University Shantou Guangdong China

ISBN: (数字)9781728190105

ISBN: (纸本)9781728190112

In this project, we mainly explore and construct the 3D indoor map. The goal of this paper is to merge the data from the laser and kinect sensor with Monte Carlo Location(MCL) in 2D map. We use the laser range sensor to construct the 2D map and get the robot's pose transformation matrix. Then, we obtain the color and depth image by using the kinect sensor and build 3D point cloud map by using the feature extraction method, getting the kinect's pose transformation matrix. After that, we get the optimal pose transformation by using the Kalman Filter to calibrate the robot's pose transformation matrix and the kinect's pose transformation matrix. Finally, the optimal pose transformation matrix is employed to accomplish the local 3D map and construct the global 3D indoor map. To show the superiority of our method, we make some experiments and compare with some other algorithms. Experimental results show that our method has a better superiority.

关键词： Three-dimensional displays Robots Robot sensing systems Two dimensional displays Kalman filters Feature extraction Buildings

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Study on Hand Gesture Recoginition by using Machine Learning

Study on Hand Gesture Recoginition by using Machine Learning

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： A. Mohanarathinam K.G. Dharani R. Sangeetha G. Aravindh P. Sasikala Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Artificial Intelligence [AI] and Machine Learning [ML] play a vital role in the health care industries and other such scientific applications. In continuation with Artificial Intelligence, so many researches are attracted towards the Hand Gesture Recognition (HGR). HGR is the simple method used to interact with machines. Hand gesture recognition is performed by using machine learning algorithm. It consists of two approaches, they are appearance based approach and model based approach. In appearance based method, the hand image is reconstructed by using image properties. But in model based method, different models such as volumetric, geometric etc., are used to reconstruct the image. The complications in machine learning algorithm and processing time are remaining as the major challenge in gesture recognition. The performance of HGR system is evaluated based on its accuracy. This paper presents the detailed study on various methods and algorithms used in gesture recognition.

关键词： Gesture recognition Machine learning algorithms conferences Signal processing algorithms Computational modeling Atmospheric modeling Real-time systems

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Recognition of High Dimensional Multi-Sensor Remote Sensing Data of Various Spatial Resolution

Recognition of High Dimensional Multi-Sensor Remote Sensing ...

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IEEE International conference on Data Stream Mining & processing (DSMP)

作者： Volodymyr Hnatushenko Viktoriia Hnatushenko Dep. of Information Systems and Technologies Dnipro University of Technology Dnipro Ukraine Dep. of Information Technologies and Systems National Metallurgical Academy of Ukraine Dnipro Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132150

High-dimensional multi-sensor image data open new possibilities in remote sensing image analysis. One of the main problems in the field of automated high-dimensional data processing is interpretation and recognition of geometric forms in images. The use of traditional moment invariants in object recognition is limited to simple geometric transforms. In this study, a novel recognition method of high-dimensional multi-sensor remote sensing data of various spatial resolution was proposed. The proposed method of photogrammetric image recognition represented in raster formats of computer graphics enables identification of objects without implementation of complex algorithms involving transformation of digital images. This, in turn, enables improvement of the recognition accuracy. The experiments have shown that the proposed algorithm provides a useful alternative approach to feature reduction in high-dimensional digital image data.

关键词： Remote sensing image recognition Mathematical model Object recognition Brightness Spatial resolution Extraterrestrial measurements

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