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

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Key point location method for pedestrians in depth images based on deep learning

Key point location method for pedestrians in depth images ba...

引用

6th International Multi-conference on Engineering and Technology Innovation (IMETI)

作者： Liu Jian Jin Zequn Zhang Rui Liu Meiju Gao Enyang Shenyang Jianzhu Univ Coll Informat & Control Engn Shenyang 110168 Liaoning Peoples R China

One difficulty that remains in image processing is the accurate location of key points in depth images. This paper presents an intelligent location method for identifying key points in depth images based on deep convolutional neural networks. This study used Kinect to process images, calculating the differences in depth as well as the directional gradient in subject depth images. The entirety of each depth image was traversed through a sliding window to identify the feature vector. Principal component analysis was used to reduce image dimensions. The random forest technique was used to select characteristics of strong classification as well as to actualize training and testing. A depth convolutional neural network was used to detect key points in images of pedestrians. During the study, an experimental test was conducted in a general environment under various conditions, including occlusion and low light. Even under these suboptimal conditions, the detection rate of the proposed method was 87.72%. Furthermore, this method was compared with the GEBCF and FCF algorithms, and proved to increase the detection rate by 0.92% and 0.68%, respectively. Using the depth convolutional neural network in the pedestrian key point positioning experiment, the average error obtained when comparing the predicted point coordinates to the sample mark coordinates was 2.102 pixels. These experimental results show that this method has good accuracy and robustness for the key point location problem of pedestrians in depth images.

关键词： Pedestrian detection depth image key point location deep learning

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DRONE BASED NEAR REAL-TIME HUMAN DETECTION WITH GEOGRAPHIC LOCALIZATION

DRONE BASED NEAR REAL-TIME HUMAN DETECTION WITH GEOGRAPHIC L...

引用

conference on Geo-information for Disaster Management (Gi4DM)

作者： Baeck, P. J. Lewyckyj, N. Beusen, B. Horsten, W. Pauly, K. VITO Remote Sensing Unit Boeretang 200 B-2400 Mol Belgium

Detection of humans, e.g. for search and rescue operations has been enabled by the availability of compact, easy to use cameras and drones. On the other hand, aerial photogrammetry techniques for inspection applications allow for precise geographic localization and the generation of an overview orthomosaic and 3D terrain model. The proposed solution is based on nadir drone imagery and combines both deep learning and photogrammetric algorithms to detect people and position them with geographical coordinates on an overview orthomosaic and 3D terrain map. The drone image processing chain is fully automated and near real-time and therefore allows search and rescue teams to operate more efficiently in difficult to reach areas.

关键词： Drones Geographic Information systems (GIS) Human Detection Photogrammetry Deep Learning

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Technion aerial systems 2018 technical design paper for AUVSI student-UAS competition

Technion aerial systems 2018 technical design paper for AUVS...

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59th Israel Annual conference on Aerospace Sciences, IACAS 2019

ISBN: (纸本)9781510882782

In the following technical design paper, the Technion Aerial systems (TAS) team presents the Ninox system and its development process for the AUVSI Student UAS Competition 2018. The team comprises 32 undergraduate students of the Faculties of Aerospace Engineering, Electrical Engineering, Geo Information and Industrial Engineering. This year, our team has focused the on optimizing various algorithms, image processing protocols, control system properties, aircraft aerodynamics and the modularity of the avionic system. We achieved improved performance by executing comprehensive CFD and finite-element analysis, as well as wing tunnel experiments and flight tests, the result being the manufacturing of a new custom-designed aircraft with fully autonomous capabilities with emphasis on the safety of our team and aircraft. © 2019 Israel Annual conference on Aerospace Sciences. All rights reserved.

关键词： Students

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3D Imaging of Sparse Wireless Signal Reconstructions via Machine Learning

3D Imaging of Sparse Wireless Signal Reconstructions via Mac...

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IEEE International conference on Communications (ICC)

作者： Scott Fowler Gabriel G. Baravdish George Baravdish Communications and Transport Systems Linköping University Norrköping Sweden Media and Information Technology Linköping University Norrköping Sweden Physics Electronics and Mathematics Linköping University Norrköping Sweden

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Wireless devices have been used to investigate the environment and to understand our physical world. In this work, we undertake the challenging problem of identifying location of obstacles and objects by WiFi signals. Gathering wireless sensory data to form an image is difficult since wireless signals are susceptible to multipath. Moreover, reconstructing an image of unknown objects based on the measurements of sparse signals is an ill-posed problem. To tackle these problems, we first present a linear model using received signal strength indicator (RSSI) measurements. We define the sparse beamforming problem as an ℓ 0 -norm optimization problem, then use the iterative reweighted ℓ 1 heuristic algorithm to obtain an optimal solution as a multipath. Finally, the multipath fading is removed by using Machine Learning. More specifically, we use Support Vector Regression (SVR) to identify a clear image of the unknown object. Our results show that the proposed method can reconstruct signals as a 3D image with a satisfactory visual appearance, i.e. the generated data mesh is well defined and smooth compared to previous work.

关键词： Wireless communication Wireless sensor networks image reconstruction Signal processing algorithms Three-dimensional displays Object recognition Kernel

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Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network 4

Chronic Tympanic Membrane Diagnosis based on Deep Convolutio...

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4th International conference on Computer Science and Engineering (UBMK)

作者： Basaran, Erdal Comert, Zafer Sengur, Abdulkadir Budak, Umit Celik, Yuksel Togacar, Mesut Karabuk Univ Dept Comp Engn Karabuk Turkey Samsun Univ Dept Software Engn Samsun Turkey Firat Univ Dept Elect & Elect Engn Elazig Turkey Bitlis Eren Univ Dept Elect & Elect Engn Bitlis Turkey Firat Univ Dept Comp Technol Elazig Turkey

ISBN: (纸本)9781728139647

Chronic Otitis Media (COM) causes deformation of the middle ear ossicles with perforation as a result of long-lasting inflammation of the middle ear and it is one of the basic reasons for hearing loss. The middle ear images are examined by otolaryngologists in the diagnosis of the disease in clinical practice. The observers make a decision considering the status of the tympanic membrane images. Decision support systems using image processing techniques and machine learning algorithms are quite useful in the diagnosis process, however, the usage of such systems in this field is limited. In this study, we propose a diagnostic model using a pretrained deep convolutional neural network (DCNN) called AlexNet. The experiments were carried out on a private dataset consisting of totally 598 tympanic membrane images collected from patients admitted to Ozel Van Akdamar Hospital. Firstly, a set of preprocessing procedures were applied to the eardrum images. Then, the tympanic membrane images were used to feed the DCNN model. The proposed model was trained using transfer learning approach. To evaluate and validate the success of the proposed model, the 10-fold cross-validation method was used. As a result, the model provided satisfactory results with an accuracy of 98.77%. Consequently, the proposed DCNN model was determined as a robust tool in separating chronic and normal tympanic membrane images.

关键词： Biomedical signal processing diagnosis system otitis media deep convolutional neural network

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Design and Simulation of ZnO based Acetone Gas Sensor using COMSOL Multiphysics

Design and Simulation of ZnO based Acetone Gas Sensor using ...

引用

International conference on Signal processing and Integrated Networks (SPIN)

作者： K. Arun M.S. Lekshmi K.J. Suja National Institute of Technology Calicut India

ISBN: (数字)9781728154756

ISBN: (纸本)9781728154763

Acetone gas is a breath marker for diabetes detection. Metal oxide gas sensors have been widely used for gas sensing applications. Zinc oxide (ZnO) is a promising material for Acetone gas detection. Metal oxide semiconductors require a high-temperature environment for detecting the target gas. The temperature generated from the micro-heater has a great influence on the concentration of gas adsorbed on the surface and also the resistance of the sensing layer. The temperature should be uniformly distributed on the surface of the heater for the better performance of the sensor. In this work, a meander shaped micro-heater is used for the ZnO sensor for Acetone gas detection. The Scanning electron microscopic image of the synthesized ZnO was found to have a spherical shape and the same structure was given for the sensing layer in the simulation. Platinum is used as the heating material because of its high thermal conductivity and low power consumption. The variation of the temperature of the heating material with respect to the input voltage was analyzed. The variation of the resistance of the sensing layer with respect to the target gas was also analyzed. The resistance of the sensing layer was found to decrease in the presence of Acetone gas. The simulation was done using COMSOL Multiphysics.

关键词： Zinc oxide Gas detectors Temperature sensors II-vi semiconductor materials Heating systems

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Development of a vision Aided Automated Ball Retrieving Robot for Tennis Training Sessions 14

Development of a Vision Aided Automated Ball Retrieving Robo...

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14th IEEE International conference on Industrial and Information systems, ICIIS 2019

作者： Perera, Dulanjana M. Menaka, G.M.D. Surasinghe, W.V.K.M. Kanishka Madusanka, D.G. Lalitharathne, Thilina Dulantha Robotics and Control Systems Laboratory University of Moratuwa Department of Mechanical Engineering 10400 Sri Lanka

ISBN: (纸本)9781728137063

A single training session of tennis requires 30-40 balls and these scattered balls have to be collected at the end of each session which costs time and induces unnecessary physical stress on players. This paper proposes a method to retrieve the balls which are scattered in the Tennis court back to the user. The proposed method comprises image processing techniques for object localization and the adaptation of Dijkstra's algorithm for optimum path planning for efficient navigation. Both localization and the path planning algorithms are developed on a Python platform and robot's differential drive system is controlled by a microcontroller. Communication between robot and the computer is established through Wi-Fi. Novel development of this research is the implementation of the global view method for a vast range of image acquisition and verifying its suitability in Tennis field. In order to collect balls efficiently, an adaption of an existing mechanism is proposed. Results show that the mechanism is 80% efficient in collecting balls and 100% efficient in object identification for a given environmental condition. © 2019 IEEE.

关键词： Mobile robots

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image Classification and Detection of Cigarette Combustion Cone Based on Inception Resnet V2

Image Classification and Detection of Cigarette Combustion C...

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International conference on Computers, Communications, and systems (ICCCS)

作者： Guoqing Deng Yangguang Zhao Long Zhang Zhigang Li Yong Liu Yi Zhang Bin Li Opto-electronics Applied Technology Research Centre Institute of Applied Technology and Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Jiangxi Branch of China Tobacco Industrial Corporation Nanchang China Key Laboratory of Tobacco Processing Technology China National Tobacco Corporation Zhengzhou China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

In order to guide the production of cigarette products and improve the quality of cigarette products, this paper proposes a classification method for cigarette combustion cones based on deep convolutional neural network model. The method is optimized based on the Inception Resnet V2 model and is innovatively used in the detection of cigarette burning cones. The classification accuracy of combustion cone fallout is characterized by the overall classification accuracy (OA) and the Kappa coefficient (Kappa). The experimental results show that the overall classification accuracy is 97.22%, and the Kappa coefficient is 0.9583. The deep convolutional neural network has better classification effect. Based on the classification method of deep convolutional neural network, the cigarette burning cone can be accurately identified.

关键词： Combustion Training Classification algorithms Convolutional neural networks Force Head Manuals

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Object Detection and Classification in Aerial Hyperspectral imagery using a Multivariate Hit-or-Miss Transform 25

Object Detection and Classification in Aerial Hyperspectral ...

引用

25th SPIE conference on algorithms, Technologies, and Applications for Multispectral and Hyperspectral imagery

作者： Macfarlane, Fraser Murray, Paul Marshall, Stephen White, Henry Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland BAE Syst Air Sect Bristol Avon England

ISBN: (纸本)9781510626386

High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques.

关键词： Hyperspectral image processing Mathematical morphology Hit-or-Miss Transform Template matching Object detection

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Towards Environmentally Adaptive Odor Source Localization: Fuzzy Lévy Taxis Algorithm and Its Validation in Dynamic Odor Plumes

Towards Environmentally Adaptive Odor Source Localization: F...

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International conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xinxing Chen Jian Huang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

In this paper, we propose a bio-inspired Fuzzy Lévy Taxis algorithm to solve the robotic odor source localization problem in dynamic odor plumes. According to the proposed algorithm, the robot is programmed to move for a length with a turning angle at each step until reaching the odor source. The movement length and the turning angle follow two specific probability distribution, of which the parameters are adaptive through a fuzzy logic system. The proposed algorithm was compared with the Adaptive Lévy Taxis algorithm in simulated pseudo-Gaussian plumes. Our proposed algorithm shows a higher success rate and efficiency. The algorithm has also been systematically evaluated in simulated filament-based odor plumes under various environmental conditions. The results revealed that the performance of the proposed algorithm is consistently good in various environmental conditions in terms of success rate, number of steps and distance overhead to find the odor source.

关键词： Public transportation Heuristic algorithms Turning Robot sensing systems Adaptation models Fuzzy systems

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