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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

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Segmentation and detection of lung cancer using image processing and clustering techniques 2nd

Segmentation and detection of lung cancer using image proces...

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2nd international conference on Advanced Computing and Intelligent Engineering, ICACIE 2017

作者： Joon, Preeti Bajaj, Shalini Bhaskar Jatain, Aman Department of Computer Science Amity University Gurugram Gurugram India

ISBN: (纸本)9789811317071

Lung cancer is a most common disease nowadays, so to get rid of it we have made a detection system. In this paper, an active spline model is used to segment the X-ray images of lung cancer. The system formed acquired medical images of lung X-ray. First, in preprocessing median filter is used for noise detection. Then, segmentation is applied and further K-mean and fuzzy C-mean clustering is applied for feature extraction. This paper is an extension of techniques of image processing of lung cancer detection and produces the final results of feature extraction after X-ray image segmentation. Here, the proposed model is developed using SVM algorithm used for classification. Using MATLAB, simulation results are obtained for cancer detection system. This paper focuses thus on segmentation and detection of lung cancer by finding normality and abnormality of the images. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Feature extraction

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Moving target detection under turbulence degraded visible and infrared image sequences 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Veenu, Chaudhary Ajay, Kumar Anurekha, Sharma Department of Electronic Science Kurukshetra University KurukshetraHaryana136119 India Instrumentation Research and Development Establishment DehradunUttarakhand248008 India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

The presence of atmospheric turbulence over horizontal imaging paths introduces time-varying perturbations and blur in the scene that severely degrade the performance of moving object detection and tracking systems of vision applications. This paper proposed a simple and efficient algorithm for moving target detection under turbulent media, based on adaptive background subtraction approach with different types of background models followed by adaptive global thresholding to detect foreground. This proposed method is implemented in MATLAB and tested on turbulence degraded video sequences. Further, this proposed method is also compared with state-of-the-art method published in the literature. The result shows that the detection performance by proposed algorithm is better. Further, the proposed method can be easily implemented in FPGA-based hardware. © 2018, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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A Simple and Mighty Arrowhead Detection Technique of Bangla Sign Language Characters with CNN 2nd

A Simple and Mighty Arrowhead Detection Technique of Bangla ...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Islam, Md. Sanzidul Mousumi, Sadia Sultana Sharmin Rabby, Akm Shahariar Azad Hossain, Syed Akhter Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811391804

Sign Language is argued as the first Language for hearing impaired people. It is the most physical and obvious way for the deaf and dumb people who have speech and hearing problems to convey themselves and general people. So, an interpreter is wanted whereas a general people needs to communicate with a deaf and dumb person. In respect to Bangladesh, 2.4 million people uses sign language but the works are extremely few for Bangladeshi Sign Language (BdSL). In this paper, we attempt to represent a BdSL recognition model which are constructed using of 50 sets of hand sign images. Bangla Sign alphabets are identified by resolving its shape and assimilating its structures that abstract each sign. In proposed model, we used multi-layered Convolutional Neural Network (CNN). CNNs are able to automate the method of structure formulation. Finally the model gained 92% accuracy on our dataset. © 2019, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

2nd International Conference on Smart Innovations in Communi...

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

ISBN: (纸本)9789811324130

The proceedings contain 47 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: computer vision-Based Fruit Disease Detection and Classification;Automated Brain Tumor Detection Using Discriminative Clustering Based MRI Segmentation;cancer Prediction Based on Fuzzy Inference System;energy Consumption Data Analysis and Operation Evaluation of Green Buildings;the Method of Random Generation of Electronic Patrol Path Based on Artificial Intelligence;Channel Power Estimation for DVBRCS to DVBS2 Onboard DSP Payload;a Slotted Microstrip Patch Antenna for 5G Mobile Phone Applications;reliability Study of Sensor Node Monitoring Unattended Environment;RSOM-Based Clustering and Routing in WSNs;real-Time Meta Learning Approach for Mobile Healthcare;Evaluation of Received Signal Strength Indicator (RSSI) for Relay-Based Communication in WBAN;side-Channel Attacks on Cryptographic Devices and Their Countermeasures—A Review;implementation and Analysis of Different Path Loss Models for Cooperative Communication in a Wireless Sensor Network;Parallel Approach for Sub-graph Isomorphism on Multicore System Using OpenMP;optimized Solution for Employee Transportation Problem Using Linear Programming;spectrum Prediction Using Time Delay Neural Network in Cognitive Radio Network;Reliability Factor Based AODV Protocol: Prevention of Black Hole Attack in MANET;a Survey of Lightweight Cryptographic Algorithms for IoT-Based Applications;fruit Disease Detection Using Rule-Based Classification;Fault Tolerance Through Energy Balanced Cluster Formation (EBCF) in WSN;firearm Detection from Surveillance Cameras Using image processing and Machine Learning Techniques;Mining Social Networks: Tollywood Reviews for Analyzing UPC by Using Big Data Framework;cloud-Based E-Learning: Using Cloud Computing Platform for an Effective E-Learning.

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Deep convolutional neural network for person re-identification: A comprehensive review 2nd

Deep convolutional neural network for person re-identificati...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Chahar, Harendra Nain, Neeta Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9789811078941

In video surveillance, person re-identification (re-id) is a popular technique to automatically finding whether a person has been already seen in a group of cameras. In the recent years, availability of large-scale datasets, the deep learning-based approaches have made significant improvement in the accuracy over the years as compared to hand-crafted approaches. In this paper, we have distinguished the person re-id approaches into two categories, i.e., image-based and video-based approaches;deep learning approaches are reviewed in both categories. This paper contains the brief survey of deep learning approaches on both image and video person re-id datasets. We have also presented the current ongoing works, issues, and future directions in large-scale datasets. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Removing Rain from Single image Based on Details Preservation and Background Enhancement

Removing Rain from Single Image Based on Details Preservatio...

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IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Jiacheng Liu Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Processing and Intelligent Control Fujian Provincial Key Laboratory of Information Minjiang University Fuzhou China

Life is often accompanied by the bad weather. In the rainy days, the quality of images and videos acquired will be greatly degraded, affecting human observation and target detection and recognition in computer vision systems. In this paper, a single image rain removal algorithm is proposed based on details preservation and background enhancement. We first decompose the rainy image into low-frequency part and high-frequency part by low pass smoothing filter. Then, edge detection is performed on the low-frequency part to extract a mask, which will be used to capture rain-free image details from the high-frequency part. Next, the rain-free image details are superimposed on the low-frequency part to obtain an image without rains but well preserved details. Finally, the dark channel prior method is utilized to further alleviate the blur due to raining. Experiments on both synthetic and real rainy images demonstrate the effectiveness and efficiency of the proposed method.

关键词： Rain image edge detection Filtering Videos Filtering algorithms Snow computer vision

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image denoising using fractional quaternion wavelet transform 2nd

Image denoising using fractional quaternion wavelet transfor...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Nandal, Savita Kumar, Sanjeev Department of Mathematics Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811078972

This paper presents an image denoising algorithm using fractional quaternion wavelet transform (FrQWT). In particular, images corrupted with additive Gaussian noise are considered and FrQWT is performed via hard and semi-soft thresholds. The thresholding on the wavelet coefficients reveals the capabilities of wavelet transform in the restoration of an image degraded by noise. FrQWT is simple and adaptive since the estimation of threshold parameters depends on the data of wavelet coefficients. The fractional order captures the texture details of an image in more adaptive way. Experimental results exploit the better performance compared to the various techniques such as denoising in discrete wavelets, complex wavelet and quaternion wavelet transform domains in terms of high peak signal-to-noise ratio (PSNR). © Springer Nature Singapore Pte Ltd. 2018.

关键词： Wavelet transforms

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Training Dataset Extension Through Multiclass Generative Adversarial Networks and K-nearest Neighbor Classifier 2nd

Training Dataset Extension Through Multiclass Generative Adv...

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2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Cecotti, Hubert Jha, Ganesh Department of Computer Science College of Science and Mathematics Fresno State FresnoCA United States

ISBN: (纸本)9789811391804

The performance of deep learning architectures, where all the feature extraction stages are learned within the artificial neural network, requires a large number of labeled examples to model the variability of the different possible inputs. This issue is also present in other classification tasks with a large number of features where the number of examples can limit the number of possible input features, involving the creation of handcraft feature sets. Typical solutions in computer vision and document analysis and recognition increase the size of the database with additional geometric transformations (e.g. shift and rotation) and random elastic deformations of the original training examples. In this paper, we propose to evaluate the impact of additional images created through generative adversarial networks (GANs), which are deep neural network architectures. We study the addition of images created through a multiclass GAN in different databases of handwritten numerals from different scripts (Latin, Devanagari, and Oriya). The contributions of this paper are related to the use of multiclass GANs to extend the size of the training database after filtering the images with a k-nearest neighbor classifier where the k nearest neighbors must all agree on the decision to validate a GAN generated image. The accuracy is evaluated with the original training dataset, the GAN generated images, and the combination of the original training images and the GAN generated images. The results support the conclusion that GAN generated images through a multiclass paradigm can provide a robust and fully data driven solution for enlarging the size of the training database for improving the accuracy on the test dataset. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Ishara-Bochon: The First Multipurpose Open Access Dataset for Bangla Sign Language Isolated Digits 2nd

Ishara-Bochon: The First Multipurpose Open Access Dataset fo...

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2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Islam, Md. Sanzidul Mousumi, Sadia Sultana Sharmin Jessan, Nazmul A. Rabby, Akm Shahariar Azad Abujar, Sheikh Hossain, Syed Akhter Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811391804

Ishara-Bochon, the first multipurpose comprehensive open access isolated digits dataset for Bangladeshi Sign Language (BdSL) is introduced in this article. Sign language digits is a major part of communication for deaf and hearing impaired people. It is important in their everyday stuff, such as – in routine accounting and to interact with general people. Ishara-Bachon dataset contains 100 sets of 10 Bangla basic sign digits (0, 1, 2.. 9), collected from different deaf and general volunteers from different institutes. After discarding maximum errors and performing different preprocessing methods, 1000 images of Bangla sign language isolated digits were included in the first dataset. After collectiong raw data, some effective preprocessing methods were performed for making those data useable for computer vision model and any other application development purposes. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Real-time monitoring of Conveyor using computer vision and IoT

Real-time monitoring of Conveyor using Computer Vision and I...

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international conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Alakesh Narzary S Ashok National Institute of Technology Calicut Kozhikode India

Sorting objects in the conveyor are one of the main objectives in many manufacturing industries. This paper presents the idea to use computer vision with a low-resolution camera to sort out different objects in a conveyor based on a different colour, size, and shape and also monitor and control a conveyor in realtime by using the IoT technology. Raspberry-pi is used as the central processing unit for image processing, sorting and collecting data as well as data update by parallel processing. IoT technology is an important technology, which helps in controlling devices or collecting data regardless of distance. MQTT, a lightweight IoT protocol, is used to gather data through the internet and a GUI is made to control the conveyor through the internet as well as for real-time data visualization.

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