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Real-time monitoring of Conveyor using computer vision and IoT 2

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

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2nd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019

作者： Narzary, Alakesh Ashok, S. National Institute of Technology Calicut Electrical Engineering Department Kozhikode India

ISBN: (纸本)9781728102832

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. © 2019 IEEE.

关键词： Data visualization

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Densely Connected AutoEncoders for image Compression 19

Densely Connected AutoEncoders for Image Compression

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2nd international conference on image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Song Zebang Sei-ichiro, Kamata Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781450360920

image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computer vision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.

关键词： Lossy image compression U-net like structure Densely AutoEncoders convolutional neural networks (CNNs)

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A Saliency Detection Method via Color Contradistinction and Background Similarity 2

A Saliency Detection Method via Color Contradistinction and ...

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2nd IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Zhang, Xufan Wang, Dianhong China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781728151021

image saliency detection helps the computer quickly analyze the surrounding environment, locate the interested objects and extract the salient regions from the background. Conventional image saliency detection algorithms usually have high computational complexity, and the detection results seems to be less than satisfactory under complex application circumstances. In this paper, a novel image saliency detection method via color contradistinction and background similarity is proposed, which is effective. In our method, the input image is reconstructed according to block-based compressed sensing for reducing the computational complexity. Then, a weighted local contrast principle and a background similarity calculation framework are designed to obtain two different primary saliency maps. Finally, a weighted fusion strategy is used to combine the two saliency maps to get the final result which has the best detection performance. The experimental results show that the proposed method has good detection performance in terms of accuracy and running time.

关键词： computer vision saliency detection compressed sensing

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Pathological Myopia image Analysis Using Deep Learning 2nd

Pathological Myopia Image Analysis Using Deep Learning

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2nd international conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Devda, Jaydeep Eswari, R. Natl Inst Technol Tiruchirappalli 620015 India

Pathological Myopia (PM) is one among the main reason of visual defect in the world. Pathological myopia is associated with decaying changes in the retina. If it remains untreated it may lead to vision loss that can't be recoverable. The correct diagnosis of pathological myopia will facilitate proper treatment and improve disease management which reduce the growth of the disease. However, it is nearly impossible to scan the whole population. computer-aided diagnosing tools in eye image study will build the method scalable and economical. This paper focuses on the problems of classification of Pathological myopia images and non pathological myopia images and optical disc, fovea detection, localization, lesions (atrophy and detachment) segmentation with 400 samples provided by international Symposium on Biomedical Imaging (ISBI). In this paper, a deep learning method with Convolutional Neural Networks (CNN) is used for classification and U-net model is used for image segmentation which shows that it achieves highly competitive results. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Pathological Myopia Medical image processing Deep Learning

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A generalized sub-pixel mapping algorithm for auto-stereoscopic displays using slanted optical plates 2

A generalized sub-pixel mapping algorithm for auto-stereosco...

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2nd international conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Kim, Hyun-Woo Cho, Myungjin Kang, Min-Koo Kim, Sung-Kyu Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Iizuka city Japan Hankyong National University Department of Electrical Electronic and Control Engineering Anseong-si Korea Republic of Imaging Media Research Center Korea Institute of Science and Technology Seoul Korea Republic of

ISBN: (纸本)9781728149851

Auto-stereoscopic displays using slanted optical plates have inherent subpixel rasterization compared to the normal 2D displays, and mappings between subpixel positions and multi-view indices even vary according to the number of views and angles of slanted optical plates. In this paper, we derive a simple but generalized formula for subpixel mappings from a naïve ray-tracing technique on RGB stripe type panels. To verify the proposed algorithm, our proto-type auto-stereoscopic display using parallax barriers with a slanted angle was used and examined in the experiment. The proposed algorithm is expected to leverage converting multi-view 3D inputs to various types of auto-stereoscopic contents in real time. © 2020 IEEE.

关键词： Stereo image processing

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Removing Rain from Single image Based on Details Preservation and Background Enhancement 2

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

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2nd IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Liu, Jiacheng Teng, Shenghua Li, Zuoyong Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou Peoples R China

ISBN: (纸本)9781728151021

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 removal image decomposition Edge detection Dark channel prior

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Automated Goal Score Detection in Football Match Using Key Moments 2nd

Automated Goal Score Detection in Football Match Using Key M...

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2nd international conference on Recent Trends in Advanced Computing, Disruptive Innovation (ICRTAC-DTI)

作者： Penumala, Radha Sivagami, M. Srinivasan, Suresh Vellore Inst Technol Chennai 600127 Tamil Nadu India Nanoyotta Technol Pvt Ltd Chennai 600096 Tamil Nadu India

Automated goal score detection in a football match is a comprehensive work and a challenging task as well. In this paper, we proposed a methodology for goal score detection using key moments of the match. Key moments in any sports are referred as actions that stimulate excitement or attraction of the audience. So, key moments can be identified using computer vision. Extracting one key moment involves many steps to happen at a time and it is necessary to integrate all the steps to identify it as a key moment. It is necessary to identify the goalpost region and track the ball to calculate the goal in football match. The proposed work analyses the various tracking algorithms and finalize MIL (Multiple Instance Learning) tracker technique to track the ball. So, the proposed work identifies the goal post region using sequence of image processing operations, tracks the ball using multiple instance learning tracker and confirms whether the goal has been occurred or not. The results of the proposed work have been shown. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Key Moments computer vision image Analysis Line detection Video Analysis Object tracking

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Evolution Analysis of Adaptive Distance-Preserving Level Set for image Segmentation Based on Depth Learning 2

Evolution Analysis of Adaptive Distance-Preserving Level Set...

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2nd international conference on computer Information Science and Application Technology, CISAT 2019

作者： Wang, Jinpeng Li, Jing College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China

image segmentation technology is the most basic part of computer vision and the basis of all other image processing methods. The quality of image segmentation technology will affect the effect of subsequent processing to a large extent. In the research and application of computer vision, image segmentation is often the first step and plays a very important role in the whole process. Compared with image classification and target detection, Distance-Preserving level set method involves two cross-cutting areas of image recognition and natural language processing, which is very challenging. Handling the information interaction between the machine and the real world is the first step in the study of artificial intelligence. The goal of computer vision research is to make the computer have the ability to perceive and understand image information. In the future, the focus of the work will be on improving the generalization ability of the model so that the model can perform well in a variety of tasks. © 2019 IOP Publishing Ltd.

关键词： image segmentation

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Detection of Overall Fruit Maturity of Local Fruits using Convolutional Neural Networks Through image processing 2019

Detection of Overall Fruit Maturity of Local Fruits using Co...

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2nd international conference on Computing and Big Data (ICCBD)

作者： Ayllon, Mark Anthony Cruz, Melwin James Mendoza, Justin Jason Tomas, Mary Christine Mapua Univ Sch Informat Technol Makati Philippines

ISBN: (纸本)9781450372909

Determining the ripening of a fruit is critical to a farmer, since the fresher the fruit, the better it will be priced and sold. This is also critical to the economy since the ninth (9th) most exported good in the Philippines is fruits. The researchers made use of Convolutional Neural Networks through image processing to determine the fruit maturity of Banana (Cavendish), Mango (Carabao), Calamansi/Calamondin, will classify said fruits into three categories for the fruit maturity: pre-matured, matured, over-matured. Of the sixty fruits used, twenty pieces of which will be used to gather data, starting from their very unripe/pre-matured stage up-to the over-matured stage. This will approximately take one (1) to two (2) weeks if stored in room temperature. The total data gathered would be 3681 pieces for Calamansi (Philippine lime);3270 pieces for Banana (Cavendish);and 5706 pieces for Mango (Carabao). The model is written in Spyder in Anaconda Navigator, which will be applying Tensorflow-GPU and Keras. These will also be coupled with CUDA and CUDDN to process the data and determine the results. Two total experiments will be executed - one for the Red-Green-Blue (RGB) dataset, and one for the greyscale dataset.

关键词： computer vision Convolutional Neural Network Fruit Maturity image processing

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Do GANs Leave Artificial Fingerprints? 2

Do GANs Leave Artificial Fingerprints?

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2nd IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Marra, Francesco Gragnaniello, Diego Verdoliva, Luisa Poggi, Giovanni Univ Federico II Naples Dept Elect Engn & Informat Technol Naples Italy

ISBN: (纸本)9781728111988

In the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields. With the current pace of progress, it is a sure bet they will soon be able to generate high-quality images and videos, virtually indistinguishable from real ones. Unfortunately, realistic GAN-generated images pose serious threats to security, to begin with a possible flood of fake multimedia, and multimedia forensic countermeasures are in urgent need. In this work, we show that each GAN leaves its specific fingerprint in the images it generates, just like real-world cameras mark acquired images with traces of their photo-response non-uniformity pattern. Source identification experiments with several popular GANs show such fingerprints to represent a precious asset for forensic analyses.

关键词： image forensics GAN image fingerprint

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