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Multiclass Fruit Packing System Based on computer vision 2

Multiclass Fruit Packing System Based on Computer Vision

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2019 2nd international Symposium on Power Electronics and Control Engineering, ISPECE 2019

作者： Lin, Zenan Shao, Juanjuan Zhang, Xinzheng Deng, Xinyue Zheng, Tian School of Electrical Engineering Jinan University Zhuhai Guangdong519070 China Guangdong Electrical and Intelligent Control Engineering Technology Research Center China

The traditional human-based fruit packing method is inefficient and costly. In response to the requirement of packing multiple fruits automatically in the present market, a multiclass fruit packing system based on computer vision is designed. Firstly, the image acquisition module captures images and passes them to the recognition module, which converts the RGB space of the image to the HSV color space and extracts the H and S components. Secondly, by using the morphological method to process the target areas, the class, size and position of the fruit are calculated. Finally, in the fruit packing control module, the manipulator automatically grasps different kinds of fruits into the different packing boxes according to the recognition results. The experimental results show that the accuracy of recognition is 93.73 %, the average recognition time of each image is 0.0402s. The accuracy of packing is 95.67% and the injury rate of fruit is 1.67%, which lay the foundation for fully automatic production of fruit. © Published under licence by IOP Publishing Ltd.

关键词： Fruits

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Incremental Multi-Feature Tensor Subspace Learning Based Smart Traffic Control System and Traffic Density Calculation Using image processing

Incremental Multi-Feature Tensor Subspace Learning Based Sma...

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international conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： G. Suseendran D. Akila D. Balaganesh V.R. Elangovan V. Vijayalakshmi Technology & Advanced Studies Vels Institute of Science Chennai India Computer Science and Multimedia Lincoln University College Malaysia Agurchand Manmull Jain College Chennai SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9781728194912

ISBN: (纸本)9781728194929

In times, urban centers are growing at a high rate. Growing with them is a road traffic jam. Traffic jams, especially at peak hours, became routine. As a result, traffic management is one of the foremost pressing issues in today's towns. Several alternatives are being sought to affect the matter. These include expanding road networks, regulating the number of vehicles on the roads, and deployment of Intelligent Transportation Systems (ITSs). Aside from the ITSs, the opposite alternatives (however significant) have many practical challenges in their implementation. ITSs have supported a good range of technologies like loop sensors and video surveillance systems. vision-based ITSs have proved advantageous over the standard methods supported loop sensors. In these modern systems, video surveillance cameras are installed along the roads and road intersections where they're wont to collect traffic data. The info is then analyzed to get traffic parameters like road traffic density. This paper presents a comfortable and stylish approach for estimating the road traffic density during daytime using image processing and computer vision algorithms. The video data collected is first weakened into frames, which are then preprocessed during a series of steps. Finally, the vehicles are detected and extracted from the pictures and Density estimated. The traffic density is then obtained because of the number of vehicles per unit area of the road section. The proposed approach was implemented in MATLAB R2013a and average vehicle detection accuracy of 96.0% and 82.1% were achieved for fast-paced and slow-moving traffic scenes.

关键词： computer vision Roads image processing Video surveillance Cameras Intelligent sensors Matlab

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Feature Engineering for Cardio-Thoracic Disease Detection from NIH Chest Radiographs 2nd

Feature Engineering for Cardio-Thoracic Disease Detection fr...

引用

2nd international conference on Computational Intelligence in Pattern Recognition (CIPR)

作者： Mukherjee, Avishek SUNY Albany Albany NY 12222 USA

ISBN: (纸本)9789811524493;9789811524486

There is a huge shortage of radiologists since the advent of radiological imaging that has resulted in outsourcing of radiological services called as 'Teleradiology', but without much success in mitigating the huge demand. Deep learning can help inmitigating the shortage and in providing timely medical care by shortening the time required for diagnosis and segmentation of the disease. Contemporary patient privacy regulations along with the fact that 'medical images' unlike a standard picture differ in many aspects like resolution, dimensions, brightness, etc., which sufficiently degrades the performance of a trained neural network. In this work, we study the performance of the neural network across varying attributes (dimensions, resolutions, etc.) of the NIH Chest X-ray dataset by augmenting the existing dataset by leveraging a densely connected 121-layer deep neural network. Apart from that, we studied the possibility of generating segmentation maps to help radiologists achieve concurrence amongst themselves.

关键词： Deep learning Medical imaging Artificial intelligence image processing computer vision

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image Denoising Method Based on Hybrid Deep Dictionary Learning and Elastic Threshold

Image Denoising Method Based on Hybrid Deep Dictionary Learn...

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Information Technology and computer Application (ITCA), international conference on

作者： Guanlin Liu Guangxi University of Foreign Languages Guangxi China

The application of machine vision in the modern industry makes intelligent manufacturing possible. At present, circuit board related fault detection, such as broken wires, missing solder joints, and other problems frequently occur, the manual recognition speed is slow and the false detection rate is high. In response to this problem, this research proposes an image noise reduction processing method that combines deep dictionary learning and elastic thresholds, which can serve computer image processing, thereby greatly improving the detection speed of printed circuit boards, and ultimately serving the processing and production of modern electronic products.

关键词： Machine vision Printed circuits Wires Noise reduction Machine learning Production Manuals

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Securing Face Recognition System Using Blockchain Technology 2nd

Securing Face Recognition System Using Blockchain Technology

引用

2nd international conference on Machine Learning, image processing, Network Security and Data Sciences, MInd 2020

作者： Shankar, Saumya Madarkar, Jitendra Sharma, Poonam Computer Science and Engineering VNIT NagpurMaharashtra India

ISBN: (纸本)9789811563171

Facial recognition is a wide area of a computer vision which is mostly used for security purpose. The main motive of the face recognition system is to authentic a person from a given training database. The face-based biometric system and surveillance cameras are deployed everywhere, for that need a strong face recognition system. The recognition system needs a large number of training samples that need to store in any storage center but when the face data is collected and stored, that time hackers can access and manipulate that data. So, a platform is presented that is secure and tamper-proof from these data breaches as well as hacks and is not compromising with data availability, by using blockchain to store face images. The absolute infeasibility of editing a historical record in a blockchain makes it tamper-proof (immutable) ensuring security. The storage of data on multiple computers provides accessibility. For face recognition, VGGFace deep neural network is used for automatic extraction of features and logistic regression for classification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Detection and Control System for Automotive Products Applications by Artificial vision Using Deep Learning 2nd

Detection and Control System for Automotive Products Applica...

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2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： El Wahabi, Abdelhamid Baraka, Ibrahim Hadj Hamdoune, Salaheddine El Mokhtari, Karim LIST UAE Faculty of Science and Technology Tangier Morocco Data Science Laboratory Ryerson University Toronto Canada

ISBN: (纸本)9783030366704

Object recognition is among the most important subjects in computer vision, it has undergone a huge evolution during these last decades, but in the last years artificial intelligence has seen the appearance of Deep Learning, and through the efforts of researchers, Deep Learning is having great success, its applications have touched on different fields, such as robotics, industry, automotive … In this context, in collaboration with an Automotive components manufacturer and FST faculty of sciences and technologies of tangier (UAE University) have taken the initiative to develop and implement an object recognition and inspection system for automotive products application that requires a good accuracy of image classification using the Deep Learning which is the purpose of this paper. This report summarizes the work done within this Company concerning the development and implementation of a system aims to realize an artificial vision system for the inspection of automotive products based mainly on the "Deep Learning" method. Before, during and after manufacturing, many products in automotive sector (electronic components, …) are subjected to a visual inspection phase, in this context we have replace this phase by our vision system so that the piece will be accepted or not accepted, as well as to act to parameters (for example: winding shape, welding quality …) in the case of not accepted. The convolutional neural networks have become advanced methods for classification and detection of objects over the last five years. At present, they work better than conventional image processing method set, on many image classification data sets. Most of these datasets are based on the notion of concrete classes. In this paper, we present a new set of image classification data as well as object detection data, which should be easy for humans to solve, but its variations are difficult for CNN. The classification performance of popular CNN architectures is evaluated on this dataset and

关键词： Automotive industry

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An Overview of Remote Photoplethysmography Methods for Vital Sign Monitoring 1st

An Overview of Remote Photoplethysmography Methods for Vital...

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1st international Symposium on computer vision and Machine Intelligence in Medical image Analysis, ISCMM 2019 in conjunction with the 2nd international conference on Advanced Computational and Communication Paradigms, ICACCP 2019

作者： Sinhal, Ruchika Singh, Kavita Raghuwanshi, M.M. Department of CSE Datta Meghe Institute of Engineering Technology & Research Wardha India Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Information Technology Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9789811387975

Vital signs such as heart rate, respiratory rate, blood pressure, body temperature, and oxygen saturation are essential for early detection of any related significant illness. Many of the existing methods that are used to monitor the aforementioned vital signs are camera-based. In these methods, sensors are fixed to the body that are not sturdy to the motion of the subject. Another method to monitor vital signs is photoplethysmography (PPG), an emerging noncontact technique that maps, spatial blood volume variation in living tissue from the images captured through a video. Most of the camera-based methods are driven by three remote photoplethysmography algorithms. The camera-based methods are useful for detecting vital signs with an objective AAA, i.e., anyone, anywhere, and anytime. However, there exist few challenges in r-ppg methods and make it an open research problem. This paper presents an overview of the signal processing challenges faced by remote photoplethysmography for calculating the vital signs with a focus on heart rate estimation. © Springer Nature Singapore Pte Ltd. 2020

关键词： Heart

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Laser Scar Classification in Retinal Fundus images Using Wavelet Transform and Local Variance 1st

Laser Scar Classification in Retinal Fundus Images Using Wav...

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作者： Raut, Rashmi Sapate, Visharad Rokde, Abhay Pachade, Samiksha Porwal, Prasanna Kokare, Manesh Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789811387975

Diabetic retinopathy (DR) affects the vision of the person and may eventually lead to blindness. In the initial stage of the disease, patients are treated with a laser to restrict its progression. Such laser treatment leaves behind scars on the retina and patients are advised to undergo screening regularly to track further complications. This paper presents a novel retinal background characterization approach that explores the potential of discrete wavelet transform and rotational-invariant variance features for texture classification of retinal images with and without laser marks. For this experiment, different classifiers, namely, support vector machine, naive Bayes, neural network, and random forest classifiers are tested. We used two publicly available datasets, namely, LMD-DRS and LMD-BAPT. In all cases, the proposed approach obtained the sensitivity, specificity, and accuracy values higher than 68.9%, 70.2%, and 69.4%, respectively. It was found that all performance measures achieve over 87.5, 89.4, and 86.7% for the classification task using random forest classifier. These promising results suggest that the proposed technique can discriminate retinal images having laser marks and without laser marks, and has the potential to be an important constituent in computerized screening solution for retinal images. © Springer Nature Singapore Pte Ltd. 2020

关键词： Discrete wavelet transforms

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An Edge Detection and Sliding Window Based Approach to Improve Object Localization in YOLOv3 1

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2nd international conference on Machine Learning, image processing, Network Security and Data Sciences, MInd 2020

作者： Blue, Shaji Thorn Brindha, M. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli TiruchirappalliTamil Nadu India

ISBN: (数字)9789811563157

ISBN: (纸本)9789811563140

Object detection is considered as a challenging field in computer vision. Once an object has been detected, the next challenge is object localization where a rectangular boundary box is drawn around the location of detected object. The proposed framework addresses the problem of object localization by improving its precision. You only look once or YOLOv3 is one of the well-known object detection algorithm with its state-of-the-art object detection and real time capabilities. Because of this reason, the proposed scheme uses YOLOv3 as the base algorithm. In this work, COCO dataset is used to detect an object, and to improve the precision of boundary box this work make use of edge detection, thresholding and morphological operation. Also, redundant edge removal algorithm is proposed to remove redundant edges and boundary box construction algorithm draws rectangular boundary box around detected object. When compared with YOLOv3, the proposed model produces significantly better results when boundary boxes around detected object is concern. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Diabetic Retinopathy Detection on Retinal Fundus images Using Convolutional Neural Network 2nd

Diabetic Retinopathy Detection on Retinal Fundus Images Usin...

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2nd international conference on Machine Learning, image processing, Network Security and Data Sciences, MInd 2020

作者： Pradhan, Adarsh Sarma, Bhaskarjyoti Nath, Rahul Kumar Das, Ajay Chakraborty, Anirudha Department of Computer Science and Engineering Girijananda Chowdhury Institute of Management and Technology Azara Guwahati India

ISBN: (纸本)9789811563140

Diabetic retinopathy is a serious eye disease that occurs due to diabetes mellitus, also commonly known as diabetes, and it has grown as the most common cause of blindness in the present world. It is a disease in which the blood vessels behind the retina are damaged. At first, it shows no symptoms, but with time, it eventually leads to blindness. Early diagnosis of diabetic retinopathy can prevent vision loss in patients. The method proposed here for the detection of diabetic retinopathy disease is based on a convolutional neural network that categorizes the fundus images of patients according to the severity level of diabetic retinopathy. The input images are collected from the Kaggle diabetic retinopathy dataset, and various preprocessing steps such as cropping, resizing, grayscaling, CLAHE and Min-Max normalization are performed. Precision, recall and Kappa score values of our model are highly affected, due to the unequal distribution of dataset. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Blood vessels

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