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Camera2Video: A Low-Cost Food Authentication System Using Smartphone Videos 2019

Camera2Video: A Low-Cost Food Authentication System Using Sm...

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Proceedings of the 2019 2nd international conference on Sensors, Signal and image processing

作者： Hui Wang Weiran Song Nanfeng Jiang Jordan Vincent Zhiyuan Song Xizhao Wang Ulster University Shore Rd Newtownabbey Co. Fujian Normal University King's College London Strand Shenzhen University

ISBN: (纸本)9781450372435

This paper presents a low-cost sensor system for food authentication based on computer vision and pattern *** sensor system uses a smartphone to record a video of a sample under gradient coloured illumination and transforms the video into data vector for analysis. The sensor system is evaluated on the tasks of detecting olive oil adulteration and milk fat content,achieving 100% test accuracies. Since the sensor system is built in a smartphone without any additional hardware, it has the potential to serve as a simple and effective solution for food authentication.

关键词： Food authentication Smartphone Classification computer vision Video

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Deep Learning Assisted Smart Glasses as Educational Aid for Visually Challenged Students 2

Deep Learning Assisted Smart Glasses as Educational Aid for ...

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2nd international conference on New Trends in Computing Sciences (ICTCS)

作者： AlSaid, Hawra AlKhatib, Lina AlOraidh, Aqeela AlHaidar, Shoaa Bashar, Abul Prince Mohammad Bin Fahd Univ Coll Comp Engn & Sci Al Khobar 31952 Saudi Arabia

ISBN: (纸本)9781728128825

computer vision Technology has played a significant role in assisting visually challenged people to carry out their day to day activities without much dependency on other people. Smart glasses in one such solution which enables blind or visually challenged people to "read" images. This paper is an attempt in this direction to build a novel smart glass which has the ability to extract and recognize text captured from an image and convert it to speech. It consists of a Raspberry Pi 3 B+ microcontroller which processes the image captured from a webcam super-imposed on the glasses of the blind person. Text detection is achieved using the OpenCV software and open source Optical Character Recognition (OCR) tools Tesseract and Efficient and Accurate Scene Text Detector ( EAST) based on Deep Learning techniques. The recognized text is further processed by Google's Text to Speech (gTTS) API to convert to an audible signal for the user. A second feature of this solution is to provide location-based services to the blind people by identifying locations in an academic building using the RFID technology. This solution has been extensively tested in a university environment for aiding visually challenged students. The novelty of the implemented solution lies in providing the desired computer vision functionalities of image/text recognition which is economical, small-sized, accurate and uses open source software tools. This solution can be potentially used for both educational and commercial applications.

关键词： image Recognition Speech processing Optical Character Recognition Deep Learning Raspberry Pi Python

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image Captioning with Clause-Focused Metrics in a Multi-Modal Setting for Marketing 2

Image Captioning with Clause-Focused Metrics in a Multi-Moda...

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

作者： Harzig, Philipp Zecha, Dan Lienhart, Rainer Kaiser, Carolin Schallner, Rene Univ Augsburg Multimedia Comp & Comp Vis Lab D-86159 Augsburg Germany GfK Verein D-90419 Nurnberg Germany

ISBN: (纸本)9781728111988

Automatically generating descriptive captions for images is a well-researched area in computer vision. However, existing evaluation approaches focus on measuring the similarity between two sentences disregarding fine-grained semantics of the captions. In our setting of images depicting persons interacting with branded products, the subject, predicate, object and the name of the branded product are important evaluation criteria of the generated captions. Generating image captions with these constraints is a new challenge, which we tackle in this work. By simultaneously predicting integer-valued ratings that describe attributes of the human-product interaction, we optimize a deep neural network architecture in a multi-task learning setting, which considerably improves the caption quality. Furthermore, we introduce a novel metric that allows us to assess whether the generated captions meet our requirements (i.e., subject, predicate, object, and product name) and describe a series of experiments on caption quality and how to address annotator disagreements for the image ratings with an approach called soft targets. We also show that our novel clause-focused metrics are also applicable to other image captioning datasets, such as the popular MSCOCO dataset.

关键词： image captioning multi-task learning marketing analysis lstm multi-modal learning

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Application of Multi-Stage Filtering and Multi-Layer Model in the Context of Dark and Non uniformly Illuminated images 2

Application of Multi-Stage Filtering and Multi-Layer Model i...

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

作者： Govind, Lekshmi P. Josemartin, M.J. Rajiv Gandhi Institute of Technology Dept. of ECE Kottayam India

ISBN: (纸本)9781728102832

The increasing relevance of photography leads to the development of image processing techniques. Among the entire image processing steps., image enhancement is one of the most important one. Nowadays image enhancement is used for various applications like object recognition., remote sensing., surveillance., computer vision., etc. In this paper an image enhancement algorithm which enhances dark and non-uniformly illuminated images affected by bad weather conditions like rain., fog and haze are presented. This method is a combination of multi-stage filtering of rainy image and multi-layer model based image enhancement algorithms. The whole process involves three steps. They are rain streak removal., fog or haze removal and image enhancement. Experimental results show that the presented method improves the visual quality of images very well. PSNR and SSIM values are calculated and compared with existing methods. © 2019 IEEE.

关键词： image enhancement

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A Deep Neural Network Framework for Road Side Analysis and Lane Detection 2nd

A Deep Neural Network Framework for Road Side Analysis and L...

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

作者： Shukla, Utkarsh Mishra, Ayush Jasmine, Graceline S. Vaidehi, V. Ganesan, Subramaniam ATLAN Delhi 110030 India DailyCutting Bangalore 560044 Karnataka India Vellore Inst Techonol Chennai 600127 Tamil Nadu India Mother Teresa Womens Univ Kodaikanal 624101 Tamil Nadu India Oakland Univ Rochester Rochester MI USA

This paper presents a computer vision based framework with the aim of aiding the task of driving. The framework serves the purpose of road analysis. Road analysis is further divided into two sub-tasks. The first task aims at recognition of the different road signs, the second task aims at lane analysis. The task of automatic driving requires humans to multitask and perform many operations in split seconds. The framework is introduced to aid this task of driving if not completely automate it while keeping in mind of using it with a simple hardware and software setup. The effectiveness of the framework lies in its feature of having minimal complexity which enables it to be used in real-time. The results of the pipeline are quantified by first measuring its accuracy in the classification of road signs, second measuring its ability to gather the information about the road (lane analysis and 2 vehicle detection) thirdly by performing the time bench -marking. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： computer vision image processing Vehicle Detection Convolution Neural Network Transfer Learning

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Automatic Framework for Extraction of Red Lesion Using Gabor Filter from Fundus image 19

Automatic Framework for Extraction of Red Lesion Using Gabor...

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2nd international conference on Applications of Intelligent Systems (APPIS)

作者： Singh, Astha Srivastava, Shubhi Yadav, Anjali Dutta, Malay Kishore Travieso, Carlos M. Dr APJ Abdul Kalam Tech Univ Ctr Adv Studies Lucknow Uttar Pradesh India Univ Las Palmas Gran Canaria IDeTIC Signals & Commun Dept Las Palmas Gran Canaria Spain

ISBN: (纸本)9781450360852

The paper proposes an automated computer vision method for detection of red lesions present in the fundus images. In case of Diabetic Retinopathy, red lesions constitute of both micro-aneurysms and haemorrhages. In the proposed work, the possible candidate pixels similar to red lesions with respect to intensity levels are identified and subjected to a logical subtraction operation that leads to desirable result in segmenting out the lesions only. Strategic use of Gabor filter helps in identification of blood vessels and geometrical features-based thresholding is applied to extract red lesions efficiently and make the algorithm effective. Developed algorithm is tested on a comprehensive digital fundus image database and the obtained results are encouraging with a high accuracy and has low computation cost and can be deployed for the detection of the red lesions from fundus images.

关键词： Red Lesions fundus image extraction image processing morphological operations

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Feature Extraction from Smartphone images by Using Elliptical Fourier Descriptor, Centroid and Area for Recognizing Indonesian Sign Language SIBI (Sistem Isyarat Bahasa Indonesia) 2

Feature Extraction from Smartphone Images by Using Elliptica...

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2nd international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Fauzan, Mohamad Harits Nur Rakun, Erdefi Hardianto, Dadan Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781728126623

Sistem Isyarat Bahasa Indonesia (SIBI) is the official sign language in Indonesia. This research aims to create a translator for SIBI to be installed on a smartphone. The translator will analyze gestures for inflectional words, which are root words combined with prefixes, and/or suffixes. The feature extraction method that was used in this research is Elliptical Fourier Descriptor (EFD), additionally centroid and area data were added to retain information on hand orientation, position and shape. The extracted features will be fed into, a Long Short-Term Memory (LSTM) model which will then recognize gestures into text. The method used in this research produced 99% accuracy for root word gestures, 71% accuracy for prefix gestures, 86% accuracy for suffix gestures.

关键词： feature extraction computer vision machine learning deep learning image processing sign language

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Defect detection system for optical element surface based on machine vision 2

Defect detection system for optical element surface based on...

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2nd IEEE international conference on Information Systems and computer Aided Education, ICISCAE 2019

作者： Lin, Yuqing Xiang, Yichuan Lin, Youxi Yu, Jie Beijing Institute of Technology School of Information and Electronics Beijing China Fuzhou University School of Mechanical Engineering and Automation Fuzhou China Fuzhou University Electromechanical Engineering Practice Center Fuzhou China

ISBN: (数字)9781728130668

ISBN: (纸本)9781728130668

Machine vision is now an excellent tool for testing a wide range of industrial products such as textiles, printed circuit boards, electronic component tags, integrated circuits (ICs) and machine tools. In this paper, we propose an optical lens detection system based on machine vision, which can be widely used in the defect detection of camera lenses, eyeglass lenses and other related optical components. Firstly, the hardware composition of the entire system and the flow of the control system were introduced. Secondly, through the image preprocessing algorithm such as image denoising and contrast enhancement, combined with the algorithm of Canny operator and morphological processing, the reasonable image processing results were obtained. Finally, a decision tree classifier based on C4.5 algorithm was established according to the shape feature of the defect, which realized the automatic recognition and classification of surface defects on optical components. The experimental results show the effectiveness of this method in the detection of optical component defects, which provides a theoretical basis for the functional analysis of optical components in the future. Meanwhile, it can be extended to defect detection in many fields such as circuit boards and integrated circuits. © 2019 IEEE.

关键词： Integrated circuits

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Comparative Analysis of Various Face Detection Methods 2

Comparative Analysis of Various Face Detection Methods

引用

2nd IEEE-Pune-Section Annual international conference (IEEE PuneCon)

作者： Ganakwar, Deepali G. Kadam, Vipulsangarm K. Dr Babasaheb Ambedkar Marathwada Univ Dept Engn & Technol Aurangabad Maharashtra India PES Coll Engn Dept Elect Engn Aurangabad Maharashtra India

ISBN: (纸本)9781728119243

with wide variety of increase in image and video database, the demand raises for automatic examination of this database as it is cumbersome in manual understanding and examination. This paper provides brief insights into some of renowned and mostly accepted Techniques of face detection. Face detection technique can be simply defined as a technology used by computer system that detects one or several human faces resulting in digital image. Recognizing and tracking the face, estimating pose and expressions, analysis of face and detecting any other features of face are the steps included in face detection method. Nowadays, face detection techniques owes one of the most active research areas of computer vision. Considering the face as an object that grabs countless applications in image processing makes it challenging task in computer vision. This paper provides a survey of existing literature on human face detection system. Three commonly used methods have been considered for comparative analysis in this paper.

关键词： Face detection integral image Adaboost algorithm Hausdorff distance

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Recognizing Indonesian Sign Language Gestures Using Features Generated by Elliptical Model Tracking and Angular Projection 2

Recognizing Indonesian Sign Language Gestures Using Features...

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2nd international conference on Intelligent Autonomous Systems (ICoIAS)

作者： Adimas, Drianka Mahdy Rakun, Erdefi Hardianto, Dadan Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781728126623

In this paper, we propose a method of feature extraction applied to the hands, fingers and arms, intended to improve the accuracy of Sistem Isyarat Bahasa Indonesia (SIBI) gesture recognition. Using common smartphone camera, we recorded multiple sequence of gestures used to sign for affixes and root words in SIBI then applied image processing and tracking methods to extract features from the shape and location of the hands. To extract the features, skin color segmentation was applied to separate hands and face blob from the background. Then the object would be registered to an ellipse model and tracked through the videos using elliptical model tracking. Finally, the video was then processed frame by frame, and each successfully tracked object is subjected to angular projection to generate the aforementioned features. The model that was used to recognize SIBI gestures is 2-layers Long Short-Term Memory (LSTM) neural network. Accuracy of the proposed method is measured by comparing the prediction with the actual gesture of the testing data. The highest level of accuracy achieved for the prefix, root and suffix datasets are 91.74%, 98.94%, and 97.71% respectively.

关键词： computer vision Pattern Recognition image processing Machine Learning Deep Learning Sign Language Feature Extraction

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