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2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

作者： Romero, Marcelo Flores, Cesar Muñoz, Vianney Altamirano, Luis Carlos Universidad Autónoma del Estado de México Mexico Benemérita Universidad Autónoma de Puebla Mexico

ISBN: (纸本)1601324421

This paper introduces an original investigation that takes advantages of an economical depth-map camera and state of the art algorithms for 3D face recognition. For this research, we have prototyped a face recognition system that uses Kinect depth-maps to grant access into our Human computer Interaction Laboratory. Our implemented prototype uses Eigensurface for recognition, embedded into an online functional system that accessing its enrolled-users’ records would be able to allow/deny access into our facilities. Our face recognition system consists of three main stages. Firstly, a user’s depth-map is acquired using the Kinect Sensor and Matlab’s standard image acquisition Toolbox. Secondly, it is carried-out an innovative and effective face detection process that corresponds a point-cloud face model to the acquired image to detect and extract the facial surface. After that, the face image is processed to get smoothed and scaled data. Thirdly, recognition is performed based on the Eigensurface algorithm. We have evaluated both face recognition scenarios: identification and verification. So far, our preliminary experimental results are encouraging our final aim in obtaining an economical and functional face recognition system. © CSREA Press.

关键词： Human computer interaction

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computer vision Based Feature Extraction of Leaves for Identification of Medicinal Values of Plants

Computer Vision Based Feature Extraction of Leaves for Ident...

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7th IEEE international conference on Computational Intelligence and Computing Research (ICCIC)

作者： Venkataraman, D. Mangayarkarasi, N. Amrita Univ Dept Comp Sci & Engn Amrita Sch Engn Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India

ISBN: (纸本)9781509006120

Plants are considered as one of the greatest assets in the field of Indian Science of Medicine called Ayurveda. Some plants have its medicinal values apart from serving as the source of food. The innovation in the allopathic medicines has degraded the significance of these therapeutic plants. People failed to have their medications at their door step instead went behind the fastest cure unaware of its side effects. One among the reasons is the lack of knowledge about identifying medicinal plants among the normal ones. So, a vision based approach is being employed to create an automated system which identifies the plants and provides its medicinal values thus helping even a common man to be aware of the medicinal plants around them. This paper discusses about the formation of the feature set which is the important step in recognizing any plant species.

关键词： SVM image pre processing Probabilistic Neural Network Principal Component Analysis Feature Extraction

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Real-time Analysis of Baseball Pitching using image processing on Smartphone 20th

Real-time Analysis of Baseball Pitching using Image Processi...

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20th international conference on Knowledge - Based and Intelligent Information and Engineering Systems (KES)

作者： Yamaguchi, Yosuke Miura, Motoki Kyushu Inst Technol Dept Appl Sci Integrated Syst Engn 1-1 Sensui Kitakyushu Fukuoka 8048550 Japan Kyushu Inst Technol Fac Basic Sci 1-1 Sensui Kitakyushu Fukuoka 8048550 Japan

Pitching is one of the most important elements of baseball. Therefore, both professional and amateur players are interested in measuring their pitching performance, particularly their pitch speed. Conventional equipment such as radar speed guns can be used to measure pitch speed;however this approach is unpopular among amateur players because of its cost. Therefore, we proposed a vision-based speed-measuring method for baseball pitches and developed a smartphone application called iPhoneSG, which can measure the speed of pitched baseballs on a real baseball diamond in near-real-time using a standalone image-processing technique on the smartphone. Relative to conventional radar speed guns, which must be placed on the extended line of the ball trajectory, iPhoneSG widens the possible area of data acquisition. Considering the popularity of smartphones, iPhoneSG provides players with a convenient means of measuring pitch speeds. We confirmed the basic viability of the proposed method as implemented in our iPhoneSG application. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of KES international

关键词： image processing Mobile computing Sports Baseball pitching analysis Velocity measurement

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An Efficient Automated Method for Exudates Segmentation using image Normalization and Histogram Analysis 9

An Efficient Automated Method for Exudates Segmentation usin...

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9th international conference on Contemporary Computing (IC3)

作者： Gupta, Ashmita Issac, Ashish Sengar, Namita Dutta, Malay Kishore Amity Univ Dept Elect & Commun Engn Noida India

ISBN: (纸本)9781509032518

Exudates are one of the abnormalities present on the retina which are used for identification of diseases like Diabetic Retinopathy and Macular Edema. There arises a need for automated and correct segmentation of exudates from digital fundus images. This paper proposes an automated computer vision technique for efficient exudates segmentation from fundus images. The proposed method segments the exudates using an adaptive intensity based threshold which is selected by strategically combining first order statistical parameters and local thresholding based method. The proposed technique correctly detects exudates from the fundus images with an average computation time of 9 seconds. The proposed method is computationally fast and can be used in image processing based applications for diagnosis of ocular diseases.

关键词： Medical Imaging Fundus image Exudates image Normalization Histogram Analysis

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A leucocytes count system from blood smear images Segmentation and counting of white blood cells based on learning by sampling

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MACHINE vision AND APPLICATIONS 2016年第8期27卷 1151-1160页

作者： Di Ruberto, Cecilia Loddo, Andrea Putzu, Lorenzo Univ Cagliari Dept Math & Comp Sci Via Osped 72 Cagliari Italy

Automated blood cell counting instruments are very important tools, daily used by haematologists and medical analysts to perform a complete blood count (CBC). The results of the CBC may be complex to interpret but could lead to important decisions regarding the patient medical treatment. The main focus of this research is oriented to a CBC technique, named white blood cell count (WBCC). Generally, the WBCC is performed by skilled medical operators on peripheral blood smears in order to make a correct count and to obtain useful information such as cell abnormalities or the physical status. The manual WBCC is associated with several challenges, in fact it is a time-consuming, labour intensive and expensive process. This paper introduces a reliable automated WBCC system based on image processing techniques. The main aims are to speed up and to improve the accuracy of the WBCC process. The proposed automated system introduces a new approach to segment white blood cells taking into account the knowledge acquired from a training set formed of the three main classes elements, the white blood cells, the red blood cells and the plasma present in a blood smear image. The segmented regions containing only the white blood cells are subjected to a further step in which the count is performed using the circular Hough transform exploiting the grey-level information. The method has been tested on three different public datasets, in order to highlight the accuracy of the segmentation approach with different colour images and illumination conditions. The experimental results obtained on these datasets demonstrate that the proposed method is very accurate and robust achieving an accuracy of at least 99.2% in white blood cells counting.

关键词： Automatic detection Biomedical image processing Segmentation Machine learning White blood cell counting

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Automated detection of bright lesions from contrast normalized fundus images 9

Automated detection of bright lesions from contrast normaliz...

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9th international conference on Contemporary Computing (IC3)

作者： Issac, Ashish Madan, Rishabh Dutta, Malay Kishorc Travicso, Carlos M. Amity Univ Dept Elect & Commun Engn Noida India Univ Las Pamas de Gran IDeTIC Signal & Commun Dept Canaria Spain

ISBN: (纸本)9781509032518

Exudates are one of the abnormalities present in the eye which can lead to vision loss. Fundus images may consist of artifacts which occur during image acquisition and hamper the accuracy of detection of exudates. There is a need to develop an image processing based techniques for automated and correct segmentation of exudates from fundus images. This paper demonstrates an automatic computer vision algorithm for efficient identification of the exudates from fundus images by strategic fusion of techniques i.e. contrast normalization, top-hat transformation and average filtering. The proposed technique correctly detects exudates from the fundus images and rejects the artifacts and reflections. The average computation time for exudates segmentation from fundus images is 11 seconds. The proposed method is computationally efficient and robust and can be used for real time applications.

关键词： Fundus image Exudates Diabetic Retinopathy Top-Hat Transform Contrast adjustment Average Filter

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Intelligent Adaptive Object Recognition 12

Intelligent Adaptive Object Recognition

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12th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Almazmome, Safa Zhang, Li Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England Northumbria Univ Dept Comp Sci & Digital Technol Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (纸本)9781509040933

This research proposes an object recognition system using image processing and neural network based classification. The system is capable of recognizing 7 objects from an uncluttered background by extracting color, texture and shape features. The proposed system consists of image segmentation, feature extraction and classification. Diverse neural network topology settings have been employed for evaluation. Experimental results indicate that the proposed system achieves high accuracy 98% accurate for real-time object recognition tasks.

关键词： Object recognition neural networks computer vision and image processing

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Real-Time Defects Detection System for Orange Citrus Fruits Using Multi-Spectral Imaging 59

Real-Time Defects Detection System for Orange Citrus Fruits ...

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59th IEEE international Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Abdelsalam, Ahmed M. Sayed, Mohammed S. Egypt Japan Univ Sci & Technol Elect & Commun Engn New Borg El Arab Egypt Zagazig Univ Elect & Commun Engn Zagazig Egypt

ISBN: (纸本)9781509009169

In this paper, we propose a computer vision system that detects the external defects of orange citrus fruits using multi-spectral imaging sensor. First, the proposed algorithm segments the orange fruit from the captured Near-Infra Red (NIR) and RGB images using only the NIR component. Second, some adaptive pre-processing techniques are applied on the segmented RGB and NIR orange fruit images. Hence, a thresholding technique is utilized in order to detect the defects in seven different color components of the orange fruit. Finally, a voting process is applied on the seven thresholded color components images to determine if the citrus fruit image is defected or defect free. The overall accuracy of the algorithm is more than 95%, and the proposed algorithm can process three images of resolution (640x480 pixels) per second.

关键词： Citrus Grading computer vision image processing NIR Defects Detection

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Hyperspectral vision control of environmental impacts in civil works

Hyperspectral vision control of environmental impacts in civ...

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2016 international conference on image processing, computer vision, and Pattern Recognition, IPCV 2016

作者： Sorrosal, G. Solabarrieta, L. Larrauri, J.I. Borges, C.E. Alonso-Vicario, A. Deusto Institute of Technology – DeustoTech Energy University of Deusto Unibertsitate Etorbidea 24 Bilbao48007 Spain

ISBN: (纸本)1601324421

This work aims to develop a system for the automation of the actuation protocols against possible environmental impacts generated by civil works. This system involves from the detection, control and physic tracking of the impact to the generation of alarms and reports to facilitate the decision making. This work is part of the LIFE+ research project visionTech4Life funded by the European Commission. The automated actuation protocol is based on inspection flights done by a drone equipped with a hyperspectral system. processing the captured images, it will be possible to detect the presence of certain common contaminants in civil works, reducing both detection times and laboratory tests costs. © CSREA Press.

关键词： Environmental impact

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A Corpus of images and Text in Online News 10

A Corpus of Images and Text in Online News

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10th international conference on Language Resources and Evaluation (LREC)

作者： Hollink, Laura Bedjeti, Adriatik van Harmelen, Martin Elliott, Desmond CWI Amsterdam Netherlands Univ Amsterdam ILLC Amsterdam Netherlands

ISBN: (纸本)9782951740891

In recent years, several datasets have been released that include images and text, giving impulse to new methods that combine natural language processing and computer vision. However, there is a need for datasets of images in their natural textual context. The ION corpus contains 300K news articles published between August 2014 - 2015 in five online newspapers from two countries. The 1-year coverage over multiple publishers ensures a broad scope in terms of topics, image quality and editorial viewpoints. The corpus consists of JSON-LD files with the following data about each article: the original URL of the article on the news publisher's website, the date of publication, the headline of the article, the URL of the image displayed with the article (if any), and the caption of that image. Neither the article text nor the images themselves are included in the corpus. Instead, the images are distributed as high-dimensional feature vectors extracted from a Convolutional Neural Network, anticipating their use in computer vision tasks. The article text is represented as a list of automatically generated entity and topic annotations in the form of Wikipedia/DBpedia pages. This facilitates the selection of subsets of the corpus for separate analysis or evaluation.

关键词： online news image features topic extraction

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