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Development and Application of the computer vision Technology

Development and Application of the Computer Vision Technolog...

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2016 3rd international conference on Management Innovation and Business Innovation(ICMIBI 2016)

作者： Xiaowen Liu Xinjiang Vocation & Technical College of Construction

The computer vision is the analysis and processing of the visual information by the computer. The theories and technologies related to the researches on the computer vision try to establish a manual system to obtain the information from the images or the multidimensional data. This paper briefly introduces the development of the computer vision, the relationship between the computer vision and the related fields, and the image processing methods in the information processing of the computer vision. The research target of the computer vision is to enable the computers to have the ability of the autonomous adaptation to the environment like the humans through the visual observation and understandings of the world, and to make the computers have the abilities to perceive the geometrical information in the 3D environment including shapes, positions, gestures and motions and so on through the cognition of the two-dimensional images and the three-dimensional environmental information, to carry on the description, storage, identification and understanding.

关键词： computer vision technology Development ideas Application strategy

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Unsupervised abnormal crowd activity detection in surveillance systems

Unsupervised abnormal crowd activity detection in surveillan...

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international conference on Systems, Signals and image processing, IWSSIP

作者： Łukasz Kamiński Paweł Gardziński Krzysztof Kowalak Sławomir Maćkowiak Poznan University of Technology Poznan Poland

We propose an unsupervised method for abnormal crowd activity detection in surveillance systems. Proposed solution is using MPEG-7 Motion Activity descriptors and Particle Filter algorithm for classification. The experiments were performed on UMN dataset sequences. The detection results are comparable to results obtained by supervised methods.

关键词： Classification algorithms conferences Particle filters computer vision Transform coding Surveillance Atmospheric measurements

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Statistical analysis of lung CT images for ground glass diagnosis 2

Statistical analysis of lung CT images for ground glass diag...

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2nd IEEE international conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, IEEE - AEEICB 2016

作者： Arabi, Punal M. Krishna, Nanditha Deepa, N. Vamsha Bai, P. Lakshmi Department of Biomedical Engineering ACSCE Bangalore India

ISBN: (纸本)9781467397452

Lung is an important organ in human body that performs vital functions for every second. Hence we need to consider the main lung abnormalities in detection, diagnosis and also treatment of detected abnormality if it is possible. Also lung diseases are said to be the main reason for death around the world. The main reasons behind the lung diseases are inhaling chemicals, inhaling dusts, or even due to bacterial action. According to a survey done on Indian people approximately 70275 were affected by major lung diseases, among which 63759 people were not in a condition to be cured and caused their deaths. computer aided diagnosis (CAD) system for thoracic computed tomography are used to detect and characterize most of the lung abnormalities with a high a degree of accuracy in a short time period. High-resolution Computed Tomography (HRCT) is a method used for diagnosing the lung diseases. In This paper a study on lung abnormality which is characterized by a slight increase in lung density with persistent visibility of vascular structures and bronchial walls called as ground glass structures are imaged. Ground glass structure is a marker for pulmonary edema, desquamative pneumonitis, hypersensitive pneumonitis, pneumocystis carinii pneumonia. image processing techniques play a significant role in the diagnosis of many lung diseases. This paper presents a novel method for ground glass structure diagnosis using statistical feature analysis using gray level co-occurrence matrix (GLCM). These features based on GLCM are considered to observe the textural patterns in lung images which can differentiate between abnormal lung and normal lung. This method is experimented on a set of two normal and ground glass lung images. The results obtained show that the proposed method is promising in ground glass texture identification. © 2016 IEEE.

关键词： computer vision

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Exploring encoding and normalization methods on probabilistic latent semantic analysis model for action recognition

Exploring encoding and normalization methods on probabilisti...

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international conference on Wireless Communications and Signal processing (WCSP)

作者： Qinjun Xu Tongchi Zhou Lin Zhou Zhenyang Wu School of Information Science and Engineering Southeast University Nanjing China College of Physics and Information Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9781509028610

Topic models have been wildly applied in the field of computer vision, through which superior performance was yielded in various recognizing tasks. Among them, probabilistic latent semantic analysis model has earned much attention due to its simplicity and effect. But the affection of encoding and normalization methods on topic models has been ignored during the period. This paper explores the impact of encoding methods combined with different normalization on probabilistic latent semantic analysis model in the context of action classification in videos. Detailed experiments are conducted on KTH and UT-interaction datasets. The results show that an appropriate combination of encoding and normalization methods could significantly improve the performance of probabilistic latent semantic analysis model. The recognition accuracy reachs 96.44% and 93.33% on UT-interaction set1 and set2 respectively, which outperforms the state-of-the-art. Especially, we obtain 94.24% on UT-interaction set1 using sparse STIPs.

关键词： video signal processing computer vision image classification image coding probability images computer vision Video signal processing latent semantic analysis image coding image classification ANALYSIS MODELS normalization method normalization SETD1A gene Probability

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Dermatological disease detection using image processing and machine learning

Dermatological disease detection using image processing and ...

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international conference on Artificial Intelligence and Pattern Recognition (AIPR)

作者： Vinayshekhar Bannihatti Kumar Sujay S Kumar Varun Saboo Computer Science Department PES Institute of Technology

ISBN: (纸本)9781467391887

Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines computer vision and Machine Learning on clinically evaluated histopathological attributes to accurately identify the disease. In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. The second stage involves the use of Machine learning algorithms to identify diseases based on the histopathological attributes observed on analysing of the skin. Upon training and testing for the six diseases, the system produced an accuracy of up to 95 percent.

关键词： Feature extraction Diseases Skin image color analysis computer vision Artificial neural networks Computational modeling

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A system on chip based serial number identification using computer vision

A system on chip based serial number identification using co...

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IEEE international conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Raj Kumar Mishra Preet Jain Electronics and Communication Department Shri Vaishnav Institute of Technology and Science Indore India

ISBN: (纸本)9781509007752

Serial number is a unique number given to a product which can be used for product identification and inventory management. Information about the product may be its manufacturing date, expiry date, place of manufacture and so on. Some companies use serial numbers to keep track on the number of products which a retailer is authorized to sell. Thus serial number identification becomes an important part at industry level. This paper presents an approach to detect and identify serial numbers using computer vision. Webcam is used to provide vision capability and Raspberry Pi which is Linux based system on chip hardware acts as the processing unit for this project making it a compact system. Various algorithms of OpenCV along with C++ language were used to extract numbers from the image. These numbers were later stored in a file to keep track of them.

关键词： Cameras Libraries computer vision Companies Hardware computers image processing

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vision-based detection of events using line-scan camera

Vision-based detection of events using line-scan camera

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international conference on Event-based Control, Communication, and Signal processing (EBCCSP)

作者： Mirosław Jabłoński Ryszard Tadeusiewicz Faculty of Electrical Engineering AGH University of Science and Technology Kraków Poland

vision-based method of event detection for control algorithm of acorn scarification device has been presented in the paper. Instead of frame-based approach, line-scan mode of digital camera has been used. The advantage of this approach is an improved temporal resolution of machine-vision-based event detector and reduced latency as the line-acquisition time is significantly lower than the acquisition time of a frame of 2D image. Segmentation of objects moving in front of the camera is one of the steps of the detection. Usage of two alternative methods of segmentation have been reported in the article: Sum of Absolute Differences and motion detection by means of adaptive Gaussian Mixture Model. Motion signal is then a subject of event detection.

关键词： Cameras Motion segmentation Event detection Silicon computer vision image segmentation Machine vision

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On the generality of neural image features

On the generality of neural image features

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IEEE international conference on image processing

作者： Ragav Venkatesan Vijetha Gatupalli Baoxin Li School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (纸本)9781467399623

Often the filters learned by Convolutional Neural Networks (CNNs) from different image datasets appear similar. This similarity of filters is often exploited for the purposes of transfer learning. This is also being used as an initialization technique for different tasks in the same dataset or for the same task in similar datasets. Off-the-shelf CNN features have capitalized on this idea to promote their networks as best transferable and most general and are used in a cavalier manner in day-to-day computer vision tasks. While the filters learned by these CNNs are related to the atomic structures of the images from which they are learnt, all datasets learn similar looking low-level filters. With the understanding that a dataset that contains many such atomic structures learn general filters and are therefore useful to initialize other networks with, we propose a way to analyse and quantify generality. We applied this metric on several popular character recognition, natural image and a medical image dataset, and arrive at some interesting conclusions. On further experimentation we also discovered that particular classes in a dataset themselves are more general than others.

关键词： Atomic layer deposition Colonoscopy Feature extraction Training image edge detection Detectors computer vision

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An adaptive detection approach for autonomous forest path following using stereo vision

An adaptive detection approach for autonomous forest path fo...

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international conference on Control, Automation, Robotics and vision (ICARCV)

作者： Patrick Fleischmann Johannes Kneip Karsten Berns Department of Computer Science University of Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781509035502

In this paper, an image-based segmentation method to improve autonomous robot navigation in the forest is presented. The detection is supported by a filtered image generated from a stereo-based pre-processing which is a byproduct of our obstacles detection system. To cope with the large variability of forest paths, the classifier is dynamically adapted to the current situation and the segmentation relies on different image features to ensure robustness against illumination changes. Furthermore, it is summarized how the detection results are transformed to the 3D space, using a plane which is extracted from the stereo data, to be stored and maintained in a probabilistic grid map.

关键词： Roads Three-dimensional displays image segmentation image color analysis Navigation Robot kinematics

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Automated cashew kernel grading using machine vision

Automated cashew kernel grading using machine vision

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international conference on Next Generation Intelligent Systems (ICNGIS)

作者： M O Aran Aneesh G Nath A Shyna Thangal Kunju Musaliar College of Engineering Kollam Kerala IN Computer Science and Engineering Department TKM College of Engineering Kollam India

ISBN: (纸本)9781509008711

Quality of food and agricultural products is vital for farmers and consumers. Quality based classification of these products is being carried out manually in the industry which is tedious and expensive. computer vision systems can be used to automate the classification process. Automation can reduce the production cost and improve the overall quality. A computer vision system captures the image of the underlying object and transmits it to an image processor. The processor, after processing the image, presents it to a pattern recognizer. The recognizer performs the quality assessments and classifies the underlying object into pre-specified quality classes. Previous studies in this regard used minimal features to perform classification which reduces the accuracy. In the proposed approach more features are used which significantly improves the automation process. Automation process faces several challenges like identifying appropriate features and the best classifier. This study tries to grade cashew nuts based on external features like color, texture, shape and size. The effect of various pre-processing operations on the grading process is also studied. Five different classifiers were used and their performance in terms of accuracy is observed. Among the classifiers, Back Propagation Neural Network proved to be the most optimal.

关键词： Feature extraction image color analysis Shape Kernel Color computer vision Automation

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