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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Low-level computer vision techniques for processing of extensive air shower track images 14

Low-level computer vision techniques for processing of exten...

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14th IEEE international Scientific conference on Informatics

作者： Vrabel, Michal Genci, Jan Bobik, Pavol Tech Univ Kosice Fac Elect Engn & Informat Technol Letna 9 Kosice 04001 Slovakia Slovak Acad Sci Inst Expt Phys Watsonova 47 Kosice 04001 Slovakia

ISBN: (纸本)9781538608890

An extensive air shower can be observed as a bright spot moving through the field of view of an orbital fluorescence detector. A challenging part of the air shower recognition is segmentation of its track. The issues arise from a low signal to noise ratio. This paper provides a short review of selected low-level computer vision techniques such as filtering and thresholding methods, which are for a demonstration applied to a composite simulated air shower image. The article should provide a shortlist of algorithms that can be applied as a part of more complex event classification or reconstruction procedure.

关键词： Detectors computer vision image edge detection Thresholding (Imaging) image segmentation Feature extraction

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Omega-3 Chicken Egg Detection System using a Mobile-based image processing Segmentation Method 8

Omega-3 Chicken Egg Detection System using a Mobile-based Im...

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8th international conference on Graphic and image processing (ICGIP)

作者： Nurhayati, Oky Dwi Teguh, Kurniawan M. Amalia, Cintya P. Diponegoro Univ Engn Fac Multimedia Lab Comp Engn Semarang Indonesia

ISBN: (纸本)9781510609518;9781510609525

An Omega-3 chicken egg is a chicken egg produced through food engineering technology. It is produced by hen fed with high omega-3 fatty acids. So, it has fifteen times nutrient content of omega-3 higher than Leghorn's. Visually, its shell has the same shape and colour as Leghorn's. Each egg can be distinguished by breaking the egg's shell and testing the egg yolk's nutrient content in a laboratory. But, those methods were proven not effective and efficient. Observing this problem, the purpose of this research is to make an application to detect the type of omega-3 chicken egg by using a mobile-based computer vision. This application was built in OpenCV computer vision library to support Android Operating System. This experiment required some chicken egg images taken using an egg candling box. We used 60 omega-3 chicken and Leghorn eggs as samples. Then, using an Android smartphone, image acquisition of the egg was obtained. After that, we applied several steps using image processing methods such as Grab Cut, convert RGB image to eight bit grayscale, median filter, P-Tile segmentation, and morphology technique in this research. The next steps were feature extraction which was used to extract feature values via mean, variance, skewness, and kurtosis from each image. Finally, using digital image measurement, some chicken egg images were classified. The result showed that omega-3 chicken egg and Leghorn egg had different values. This system is able to provide accurate reading around of 91%.

关键词： chicken egg P-Tile computer vision morphology feature extraction mobile

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Biomedical image mosaicing: A review

Biomedical image mosaicing: A review

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international conference on Communication and Computing Systems, ICCCS 2016

作者： Pol, Kshitija Chaudhary, Diksha Bansal, Parul Dronacharya College of Engineering Gurgaon India

ISBN: (纸本)9781138029521

One of key research area in the field of image processing & computer vision is image mosaicing. When image mosaicing used for medical diagnosis purpose it is called as Biomedical image Mosaicing. This paper takes an overview of how image mosaicing is used in medical application such as X-Ray stitching, Visualization of Tissue structure, Robotically-assisted Biomedical image mosaicing system, Blood Vessel image, whole Body MRI. © 2017 Taylor & Francis Group, London.

关键词： Medical applications

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A redefined codebook model for dynamic backgrounds

A redefined codebook model for dynamic backgrounds

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international conference on computer vision and image processing, cvip 2016

作者： Sharma, Vishakha Nain, Neeta Badal, Tapas Computer Science and Engineering Department Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9789811021060

Dynamic background updation is one of the major challenging situation in moving object detection, where we do not have a fix reference background model. The background model maintained needs to be updated as and when moving objects add and leave the background. This paper proposes a redefined codebook model which aims at eliminating the ghost regions left behind when a non-permanent background object starts to move. The background codewords which were routinely deleted from the set of codewords in codebook model are retained in this method while deleting the foreground codewords leading to ghost elimination. This method also reduces memory requirements significantly without effecting object detection, as only the foreground codewords are deleted and not background. The method has been tested for robust detection on various videos with multiple and different kinds of moving backgrounds. Compared to existing multimode modeling techniques our algorithm eliminates the ghost regions left behind when non permanent background objects starts to move. For performance evaluation, we have used similarity measure on video sequences having dynamic backgrounds and compared with three widely used background subtraction algorithms. © Springer Science+Business Media Singapore 2017.

关键词： Object detection

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On sphering the high resolution satellite image using fixed point based ICA approach

On sphering the high resolution satellite image using fixed ...

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international conference on computer vision and image processing, cvip 2016

作者： Singh, Pankaj Pratap Garg, R.D. Department of Computer Science & Engineering Central Institute of Technology Kokrajhar BTAD KokrajharAssam India Geomatics Engineering Group Department of Civil Engineering Indian Institute of Technology Roorkee RoorkeeUttarakhand India

ISBN: (纸本)9789811021060

On sphering the satellite data, classified images are achieved by many authors that had tried to reduce the mixing effect in image classes with the help of different Independent component analysis (ICA) based approaches. In these cases multispectral images are limited with small spectral variation in heterogeneous classes. For better classification, high spectral variance among different classes and low spectral variance within a particular class should exhibit. In the consideration of this issue, a Fixed point (FP) based Independent Component Analysis (ICA) method is utilized to get better classification accuracy in the existing mixed classes that consist similar spectral behavior. This FP-ICA method identifies the objects from mixed classes having similar spectral characteristics, on sphering high resolution satellite images (HRSI). It also helps to reduce the effect of similar spectral behavior between different image classes. The estimation of independent component related to non-gaussian distribution data (image) with optimizing the performance of this approach with the help of nonlinearity, which utilize the low variance between similar spectral classes. It is quite robust, effortless in computation and high convergence rate, even though the spectral distributions of satellite images are rigid to classify. Hence, this FP-ICA approach plays a key role in image classification such as buildings, grassland area, road, and vegetation. © Springer Science+Business Media Singapore 2017.

关键词： image classification

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The software for automatic creation of the formal grammars used by speech recognition, computer vision, editable text conversion systems, and some new functions 8

The software for automatic creation of the formal grammars u...

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2016 8th international conference on Graphic and image processing, ICGIP 2016

作者： Kardava, Irakli Tadyszak, Krzysztof Gulua, Nana Jurga, Stefan Sokhumi State University Politkovskaia 9 Tbilisi0186 Georgia NanoBioMedical Centre Adam Mickiewicz University Umultowska 85 PoznanPL 61614 Poland

ISBN: (纸本)9781510609518

For more flexibility of environmental perception by artificial intelligence it is needed to exist the supporting software modules, which will be able to automate the creation of specific language syntax and to make a further analysis for relevant decisions based on semantic functions. According of our proposed approach, of which implementation it is possible to create the couples of formal rules of given sentences (in case of natural languages) or statements (in case of special languages) by helping of computer vision, speech recognition or editable text conversion system for further automatic improvement. In other words, we have developed an approach, by which it can be achieved to significantly improve the training process automation of artificial intelligence, which as a result will give us a higher level of self-developing skills independently from us (from users). At the base of our approach we have developed a software demo version, which includes the algorithm and software code for the entire above mentioned component's implementation (computer vision, speech recognition and editable text conversion system). The program has the ability to work in a multi - stream mode and simultaneously create a syntax based on receiving information from several sources. © 2017 SPIE.

关键词： computer vision

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A hierarchical shot boundary detection algorithm using global and local features

A hierarchical shot boundary detection algorithm using globa...

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international conference on computer vision and image processing, cvip 2016

作者： Verma, Manisha Raman, Balasubramanian Mathematics Department IIT Roorkee Roorkee India Computer Science and Engineering Department IIT Roorkee Roorkee India

ISBN: (纸本)9789811021060

Avideo is considered as high dimensional datawhich is tedious to process. Shot detection and key frame selection are activities to reduce redundant data from a video and make it presentable in few images. Researchers have worked in this area diligently. Basic shot detection schemes provide shot boundaries in a video sequence and key frames are selected based on each shot. Usually in video clips, shots repeat after one another, in that case the basic shot detection scheme gives redundant key frames from same video. In this work, we have proposed a hierarchical shot detection and key frames selection scheme which reduce a considerable amount of redundant key frames. For temporal analysis and abrupt transformation detection, color histogram has been used. After shot detection, spatial analysis has been done using local features. Local binary patterns have been utilized for local feature extraction. The proposed scheme is applied to three video sequences of news video, movie clip and tv-advertisement video. © Springer Science+Business Media Singapore 2017.

关键词： Local binary pattern

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Extending Guided image Filtering for High-Dimensional Signals 11

Extending Guided Image Filtering for High-Dimensional Signal...

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11th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP)

作者： Fujita, Shu Fukushima, Norishige Nagoya Univ Nagoya Aichi Japan Nagoya Inst Technol Nagoya Aichi Japan

ISBN: (纸本)9783319648705;9783319648699

This paper presents an extended method of guided image filtering (GF) for high-dimensional signals and proposes various applications for it. The important properties of GF include edge-preserving filtering, local linearity in a filtering kernel region, and the ability of constant time filtering in any kernel radius. GF can suffer from noise caused by violations of the local linearity when the kernel radius is large. Moreover, unexpected noise and complex textures can further degrade the local linearity. We propose high-dimensional guided image filtering (HGF) and a novel framework named combining guidance filtering (CGF). Experimental results show that HGF and CGF can work robustly and efficiently for various applications in image processing.

关键词： image processing

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A comparative review of fast, surf and brisk feature descriptors

A comparative review of fast, surf and brisk feature descrip...

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international conference on Communication and Computing Systems, ICCCS 2016

作者： Makkar, Alisha Kaur, Bikrampal Department of Computer Science and Engineering Chandigarh Engineering College Landran MohaliPunjab India

ISBN: (纸本)9781138029521

The concept of feature detection is a method to compute abstraction of image information at every point of an image and making local decision at that particular point that there is a feature in an image or not under image processing and computer vision. In this a comparison between three keypoint descriptors have been done and proposed a new combined approach to detect the keypoints present in an image. One of the descriptor is SURF descriptor and other two are new are called BRISK and FREAK. Thus by dividing the time by the number of feature the average time taken for a single feature can be calculated, lesser the number greater is Descriptor in terms of speed and lesser computation power. In the end a flow chart of new proposed methodology is drawn and in future new methodology would be implemented and compare with the other descriptor in my future work. © 2017 Taylor & Francis Group, London.

关键词： image processing

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Count-ception: Counting by Fully Convolutional Redundant Counting 16

Count-ception: Counting by Fully Convolutional Redundant Cou...

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16th IEEE international conference on computer vision (ICCV)

作者： Cohen, Joseph Paul Boucher, Genevieve Glastonbury, Craig A. Lo, Henry Z. Bengio, Yoshua Univ Montreal Montreal Inst Learning Algorithms Montreal PQ Canada Harvard Univ Herbaria Cambridge MA 02138 USA Univ Montreal Inst Res Immunol & Canc Montreal PQ Canada Univ Oxford Big Data Inst Oxford England Univ Massachusetts Dept Comp Sci Boston MA 02125 USA Univ Montreal Montreal Inst Learning Algorithms CIFAR Montreal PQ Canada

ISBN: (纸本)9781538610343

Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016.

关键词： Kernel image segmentation Predictive models Training conferences Digital images

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