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Curvelet Transform Based Super-Resolution image processing

Curvelet Transform Based Super-Resolution Image Processing

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12th ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Thuong Le-Tien Luc Nguyen-Tan Giang Le-Thach Cao Bui-Thu Hochiminh City Univ Technol Dept Elect & Elect Engn 268 Ly Thuong KietDist 10 Ho Chi Minh City Vietnam Ind Univ Dept Elect Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9781467356046

Super-Resolution (SR) processing is a technique that produces a High - Resolution (HR) image from a range of Low - Resolution (LR) images. There are many methods for implementing the SR signal processing. In this paper, we introduce a specific transformation that were developed in the recent years called Curvelet Transform (CT), and then apply it in SR processing to obtain a quality improvement of SR images. We discuss two separate algorithms using the Discrete Curvelet Transform (DCT) and then compare them based on the results of HR images. We find out that the first algorithm, named the iterative algorithm can produce better HR images than the second one, the Interpolation algorithm. However, the iterative algorithm also take more computational cost than that of the Interpolation Algorithm. It is a reasonable trade-off between images quality and processing speed. We also made a comparison between our algorithms and previous works such as the Projection onto Convex Sets (POCS) and the Nearest Neighbourhood algorithms to show the quality improvement.

关键词： Curvelet Transform Superresolution image processing Iterative Algorithm Interpolation Algorithm POCS algorithm Nearest Neighbourhood Algorithm

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The aircraft engine blade detection system based on USB2.0 and FPGA 6

The aircraft engine blade detection system based on USB2.0 a...

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6th International Conference on computational intelligence and Communication Networks

作者： Yu, Kunlin Changsha Aeronaut Vocat & Tech Coll Changsha Hunan Peoples R China

ISBN: (纸本)9781479969296

In order to realize automatic detection of aircraft engine blade quickly and accurately, an aircraft engine blade detection system based on USB2.0 and FPGA is designed. The system first converts the image signals of aircraft engine blade for CCD video signal, then the video signals for A/D conversion and digital image is stored in the cache in FIFO, finally the USB interface circuit carry the digital image into a computer for image processing and detection. The detection system has advantage of not being affected by man-made factors, high detection efficiency and detection accuracy, it has very important application value.

关键词： The CCD image sensor USB2.0 FPGA blade detection system image processing

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An Improved SIFT Descriptor Based on In-Out Region Division 2

An Improved SIFT Descriptor Based on In-Out Region Division

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2nd ieee International Conference on signal and image processing (ICSIP)

作者： Xia, Chunlong Wei, Ping Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Peoples R China

ISBN: (纸本)9781538609699

SIFT descriptor is one of the most widely-used local features for image matching. This paper presents an in-out region division method to improve the matching rate and the computational efficiency of SIFT descriptor. The descriptor region composed of pixel blocks around each keypoint is divided into the inner and the outer regions, in which different weights are assigned to the feature vectors. In each pixel block, a low dimensional feature vector is extracted. A bidirectional verification strategy is adopted to determine the keypoint matching. The experimental results show that the proposed method greatly improves the matching rate and the computational efficiency.

关键词： SIFT region division bidrectional verification

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image Denoising using Wavelet Transform and Wiener Filter based on Log Energy Distribution over Poisson-Gaussian Noise Model 5

Image Denoising using Wavelet Transform and Wiener Filter ba...

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5th ieee International Conference on computational intelligence and Computing Research (ieee ICCIC)

作者： Boyat, Ajay Kumar Joshi, Brijendra Kumar MITM Dept ECE Indore Madhya Pradesh India MHOW Mil Coll Tele Commun Engn Dept ECE Indore Madhya Pradesh India

ISBN: (纸本)9781479939756

Generally Gaussian noise model is used in image processing applications due to its presence in electronic circuitry used in image acquisition devices like Charge Coupled Device (CCD) cameras. Off let due to discrete nature of electromagnetic radiation in image acquisition process, due to photon noise that follows poison distribution is also exist with Gaussian noise. In this paper we present an algorithm based on Wavelet transform and wiener filter using log energy distribution to denoised digital images corrupted by Poisson-Gaussian noise. Our results clearly demonstrate the robustness of the algorithm over a large range of variance. We have used peak signal to noise ratio (PSNR) for comparison based on filter outlet.

关键词： Poisson noise Guassian noise Haar wavelet Wiener filter Log energies PSNR

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The Study of Adaptive Multi Threshold Segmentation Method for Apple Fruit Based on the Fractal Characteristics 8

The Study of Adaptive Multi Threshold Segmentation Method fo...

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8th International symposium on computational intelligence and Design (ISCID)

作者： Song, Xin Yang, Lei Tianjin Agr Univ Coll Engn & Technol Tianjin Peoples R China

ISBN: (纸本)9781467395878

According to the characteristics of the fruit and vegetable mechanization picking working environment, a segmentation method for apple fruit based on fractal characteristics is proposed in the paper. Firstly, the image is converted from RGB color space to I1I2I3 color space. Secondly, the image is divided into several regions and the texture features of every region are described by using the fractal theory. Finally, the regional segmentation threshold is determined according to the fractal dimension. Thus the fractal theory and the segmentation method of adaptive multi threshold have been combined to realize the accurate segmentation in the natural working environment of the apple fruit and its background. The experimental verification showed that the method has own stronger anti-noise ability. The method can be applied to precisely and effectively segment fruits from the background in the natural working environment. By comparing with the method of traditional local threshold segmentation and threshold iteration segmentation, the method proposed in the paper has higher processing speed and better segmentation effect at the background with different complexity, and can meet the realtime request of mechanization picking operations.

关键词： fruit image segmentation fractal characteristics adaptive multi threshold color space

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image Deblurring with Impulse Noise Using Split Bregman Algorithm

Image Deblurring with Impulse Noise Using Split Bregman Algo...

引用

2nd International symposium on computational intelligence and Design

作者： Yi Liya Lu Xiaolei Wang Jinjun Huang Benxiong Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China NEC Labs Amer Inc Cupertino CA 95014 USA

ISBN: (纸本)9780769538655

we propose an effective method to resolve blurred images with impulse noise. Our method has two steps. First, an improved adaptive median filter is proposed for image denoising;And second, the problem of deblurring the denoised image is formulated as to minimize the object function which consists of L1 data-fedility term and double regularization term. The minimization problem is solved by split Bregman algorithm. Numerical results using image with different blurs and impulse noise show that the proposed method gives better performance than the variable splitting alternative minimization algorithm in [10] by objective peak signal to noise ratio and subjective vision quality, which demonstrates the efficiency of our proposed algorithms.

关键词： image deblurring impulse noise double regularization split Bregman algorithm

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An Improved Evolution-COnstructed (iECO) Features Framework Distribution Statement A: Approved for public release;distribution is unlimited.

An Improved Evolution-COnstructed (iECO) Features Framework ...

引用

ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Price, Stanton R. Anderson, Derek T. Luke, Robert H. Mississippi State Univ Elect & Comp Engn Dept Mississippi State MS 39762 USA US Army RDECOM CERDEC Night Vis & Elect Sensors Directorate Ft Belvoir VA USA

ISBN: (纸本)9781479945030

In image processing and computer vision, significant progress has been made in feature learning for exploiting important cues in data that elude non-learned features. While the field of deep learning has demonstrated state-of-the-art performance, the Evolution-COnstructed (ECO) work of Lillywhite et. al has the advantage of interpretability, and it does not predispose the solution to one of convolution. This paper presents a novel approach for extending the ECO framework. We achieve this through two overarching ideas. First, we address a potential major shortcoming of ECO features- the "features" themselves. The so-called ECO features are simply a transformed image that has been unrolled into a large one dimensional vector. We propose employing feature descriptors to extract pertinent information from the ECO imagery. Furthermore, it is our hypothesis that there exists a unique set of transforms for each feature descriptor used on a given problem domain that leads to the descriptors extracting maximal discriminative information. Second, we introduce constraints on each individual's chromosome to promote population diversity and prevent infeasible solutions. We show through experiments that our proposed iECO framework results in, and benefits from, a unique series of transforms for each descriptor being learned and maintaining population diversity.

关键词： computer vision evolutionary computation feature extraction learning (artificial intelligence) object recognition vectors 1D vector ECO imagery and computer vision feature descriptors feature learning iECO features framework image processing improved evolution-constructed features framework maximal discriminative information extraction pertinent information extraction population diversity promotion public release Biological cells Data mining Feature extraction Object recognition Sociology Statistics Transforms

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A pilot study of automatic lung tumor segmentation from positron emission tomography images using standard uptake values

A pilot study of automatic lung tumor segmentation from posi...

引用

ieee symposium on computational intelligence in image and signal processing

作者： Kanakatte, Aparna Gubbi, Jayavardhana Mani, Nallasamy Kron, Tomas Binns, David Monash Univ Dept Elect & Comp Syst Clayton Vic 3800 Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Peter MacCallum Canc Ctr Melbourne Vic 3002 Australia

ISBN: (纸本)9781424407071

Positron Emission Tomography (PET) is a medical imaging procedure that shows the physiological function of an organ or tissue. The role of PET during the past decade has evolved rapidly in the detection of lung tumors but the research on quantitative evaluation of PET images is still in its infancy. PET commonly involves scanning the patient after administration of a radioactive analogue of glucose called fluoro deoxy-glucose (FDG). Tumor cells metabolise more glucose than most normal cells. In PET lung images the heart is often visible and because of its constant pumping of blood it requires more glucose and hence both the tumor and the heart appear brighter than the rest in the PET image. In this paper we present a novel segmentation scheme for detecting the tumor alone in lung PET images using standard uptake values (SUV) and connected component analysis. We perform the segmentation in two steps. In coarse segmentation, a non linear scaling of SUV values is performed and then a threshold is chosen adaptively to convert the gray image into the binary image. Fine segmentation is performed on the coarse segmented data in order to narrow down the region of interest using connected component labeling. To our knowledge no one has used connected component analysis for segmenting PET images. We compare our proposed scheme with several commonly used medical image segmentation techniques like threshold, sobel edge detector, laplacian of gaussian (LoG) edge detectors region growing and SUV based segmentation (applied only to PET as SUV is specific to PET). One of the problems in lung tumor detection is the presence of the heart in the image which accumulates activity and often gets recognized as a hot spot (a probable tumor). All the other segmentation schemes detected both the heart and the tumor as hot spots while our segmentation scheme detected the tumor alone as the hot spot. The preliminary study of the proposed scheme has yielded very promising results and will be st

关键词： image segmentation

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A survey on the use and the construction of fuzzy similarity measures in image processing

A survey on the use and the construction of fuzzy similarity...

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ieee International Conference on computational intelligence for Measurement Systems and Applications

作者： Van der Weken, D Nachtegael, M De Witte, V Schulte, S Kerre, EE Univ Ghent Fuzziness & Uncertainty Modelling Res Unit B-9000 Ghent Belgium

ISBN: (纸本)0780390253

Fuzzy techniques can be applied in several domains of image processing. In this paper we will give a survey on how fuzzy similarity measures can be used in establishing measures for image comparison. Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures serve as a tool to evaluate and to compare different algorithms designed to solve particular problems, such as noise reduction, deblurring, compression, ... Consequently these measures serve as a basis on which one algorithm is preferred to another. Furthermore, it is well-known that classical quality measures, such as the RMSE (Root Mean Square Error) or the PSNR (Peak signal to Noise Ratio), do not always correspond to visual observations.

关键词： Fuzzy sets

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Automatic Mask Detecion using Convolutional Neural Networks and Variational Autoencoder 15

Automatic Mask Detecion using Convolutional Neural Networks ...

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ieee 15th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Silabela, Mxolisi Bogdandy, Bence Toth, Zsolt Eszterhazy Karoly Univ Fac Informat Eger Hungary

ISBN: (纸本)9781728195438

The importance of proper hygienical behaivour is essential in today's word especially during an ongoing pandemic. Wearing mask became mandatory in many countries during the COVID-19 Pandemic. Recognizing whether people are wearing masks is complicated image recognition task which could be facilitated and automated with machine learning techniques. Camera streams are widely available in indoor environments which can be used for object detection and image processing. Convolutional Neural Networks have been successfully applied in image classification and object recognition task in various application areas. There are already trained and openly available general purpose convolutional neural networks which can be used as an initial version for specific applications. A number of different image datasets are also available for research and industrial purposes. The InceptionV3 Neural Network architecture was used to tailored to determine whether a mask is being worn or not using transfer learning techniques, and convolutional neural networks. A variational autoencoder has also been trained to normalize the dataset with respect to skin colour, angle of the head and among other parameters. This paper describes the implementation of a mask recognition software using transfer learning, a convolutional neural network and a variational autoencoder.

关键词： Machine Learning Feedforward neural networks Variational Autoencoder image Recognition Deep learning

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