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A Ridge Extraction Algorithm Based on Partial Differential Equations of the Wavelet Transform

A Ridge Extraction Algorithm Based on Partial Differential E...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Pan, Jiasong Yue, Lin Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China

ISBN: (纸本)9781479945030

For the disadvantages of traditional ridge extraction algorithms based on the modulus maxima and phase information of wavelet coefficients, this paper proposes a new ridge extraction algorithm based on partial differential equations of the wavelet transform. According to the relationship between wavelet ridge and the modulus maxima of wavelet coefficients, the initial position of ridge is determined, and iterative formula of wavelet parameters is derived for determining other ridge points. After calculating all the wavelet coefficients at the initial time, the starting position of the ridge is chosen by searching the maximum value of these wavelet coefficients, then a complete ridge will be fitted through several ridge points obtained by successive iteration using the derived iterative formula. The biggest advantage of this algorithm is that it does not need to calculate the entire wavelet transform time-frequency plane, so redundant computation is avoided. Experimental results show that the computing speed has been significantly improved compared with Carmona's Crazy-Climber algorithm. The extracted wavelet ridges can accurately restore effective frequency components of signals. signals can be reconstructed by their ridges and the noise signals are removed. This algorithm is especially suitable for wavelet ridge extraction of multi frequency component asymptotic signals.

关键词： Wavelet Transform Wavelet Ridge Extraction Partial Differential Equation signal Reconstruction

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Endoscope Image Analysis Method for Evaluating the Extent of Early Gastric Cancer

Endoscope Image Analysis Method for Evaluating the Extent of...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Hiroyasu, Tomoyuki Hayashinuma, Katsutoshi Ichikawa, Hiroshi Yagi, Nobuaki Yamamoto, Utako Doshisha Univ Fac Life & Med Sci Kyoto Japan Doshisha Univ Grad Sch Life & Med Sci Kyoto Japan Asahi Univ Murakami Mem Hosp Dept Gastroenterol Gifu Japan

ISBN: (纸本)9781479945030

In this study, a system is proposed to help physicians perform processing on images taken with a magnifying endoscopy with narrow band imaging. In our proposed system, the transition from lesion to normal zone is quantitatively analyzed and presented by texture analysis. Eleven feature values are calculated, i.e., six from a co-occurrence matrix and five from a run length matrix with a scanning window. Integrating these feature values formulates an effective and representative feature value, which is used to draw a color map, so the transition from lesion to normal zone can be visibly illustrated. In this paper, the proposed method is applied to images, and the efficacy is considered. This method is also applied to some rotated images to examine whether it could work effectively on such images.

关键词： cancer endoscopes feature extraction image colour analysis image texture matrix algebra medical image processing color map cooccurrence matrix early gastric cancer endoscopic image analysis image processing representative feature value run length matrix texture analysis Cancer Educational institutions Endoscopes Feature extraction Image color analysis Lesions

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A Comparison of Genetic Programming Feature Extraction Languages for Image Classification

A Comparison of Genetic Programming Feature Extraction Langu...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Maghoumi, Mehran Ross, Brian J. Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada

ISBN: (纸本)9781479945030

Visual pattern recognition and classification is a challenging computer vision problem. Genetic programming has been applied towards automatic visual pattern recognition. One of the main factors in evolving effective classifiers is the suitability of the GP language for defining expressions for feature extraction and classification. This research presents a comparative study of a variety of GP languages suitable for classification. Four different languages are examined, which use different selections of image processing operators. One of the languages does block classification, which means that an image is classified as a whole by examining many blocks of pixels within it. The other languages are pixel classifiers, which determine classification for a single pixel. Pixel classifiers are more common in the GP-vision literature. We tested the languages on different instances of Brodatz textures, as well as aerial and camera images. Our results show that the most effective languages are pixel-based ones with spatial operators. However, as is to be expected, the nature of the image will determine the effectiveness of the language used.

关键词： computer vision feature extraction genetic algorithms image classification image texture Brodatz textures GP language GP-vision literature aerial images camera images classifiers computer vision problem genetic programming feature extraction languages image processing operators pixel classifiers spatial operators visual pattern recognition Boats Feature extraction Genetic programming Pattern recognition Standards Testing Training Image classification Feature extraction Genetic Algorithm (GA) images Visual pattern recognition computer vision space operation Language Classifiers aerial images image texture Boats Pattern recognition Functional training

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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 ...

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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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Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities 25

Poster: Cloud Computing with AI-empowered Trends in Software...

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25th ieee International symposium on a World of Wireless, Mobile and multimedia Networks (ieee WoWMoM)

作者： Sharma, Ekta Deo, Ravinesh C. Davey, Christopher P. Carter, Brad D. Salcedo-Sanz, Sancho Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Astrophys Toowoomba Qld 4350 Australia Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28805 Spain

ISBN: (纸本)9798350394665;9798350394672

Artificial intelligence (AI) and Software Defined Radio (SDR) are transforming the field of signal intelligence. However, the full extent of the capabilities is unknown. This poster presents a paper in development that introduces a cloud-based platform leveraging artificial intelligence to detect and apply 11 modulation schemes (8 digital and 3 analog) to complex or quadrature radio signals. The SNR values analysed range from 0.0 to 40.0, with moderate drift, slight fading, and labelled increments. A comprehensive synthetic database developed by DeepSig is used to train four AI models. These will be integrated with the Google Cloud AI platform to enhance flexibility and processing power. The system will undergo testing with an SDR platform in GNU Radio, showcasing its potential for real-world signal processing applications. Cloud-based platforms offer the adaptability and computational power needed to replace traditional computers for AI-driven signal processing. Initial results indicate successful identification and accurate modulation type detection, with convenient access to the system through internet-connected devices.

关键词： cloud computing artificial intelligence software-defined radio modulation GNU Radio signal classification

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A multi-window stereo vision algorithm with improved performance at object borders

A multi-window stereo vision algorithm with improved perform...

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ieee symposium on computational intelligence in Image and signal processing

作者： Zhao, Jun Katupitiya, Jayantha Univ New S Wales Sch Mech & Mfg Engn ARC Ctr Excellence Autonomous Syst Sydney NSW 2052 Australia

ISBN: (纸本)9781424407071

Conventional correlation based stereo vision algorithms have poor performance at object borders due to occlusion. In this paper, a new matching sequence has been introduced. To detect left object border, a left shifted window is used with right image as the reference image and process from left to right. This method gives excellent left object border detection while detection of right object border is poor. The right object borders can be detected with equal success using a right shifted window with left image as reference and process from right to left. The combination result is good detection at both object borders.

关键词： occlusion SMP correlation stereo vision

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computational intelligence for automated keg identification and deformation detection

Computational intelligence for automated keg identification ...

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ieee symposium on computational intelligence in Image and signal processing

作者： Campbell, Duncan Keir, Andrew Lees, Michael Queensland Univ Technol Sch Engn Syst Brisbane Qld Australia Yatala Operat Fosters Yatala Australia

ISBN: (纸本)9781424407071

A machine vision based keg inspection system can allow cost effective keg tracking and preventative maintenance programs to be implemented, leading to substantial savings for breweries with large keg fleets. A robust keg serial number recognition and keg condition assessment process is required to cater for different keg brands and a range of keg ages in the fleet It has been demonstrated that the proposed image processing methodology, and neural network based number recognition system, successfully located and identified keg serial numbers with a 92% digit accuracy. Furthermore, the vision system allowed the concurrent assessment of the keg condition by assessing deformity of the keg rim, and that of the filler valve. A correlation coefficient, generated using a template matching process, proved to be a suitable metric which adequately indicated rims within and outside acceptable deformity bounds.

关键词： neural networks OCR keg deformation detection keg tracking

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Analysis of Gray Scale Watermark in RGB Host using SVD and PSO

Analysis of Gray Scale Watermark in RGB Host using SVD and P...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Ansari, Irshad Ahmad Pant, Millie Neri, Ferrante Indian Inst Technol Roorkee Dept Appl Sci & Engn Roorkee Uttar Pradesh India De Montfort Univ Ctr Computat Intelligence Leicester Leics England

ISBN: (纸本)9781479945030

the present study is conducted in two phases. In the first phase we analyze the different aspects of gray image watermarking in a colored host. Robustness and imperceptibility are used as analysis parameters. The approaches explored and compared in this study are - watermark embedding with any one of the three RGB (Red-Green-Blue) components (single channel embedding), multichannel watermark embedding (same watermark with all channels) and multichannel embedding with equally segmented watermark. SVD (Singular Value Decomposition) is used to calculate the singular values of host image and then appropriate scaling factor isused to embed the watermark and the watermarked image is subjected to different attacks. To secure the watermark from an unauthorized access Arnold transform is implemented. From the simulation results it is observed that segmented watermark approach is better than the other two approaches in terms of both robustness and imperceptibility. In the second phase, change of robustness and imperceptibility is studied with the change of scaling factor for which PSO (Particle swarm optimization) is employed to determine the optimal values of scaling factor. The results here indicate that the use of different scaling factors (optimal) for each RGB component provides better result in comparison to a single (optimal) scaling factor in segmented multichannel approach. Overall, the experimental analysis shows that the equal distribution of gray watermark over RGB components with PSO optimized scaling factors provides significant improvement in the quality of watermarked image and the quality of retrieved watermark even from the distorted watermarked image.

关键词： Singular value decomposition Watermarking in color host Cross-Correlation Multi channel embedding Gray watermark Arnold Transform

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Improved H.264/AVC requantization transcoding using low-complexity interpolation filters for 1/4-pixel motion compensation

Improved H.264/AVC requantization transcoding using low-comp...

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ieee symposium on computational intelligence in Image and signal processing

作者： Notebaert, Stijn De Cock, Jan Van de Walle, Rik Univ Ghent IBBT Dept Elect & Informat Syst Multimedia Lab B-9050 Ledeberg Ghent Belgium

ISBN: (纸本)9781424407071

Transcoding is a fast technique for video adaptation, which offers great flexibility for changing the characteristics of already coded video sequences in order to match the properties of the terminal display devices and the bandwidth availability of the transmission networks. For reduction of the bitrate, requantization transcoding is often used. In our previous work, we introduced a hybrid architecture for requantization transcoding, which is able to reduce the bitrate for H.264/AVC bitstreams with quality results approaching the rate-distortion optimal decoder-encoder cascade. In this paper, we focus on the filters used for interpolation of the accumulated requantization errors, which are used for compensation of the current (sub-)macroblock partition. We show that by using reduced complexity filters, computational complexity can be significantly reduced without quality loss.

关键词： Image coding

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Identification of Visual Attention Regions in Machine vision Using Saliency Map

Identification of Visual Attention Regions in Machine Vision...

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2015 International Conference on Communications and signal processing (ICCSP)

作者： Kounte, Manjunath R. Sujatha, B. K.

ISBN: (纸本)9781479980819

In Recent years, the detection of visual attention regions ( VAR) is becoming more noteworthy due to its valuable applications in the area of multimedia. In this paper, we provide the Saliency Map hypothesis and test results for identification of Visual Attention Regions in Machine vision using computational Cognitive Neuroscience. We also review how computational Cognitive Neuroscience approach is the best approach in order for the implementation of Saliency Map technique for identification of Visual Attention Regions ( VAR) in Machine vision in real time scenarios where the systems are evolved for exhibiting intelligence in real time scenarios. And also, we focus on learning how visual attention is beneficial to the machine vision community and explain why attention is considered a selective process by highlighting how in the last 25 years research on attention has characterized into multiple ways.

关键词： computational Cognitive Neuroscience Saliency Map Visual Attention Visual Attention Region

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