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20th symposium ON signal processing, imageS AND COMPUTER VISION (STSIVA)

作者： Rodriguez, C. A. Gutierrez, R. M. Jaramillo, A. E. Triana, N. Univ Antonio Narino Bogota Colombia

ISBN: (纸本)9781467394611

The Gas Electron Multiplier (GEM) is one of the most popular and powerful technologies for gaseous ionization detectors used in high energy physics, medical physics and other applications. One of the fundamental elements of a GEM are foils with micro perforations;the quality of such perforations is determinant for an optimal performance of the GEM. In this work we study and develop different computational methods (implemented in java programming language), in order to determine the quality of GEM-foils from high resolution images of the foils. This computational method will provide an automatic and high precision alternative to the present procedures which are very expensive, time taking and imprecise, limiting the development and application of this important technology of detectors. The present method can be extended to other high resolution image analysis useful for nanostructures and microstructures. A preliminary study of the method applied to lower resolution images is also presented.

关键词： electron multiplier detectors ionisation chambers quality assurance GEM foils computational methods gas electron multiplier gaseous ionization detectors high-energy physics high-resolution image analysis java programming language medical physics microstructures nanostructures Detectors Fabrication Geometry image resolution Microscopy Physics Standards Quality assurance ELECTRON MULTIPLIER DETECTORS image resolution Medicine computational methods Ionization chambers Jewelry GEM gene Quality management Java programming language Nanostructure electron multipliers Particle physics geometry Microscopy

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Automatic classification of defect page content in scanned document collections

Automatic classification of defect page content in scanned d...

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8th International symposium on image and signal processing and Analysis (ISPA)

作者： Huber-Moerk, Reinhold Schindler, Alexander AIT Austrian Inst Technol GmbH Intelligent Vis Syst Safety & Secur Dept Vienna Austria

ISBN: (纸本)9789531841948;9789531841870

We describe a method for defect detection and classification for collections of digital images of historical book documents. Undistorted text images from various books characterized by strong variation of language, font and layout properties are discriminated from typical errors in digitization processes such as occlusion by an operator's hand, visible book edge or image warping artifacts. A bag of local features approach is compared to a global characterization of location, size and orientation properties of detected keypoints. Machine learning is used to discriminate between those classes. Results for different features are compared for the task of discrimination between undistorted text and the major distortion class which is presence of the operator's hand, where features based on the bag of local features derived histograms achieved a cross-validation accuracy better than 99 percent on a representative data set. Taking into account up to three classes of distortions still resulted in cross-validation accuracies beyond 90 percent using bag of local features derived visual histograms for classifier input.

关键词： document image processing history image classification learning (artificial intelligence)

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A Novel Color image processing Scheme in HSI Color Space with Negative image processing

A Novel Color Image Processing Scheme in HSI Color Space wit...

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International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Nakajima, Naoki Taguchi, Akira Tokyo City Univ Dept Comp Sci Setagaya Ku Tokyo 1588557 Japan

ISBN: (纸本)9781479961207

While emphasizing the intensity or saturation component in order to obtain high-quality color images, keeping the hue component unchanged is important;thus, perceptual color models such as HSI and HSV were used. Hue-Saturation-Intensity (HSI) is a public color model, and many color applications are commonly based on this model. However, the transformation from the HSI model to RGB model after the processing intensity and saturation in the HSI model usually generates the gamut problem, because, the HSI gamut is larger than the RGB gamut. Nevertheless, the common area of RGB gamut and HSI gamut is large to the middle value of the intensity. In this paper, we propose a new color image processing scheme in HSI color model. In the proposed scheme, the positive image processing and the negative image processing are performed in parallel. Since we can realize the negative processing system which is equivalent to the normal (i.e., positive) processing system, the proposed scheme is same degree-of-freedom of the normal processing scheme. In the negative image processing, color images are treated as CMY color space. The common area of CMY gamut and HSI gamut is large from the middle value of the intensity. Therefore, if the intensity value until middle, we choose the positive image processing result as the final result of our scheme. On the other hand, if the intensity value larger than middle value the result of negative image processing result is chosen as the final result. Due to that, we can almost avoid the gamut problem by the proposed scheme.

关键词： RGB color model CMY color model HSI color model gamut negative image processing

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A Matching Pursuit Approach for image Classification with Spiking Neural Networks

A Matching Pursuit Approach for Image Classification with Sp...

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ieee symposium Series on computational intelligence (SSCI)

作者： Song, Shiming Yu, Qiang Wang, Longbiao Dang, Jianwu Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China Japan Adv Inst Sci & Technol Nomi Ishikawa Japan

ISBN: (纸本)9781728124858

The human can learn to recognize objects in a very short time, with few data samples and little computing resources, while achieving high accuracy, that state-of-the-art learning methods hardly approach. In order to emulate the way how the human brain operates in processing external environmental information, a more biologically plausible network, i.e. spiking neural network (SNN), is proposed. In the SNN model, information is encoded into a spatiotemporal spike pattern first, and then SNN learning rules are used to emulate how the human brain works. Therefore, a suitable and appropriate coding method is demanded in SNN. In this paper, we propose a new approach for image recognition tasks, namely MP-SNN, by combining SNN with matching pursuit (NIP). We use the MP encoding method to extract the feature spike pattern from input data, then the tempotron learning algorithm is used to emulate how the brain operates in learning new things and making decisions. The MNIST and its variants are used to evaluate the performance of MP-SNN. Compared with other methods, NIP can generate a more sparse and robust spatiotemporal spike pattern while achieving good learning results. To the best of our knowledge. this paper is the first work to combine NIP and SNN, and gives a more biologically plausible and effective approach in image classification.

关键词： neural encoding matching pursuit spiking neural network image classification

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Analysis to forecast the spread of phytophthora of potato leaf with contour

Analysis to forecast the spread of phytophthora of potato le...

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12th ieee International symposium on computational intelligence and Informatics, CINTI 2011

作者： Horvath, Zoltan University of Pecs Faculty of Sciences Department of IT Hungary

ISBN: (纸本)9781457700453

Nowadays more and more important that our food contain fewer chemical derivates. It is also important to protect the environment even more. During my research it was important to develop a system by which the spread of phytophthora can be predirected as one of the most cultivated and consumed crop the potatoes. During my research I examined both resistant and non-resistant varieties. This more information is gained that the system behaves well in case of infection and resistance. In my opinion is important for the further development of this system as a way to defend themselves more effectively and can be made more concentrated, so not only the environment but also the plant can be spared. © 2011 ieee.

关键词： Agriculture computational intelligence Gray-scale Histograms image color analysis Immune system Informatics agricultural products chemical derivates contour cultivated crop image processing phytophthora spread forecast potato leaf

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computational Imaging and Artificial intelligence: The Next Revolution of Mobile Vision

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PROCEEDINGS OF THE ieee 2023年第12期111卷 1607-1639页

作者： Suo, Jinli Zhang, Weihang Gong, Jin Yuan, Xin Brady, David J. Dai, Qionghai Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Inst Brain & Cognit Sci Beijing 100084 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou 310030 Zhejiang Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Zhejiang Peoples R China Univ Arizona Wyant Coll Opt Sci Tucson AZ 85721 USA

signal capture is at the forefront of perceiving and understanding the environment;thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterward" mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.

关键词： Artificial intelligence (AI) autonomous driving brain science cameras cloud computing computational imag-ing (CI) deep learning edge cloud edge computing machine learning machine vision mobile vision neural networks optics self-driving

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Visual sentiment analysis for brand monitoring enhancement 9

Visual sentiment analysis for brand monitoring enhancement

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9th International symposium on image and signal processing and Analysis (ISPA)

作者： Giannakopoulos, Theodoros Papakostas, Michalis Perantonis, Stavros Karkaletsis, Vangelis Natl Ctr Sci Res Demokritos Inst Informat & Telecommun Computat Intelligence Lab Athens Greece Univ Texas Arlington Heracleia Human Centered Comp Lab Arlington TX USA Natl Ctr Sci Res Demokritos Inst Informat & Telecommun Software & Knowledge Engn Lab Athens Greece

ISBN: (纸本)9781467380324

Brand monitoring and reputation management are vital tasks in all modern business intelligence frameworks. However, recent related technologies rely mostly on the textual aspect of online content, in order to extract the underlying sentiment with respect to particular brands. In this work, we demonstrate the sentiment analysis method in the context of a brand monitoring framework, breaking the text-only barrier in the field. Towards this end, a wide range of visual features is extracted, some of which focus on the underlying semiotics and aesthetics of the images. In addition, we employ textual information embedded in the images under study, by adopting text mining techniques that focus on extracting sentiment. We evaluate the classification task for the particular binary task (negative vs positive sentiment) and propose a fusion approach that combines the two different modalities. Finally, the evaluation procedure has been carried out in the context of two different use cases, namely: (a) a general image sentiment classifier for brand and advertising images and (b) a brand-specific classification procedure, according to which the brand of the input images is known a-priori. Results have proven that the visual-based sentiment classification of brand and advertising information can outperform the respective text-based classification. In addition, fusing the two modalities leads to significant performance boosting.

关键词： Sentiment analysis

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FEPA - An Integrated computational intelligence Model for Predicting Financial Time Series

FEPA - An Integrated Computational Intelligence Model for Pr...

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ieee/SICE International symposium on System Integration (SII)

作者： Pan, Heping Ma, Yu Zhang, Chengzhao Chongqing Inst Finance Intelligent Finance Res Ctr Chongqing Inst Finance Chongqing 400067 Peoples R China Univ Chinese Acad Sci Financial Big Data Res Inst Beijing Peoples R China Swingtum Predict Sydney NSW Australia Univ Int Business & Econ Sch Banking & Finance Beijing Peoples R China Chengdu Politech Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538622636

This paper presents an integrated computational intelligence model FEPA for predicting financial time series (FTS), integrating Empirical Mode Decomposition (EMD) for signal processing, Principal Component Analysis (PCA) for dimension reduction and feature extraction, and Artificial Neural Networks (ANN) for prediction modeling. The model uses a sliding window to capture the most recent time series data, applies EMD to transform the data into multilevel Intrinsic Mode Functions (IMF's), uses PCA to reduce the dimensionality of IMF's and to generate a set of information-rich features which are input into an ANN to generate the prediction. This work is original in four aspects: 1) a structural reformulation of EMD algorithm, 2) a sliding window for tackling the end effect of EMD, 3) investigation of multi-step prediction, 4) testing on two levels of time frame: D1 (daily) and M15 (15-minutely). The new model is tested on the historical data of two stock indices - Chinese HS300 and Australian AXJO, achieving a hit rate of 78% and 82% on HS300 D1 and M15, and 74% on AXJO D1 respectively.

关键词： Principal component analysis computational modeling Predictive models Time series analysis Artificial neural networks Iron computational intelligence

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Efficient Method for Counting and Tracking of Moving Objects through Region Segmentation

Efficient Method for Counting and Tracking of Moving Objects...

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7th ieee International Conference on computational intelligence and Computing Research (ICCIC)

作者： Boggavarapu, Ravi Kiran Pateriya, Pushpendra Kumar Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9781509006120

Object Counting and Tracking are an integral part of computer-aided surveillance intelligence. In this paper, we propose an Object Counting algorithm based on region segmentation and a tracking method that works on the Kalman Filter to predict the next state of a moving object. We considered people, moving under an overhead camera as our primary targets. As a pre-processing step, we extracted foreground objects using a background subtraction method and optimized those foreground objects using Morphological operations. The counting algorithm works by segmenting the image into regions and analyzing the individual regions to count the moving objects based on certain heuristics. Experiments show that our method can obtain counting accuracy about 89.47% with a decent performance.

关键词： Moving Object Counting Object Counting Object Tracking Region Segmentation Kalman Filter

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Dynamical property of periodic oscillations observed in a coupled neural Oscillator Network for image segmentation

Dynamical property of periodic oscillations observed in a co...

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1st International Conference on Bio-Inspired Systems and signal processing

作者： Yoshinaga, Tetsuya Fujimoto, Ken'ichi Univ Tokushima Fac Med Tokushima 7708509 Japan

ISBN: (纸本)9789898111180

We consider image segmentation using the LEGION (Locally-Excitatory Globally-Inhibitory Oscillator Network), and investigate dynamical properties of a modified LEGION, described by noise-free or deterministic continuous ordinary differential equations. We clarify a phenomenon of image segmentation corresponds to the appearance of a synchronized periodic solution, and the ability of segmentation depends on its symmetric properties. We study bifurcations of periodic solutions by using a computational method based on the qualitative dynamical system theory.

关键词： coupled oscillator image segmentation dynamical system bifurcation

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