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DIVAServices-A RESTful web service for Document image Analysis methods

DIGITAL SCHOLARSHIP IN THE HUMANITIES 2017年第suppl_1期32卷 150-156页

作者： Wursch, Marcel Ingold, Rolf Liwicki, Marcus Univ Fribourg Dept Informat DIVA Grp Blvd Perolles 90 CH-1700 Fribourg Switzerland

In this article we present a web service framework providing automatic document processing methods to the public. Furthermore, an assessment environment and sample applications using this framework are briefly described. Research on Document image Analysis (DIA) focuses mainly on developing and refining automatic processing steps, e.g. text line extraction, binarization, and layout analysis. While many state-of-the-art methods perform satisfactorily, the algorithms applied to obtain the results are not easily accessible for other researchers. Making the source code available is often not sufficient as it typically requires a cumbersome installation of required libraries and reading long manuals about the usage. We present a new approach for making methods available to researchers in the digital humanities without detailed knowledge of the algorithms. For our approach we propose a RESTful web service architecture, the current state of the art in online web communication. For a developer this reduces the steps needed to access a method to sending and receiving HTTP requests with Java Script Object Notification data, removing all installation steps. We will build on standards such as the Text Encoding Initiative and the International image Interoperability Framework. Thus, methods hosted on DIVAServices can be integrated easily into document processing workflows by any software engineer in computer science, but also the digital humanities without specific knowledge of the mathematical and algorithmic details of DIA.

关键词： DOCUMENT imaging systems image analysis WEB services algorithms DIGITAL humanities TEXT Encoding Initiative (Document type definition)

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Content Based image Retrieval for Leaf Identification using Structural Features and Neural Networks 4

Content Based Image Retrieval for Leaf Identification using ...

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4th International conference on Signal processing and Integrated Networks (SPIN)

作者： Keni, Nishant Deepak Ansari, Rizwan Ahmed Veermata Jijabai Technol Inst Dept Elect Engn Bombay Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Bombay Maharashtra India

ISBN: (纸本)9781509027972

With the advances in digital imaging techniques, the number of digital images has risen in the past decade. With the increasing number of image databases consisting of a large number of images, it is challenging to extract images specific to our requirement. Therefore, it becomes necessary to design efficient search systems to query large image databases. Content-based image Retrieval (CBIR) algorithms analyze the contents of an image rather than the metadata associated with it. This allows image queries to be processed in a way that extracts images from the database, having a close similarity with the query image. Conventional CBIR systems using cosine similarity have a problem of associating images having maximum content overlap, thus similar orientation and size. Therefore, images relevant to the query but having different sizes and orientation are given lower priority in their results. In this article, we propose an approach which solves this problem and attempts to return the results relevant to the query, irrespective of the contents' orientation and size, using appropriate feature extraction techniques. 32 different types of leaves form a preliminary database of 1427 images. The system performance is assessed from the query results of 16 new images belonging to each of the 32 leaf classes. Features such as leaf shape, structure, color, and texture are extracted for pattern matching. Neural network using radial basis function is used for leaf classification to aid query result retrieval. The base accuracy of the CBIR system is 96.88%. Using a simple human relevance feedback mechanism, the neural network trains itself to capture trends in existing leaf classes. As a result, the accuracy of the CBIR system increases as the database is queried. Features extracted from correctly classified leaves are added to the training set. Thus, patterns in leaves belonging to similar and dissimilar classes are captured by the neural network. This increases the robustness of t

关键词： content-based image retrieval radial basis function neural networks rotational invariance structural decomposition eigen transformation morphological processing

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Searching for efficient multi-scale architectures for dense image prediction 18

Searching for efficient multi-scale architectures for dense ...

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Proceedings of the 32nd International conference on Neural Information processing systems

作者： Liang-Chieh Chen Maxwell D. Collins Yukun Zhu George Papandreou Barret Zoph Florian Schroff Hartwig Adam Jonathon Shlens Google Inc.

The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems.

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Accuracy Evaluation of Soft Classifiers using Interval Type-2 Fuzzy Sets Framework

Accuracy Evaluation of Soft Classifiers using Interval Type-...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Binaghi, Elisabetta Vergani, Alberto A. Pedoia, Valentina Univ Insubria Dept Theoret & Appl Sci Varese Italy UCSF Sch Med Dept Radiol San Francisco CA USA

ISBN: (纸本)9781509060344

This paper proposes a new accuracy evaluation method within a behavioral comparison strategy which uses interval type-2 fuzzy sets and derived operations to model reference data and define soft accuracy indexes. The method addresses the case in which grades of membership, collected by surveying experts, will often be different for the same reference pattern, because the experts will not necessarily be in agreement. The approach is illustrated using simple examples and an application in the domain of biomedical image segmentation.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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A Tensor-based Technique for Structure-aware image Inpainting 6

A Tensor-based Technique for Structure-aware Image Inpaintin...

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6th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Akl, Adib Yaacoub, Charles Holy Spirit Univ Kaslik USEK Fac Engn Jounieh Lebanon

ISBN: (纸本)9789897582226

image inpainting is an active area of study in computer graphics, computer vision and image processing. Different image inpainting algorithms have been recently proposed. Most of them have shown their efficiency with different image types. However, failure cases still exist, especially when dealing with local image variations. This paper presents an image inpainting approach based on structure layer modeling, where this latter is represented by the second-moment matrix, also known as the structure tensor. The structure layer of the image is first inpainted using the non-parametric synthesis algorithm of Wei and Levoy, then the inpainted field of second-moment matrices is used to constrain the inpainting of the image itself. Results show that using the structural information, relevant local patterns can be better inpainted comparing to the standard intensity-based approach.

关键词： image Inpainting Orientation Second-moment Matrix Exemplar

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Generalizing an Interval-Valued image Magnification Algorithm Using Homogeneity Measures and Interval Fusion Functions 17

Generalizing an Interval-Valued Image Magnification Algorith...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Paternain, Daniel Jurio, Aranzazu Pagola, Miguel Barrenechea, Edurne Bustince, Humberto Univ Publ Navarra Dept Automat & Comp Pamplona 31006 Spain Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain

ISBN: (纸本)9781509049172

In this work we study and generalize an image magnification algorithm based on the use of interval-valued fuzzy sets. The first proposed generalization incorporates an homogeneity measure that allows to model the length of the intervals generated by the algorithm. The second one makes use of several homogeneity measures and, by means of a fusion function, it combines the intervals generated by each individual homogeneity measure. The results show that our generalization outperforms the original algorithm when an appropriate homogeneity measure is used. Moreover, experiments have demonstrated that the second generalization, based on interval fusion functions, avoids low quality results due to bad homogeneity measures.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Principal Component Analysis Approach in Selecting Type-1 and Type-2 Fuzzy Membership Functions for High-dimensional Data 17

Principal Component Analysis Approach in Selecting Type-1 an...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Raj, Desh Gupta, Aditya Tanna, Kenil Garg, Bhuvnesh Rhee, Frank Chung-Hoon Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 India Indian Inst Technol Guwahati Dept Math Gauhati 781039 India Hanyang Univ Dept Elect & Commun Engn Ansan 15588 South Korea

ISBN: (纸本)9781509049172

With increased interest in learning from data, algorithms that manipulate datasets containing hundreds of features have become popular in various fields such as medicine, image processing, geolocation, biochemistry, and computational linguistics. Since a number of these applications exploit the power of fuzzy sets in representing uncertainties, it may be considered essential to describe a method for selecting the most suitable fuzzy membership function to represent a high-dimensional dataset. In this paper, we propose such a method, which is based on dimensionality reduction using the Principal Component Analysis (PCA) technique, followed by the Wilcoxon Minimal Bin Size algorithm, which has earlier been evaluated on multidimensional datasets up to 8 dimensions. We further demonstrate our proposed method using two real datasets consisting of 281 and 500 features, respectively.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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ACE: An Efficient Asynchronous Corner Tracker for Event Cameras

ACE: An Efficient Asynchronous Corner Tracker for Event Came...

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International conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Ignacio Alzugaray Margarita Chli Vision for Robotics Lab ETH Zürich

The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional frame-based vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras. In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.

关键词： Cameras Tracking image edge detection Simultaneous localization and mapping Feature extraction Robot vision systems Visualization

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Everything is image: CNN-based Short-term Electrical Load Forecasting for Smart Grid 14

Everything is Image: CNN-based Short-term Electrical Load Fo...

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14th International Symposium on Pervasive systems, algorithms and Networks (I-SPAN) / 11th International conference on Frontier of Computer Science and Technology (FCST) / 3rd International Symposium of Creative Computing (ISCC)

作者： Li, Liangzhi Ota, Kaoru Dong, Mianxiong Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido Japan

ISBN: (纸本)9781538608401

Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting precision, we successfully transform the numerical prediction problem into an image processing task, and, based on that, utilize the state-of-the-art deep learning methods, which have been widely used in computer image area, to perform the electrical load forecasting. A novel deep learning based short-term forecasting (DLSF) method is proposed in the paper. Our method can perform accurate clustering on the input data using a deep Convolutional Neural Network (CNN) model. And ultimately, another neural network with three hidden-layers is used to predict the electric load, considering various external influencing factors, e.g. temperature, humidity, wind speed, etc. Experimental results demonstrate that the proposed DLSF method performs well in both accuracy and efficiency.

关键词： Load forecasting Neural networks Forecasting Support vector machines Machine learning Load modeling Training

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A Survey on Performance of Various image Compression Techniques

A Survey on Performance of Various Image Compression Techniq...

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International conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： H R Gireesh L Rangaiah VTU Belagavi India RRCE Bangalore India

ISBN: (数字)9781538651308

ISBN: (纸本)9781538651315

image compression enables quite exciting solutions in many fields, such as image analysis, bio-medical image processing, wireless systems and seems to be a key application in today's digital and smart world. image compression seems to be a powerful tool in case of transmission and storage of large data images in various applications such as big data, medical etc. However due to exclusive and quality soft tissue contrast, Dynamic Magnetic Resonance Imaging (MRI) has been a field of attraction with increasing attention in recent decades. Moreover, MRI is considered as one of the most effective and strongest diagnosis system making the extensive usage of magnetic and radio waves in order to diagnose the human organs. This diagnosis is capable of generating 3D images with detailed anatomical features without any X-ray radiations. The prime purpose of this survey is to provide a comprehensive report of different image compression schemes in order to design an efficient compression scheme for dynamic MRI images. In this paper, author has surveyed different image compression schemes which are either sole implementations or hybrid of two or more algorithms. The author has also presented a comparative analysis for the surveyed compression schemes. This survey paper finally makes inroads for further researches in the domain of image compression schemes for dynamic MRI images.

关键词： image coding Magnetic resonance imaging Discrete cosine transforms Biomedical imaging Entropy Discrete wavelet transforms

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