Study of Information Extraction Algorithm of Poisson Noise Images Based on Fractional Order Differentiation

Hou, Mengshu; Liu, Yuran

doi:10.1109/iccis.2013.206

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Edge detection and texture IE together have been used to extract smooth regions with poison noises in image. 79 Shape features. The purpose of shape feature extraction is to identify objects using two main methods, that is, contour-based and region-based methods.…”

Section: Image-based Ie-from Visual To Semantic Extractionmentioning

confidence: 99%

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Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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Section: Image-based Ie-from Visual To Semantic Extractionmentioning

confidence: 99%

“…Edge detection and texture IE together have been used to extract smooth regions with poison noises in image. 79…”

Section: Ie For Unstructured Data Analysismentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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“…Image was one kind of signal, its signal processing with fractional differential would be favorable to enhance its high frequency edge, and also reserve its low frequency texture information. [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Improved Fractional Differential Algorithm and Noise Immunity Analysis

Liu

Hou

2011

AMM

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Texture and edge information are equally important to identification of most of natural images. To the problem of the existing integer order differential operators can’t extract texture information form the images, this study developed a fractional differential algorithm, which can extract texture and marginal information simultaneously, based on settlement of the drift problem of fractional differential operator. Experimental results showed that our algorithm can not only extract texture information but also extract more edge information than the traditional algorithms. And to the image with Gauss noise, our algorithm also have noise immunity.

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Study of Information Extraction Algorithm of Poisson Noise Images Based on Fractional Order Differentiation

Cited by 2 publications

References 12 publications

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

An Improved Fractional Differential Algorithm and Noise Immunity Analysis

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