Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark

Iqbal, Bilal; Iqbal, Waheed; Khan, Nazar; Mahmood, Arif; Erradi, Abdelkarim

doi:10.1007/s10586-019-02929-x

Cited by 45 publications

(18 citation statements)

References 19 publications

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“…An online system, which creates a summary for new text documents instantly, will not introduce any notable performance issue. However, for a batch processing system, in which a large dataset required to generate the summaries, we recommend using Hadoop and Spark [21] to parallel process the large datasets for reducing the overhead of creating the summaries.…”

Section: Experimental Summarymentioning

confidence: 99%

Big Data Full-Text Search Index Minimization Using Text Summarization

Iqbal¹,

Malik²,

Bukhari³

et al. 2021

ITC

Self Cite

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An efficient full-text search is achieved by indexing the raw data with an additional 20 to 30 percent storagecost. In the context of Big Data, this additional storage space is huge and introduces challenges to entertainfull-text search queries with good performance. It also incurs overhead to store, manage, and update the largesize index. In this paper, we propose and evaluate a method to minimize the index size to offer full-text searchover Big Data using an automatic extractive-based text summarization method. To evaluate the effectivenessof the proposed approach, we used two real-world datasets. We indexed actual and summarized datasets usingApache Lucene and studied average simple overlapping, Spearman’s rho correlation, and average rankingscore measures of search results obtained using different search queries. Our experimental evaluation showsthat automatic text summarization is an effective method to reduce the index size significantly. We obtained amaximum of 82% reduction in index size with 42% higher relevance of the search results using the proposedsolution to minimize the full-text index size.

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Section: Experimental Summarymentioning

confidence: 99%

Big Data Full-Text Search Index Minimization Using Text Summarization

Iqbal¹,

Malik²,

Bukhari³

et al. 2021

ITC

Self Cite

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“…To address the problem of line detection, multiple approaches are proposed: 1) For problems such as detecting the edges of objects (e.g. documents) in images, Hough Transformation can be very efficient [12]. 2) In more complex cases, such as the detection of power lines in images, various neural networks are used to determine the areas containing lines in the image [13].…”

Section: State-of-the-artmentioning

confidence: 99%

Improved and Generalized Vine Line Detection on Aerial Images Using Asymmetrical Neural Networks and ML Subclassifiers

Treboux¹,

Ingold²,

Genoud³

2021

ESANN 2021 Proceedings

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It is widely accepted that deep neural networks are very efficient for detecting objects in images. They reach their limit when detecting multiple instances of long lines in low-resolution images. We present an original methodology for the recognition of vine lines in low-resolution satellite images. The method consists in combining an asymmetrical neural network with a sub-classifier. We first compare a traditional U-Net architecture with an asymmetrical U-Net architecture designed for precision agriculture. We then highlight the significant improvement in vine line detection when a Random Forest is added after the customized U-Net. This methodology addresses the complex task of dissociating vine lines from other agricultural objects. As a result, our experiments improve the precision from 0.83 to 0.94 over our optimized neural network.

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“…In [21] cloud services for high resolution video streams in order to perform line detection using Canny edge detection followed by Hough transform are proposed and evaluated. Both Canny edge detector and Hough transform algorithms in Hadoop and Spark are proposed.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Ant Colony Optimization in Edge Detection

Chen

2021

Preprint

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With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greaterproblems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvementsin speedup, scaleup and sizeup compared to the standard algorithms.

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Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark

Cited by 45 publications

References 19 publications

Big Data Full-Text Search Index Minimization Using Text Summarization

Big Data Full-Text Search Index Minimization Using Text Summarization

Improved and Generalized Vine Line Detection on Aerial Images Using Asymmetrical Neural Networks and ML Subclassifiers

A Distributed Ant Colony Optimization in Edge Detection

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