Stand-alone quality estimation of background subtraction algorithms

Ortego, Diego; SanMiguel, Juan C.; Martínez, José María Álvarez

doi:10.1016/j.cviu.2017.08.005

Cited by 4 publications

(6 citation statements)

References 49 publications

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“…The remaining images of blurred and speckled defective samples are either ''image displacement'' or ''qualified.'' In this system, ''Sobel edge detection'' 5 and ''image subtraction method'' 6 are used to calculate the difference threshold between the detected image and template image. If the difference threshold is greater than the predetermined threshold, it is determined as ''image displacement,'' and if the difference threshold is smaller, it is determined as ''qualified product.''…”

Section: The Principle Of Defect Recognition Based On Svmmentioning

confidence: 99%

An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019

Measurement and Control

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Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

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Section: The Principle Of Defect Recognition Based On Svmmentioning

confidence: 99%

An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019

Measurement and Control

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“…As a conclusion, Model-independent techniques stand out as very interesting alternatives due to their independence of BS algorithms. The fitness-to-regions property has demonstrated a great potential to both estimate foreground quality [51] and improve results [44]. However, the use of over-segmented images (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…2) Hierarchical foreground quality estimation: Based on the potential of image regions to estimate blob-level foreground quality [51], we employ the property of fitness-toregions to extend detected foreground blobs over foreground objects while removing erroneous foreground pixels. For each hierarchy level l, we compute a foreground quality q l i for each region R l i as:…”

Section: B Descriptionmentioning

confidence: 99%

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Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction

Ortego

SanMiguel

Martinez

2019

IEEE Trans. Circuits Syst. Video Technol.

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A plethora of algorithms have been defined for foreground segmentation, a fundamental stage for many computer vision applications. In this work, we propose a post-processing framework to improve foreground segmentation performance of background subtraction algorithms. We define a hierarchical framework for extending segmented foreground pixels to undetected foreground object areas and for removing erroneously segmented foreground. Firstly, we create a motion-aware hierarchical image segmentation of each frame that prevents merging foreground and background image regions. Then, we estimate the quality of the foreground mask through the fitness of the binary regions in the mask and the hierarchy of segmented regions. Finally, the improved foreground mask is obtained as an optimal labeling by jointly exploiting foreground quality and spatial color relations in a pixel-wise fully-connected Conditional Random Field. Experiments are conducted over four large and heterogeneous datasets with varied challenges (CDNET2014, LASIESTA, SABS and BMC) demonstrating the capability of the proposed framework to improve background subtraction results.

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“…In another work, Bouwmans and Garcia-Garcia [173] reviewed real-time applications, current models and challenges of background subtraction. Many researchers evaluated the state-of-the-art methods on different video datasets for comparative analysis of background subtraction algorithms [21]- [24], [25], [27].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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Stand-alone quality estimation of background subtraction algorithms

Cited by 4 publications

References 49 publications

An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction

A Comprehensive Survey of Video Datasets for Background Subtraction

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