Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness

Srivastava, Gargi; Srivastava, Rajeev

doi:10.1016/j.jvcir.2019.06.005

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…In this section, the above three parameters were used to evaluate the performance of the proposed approach for image extraction. To verify the performance of the optimal threshold, computational time, and unclassified rate evaluation for the characteristic parameters of a coal dust image, several approaches that are widely used to process particulate images were compared by using images of different particle sizes: multiscale image acquisition (MSIA) [26], Daubechies wavelet transform (DWT) [27], Frenkel-Halsey-Hill (FHH) [28], grey-level cooccurrence matrix (GLCM) [29], fuzzy C-means (FCM) [9], Gabor filter [30], FPM, and SFC. In the process of testing the simulations with the approaches in the above works, the adopted test conditions, such as temperature, humidity, light intensity, coal dust sample specifications and other parameters, are not the same, so the measured indexes are different.…”

Section: B Discussion 1) Parameters Analysismentioning

confidence: 99%

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Wang

Zheng²,

Pan

et al. 2020

IEEE Access

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High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes ≥ 200 µm, 0.958 for [75 µm, 200 µm] and 0.950 for particle sizes < 75 µm. The proposed reduction approach offered a performance improvement in terms of more important attribute implementation. The paper demonstrated that the maximum information entropy reduction model can remove the redundant attributes without compromising the precision.

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Section: B Discussion 1) Parameters Analysismentioning

confidence: 99%

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Wang

Zheng²,

Pan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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“…2 Related work Srivastava and Srivastava [1] employed background removal, Gabor filters, objectness, and minimum directional backgroundness to tackle the problem of significant object recognition. Researchers haven't looked at deep learning techniques.…”

Section: Research Contributionsmentioning

confidence: 99%

“…The goal of automating this process is to produce machines that can accurately simulate human. To recognize prominent objects, background subtraction and the Gabor filter are used [1]. The most basic implementation of background subtraction is to pixel-wise evaluate the difference between a previously taken or estimated background image and the current image, and then threshold the difference value to find the pixels that belong to moving objects.…”

Section: Introductionmentioning

confidence: 99%

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

Shaikh

Chopade

Kharate

2023

Period. Polytech. Elec. Eng. Comp. Sci.

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One of the most difficult tasks in the area of computer vision is object detection, which combines object categorization and object location within a scene. In terms of object detection, Deep Neural Networks have been recently demonstrated to outperform alternative approaches. The issues related deep learning neural network is its complexity and huge computation, so it is not possible to detect and track the objects in image of high resolution in real time. We proposed scaled YOLOv4 lite model as Single Stage Detector Neural Network for object detection, tracking and it is trained using COCO 2017 dataset. To create the YOLOv4-CSP- P5- P6- P7- P8 networks, the Scaled YOLOv4 applied efficient network scaling strategies. The additional layer in YOLOv4 lite model is added as P8 layer which improves accuracy. Cross-stage-partial (CSP) connections and Mish activation are used in improved network design, such as backbone optimization and Neck (PAN). In the case of YOLOv4, however, it can only be trained once for all resolutions. Width and Height activations have been changed, allowing for faster network training. With YOLOv4 lite model, we used CSPDarkNet-53 model as a backbone. The experimental result show our YOLOv4 lite model can detect and track object up to 28 fps when model run with the video input and has an accuracy of 86.09% when tested on real-time video with resolutions 1920 × 1080 (full HD). AP = 50.81%, AP @50 = 63.6%, and AP @75 = 52.5% for CSPDarkNet-53 model backbone.

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“…They have been demonstrated by Premana et al [14] and Fan et al [52] to be effective in object segmentation using K-means clustering. Srivastava and Srivastava [53] proposed a novel method for salient object detection using Gabor filters, foreground saliency maps, and objectness criterion. This method outperformed state-of-the-art algorithms as evaluated by the PR curve, F-measure curve, and mean absolute error on eight public datasets.…”

Section: Gabor Filtersmentioning

confidence: 99%

Enhancing CNNs Performance on Object Recognition Tasks with Gabor Initialization

Rivas,

Rai

2023

Electronics

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The use of Gabor filters in image processing has been well-established, and these filters are recognized for their exceptional feature extraction capabilities. These filters are usually applied through convolution. While convolutional neural networks (CNNs) are designed to learn optimal filters, little research exists regarding any advantages of initializing CNNs with Gabor filters. In this study, the performance of CNNs initialized with Gabor filters is compared to traditional CNNs with random initialization on six object recognition datasets. The results indicated that the Gabor-initialized CNNs outperformed the traditional CNNs in terms of accuracy, area under the curve, minimum loss, and convergence speed. A statistical analysis was performed to validate the performance of the classifiers, and the results showed that the Gabor classifiers outperformed the baseline classifiers. The findings of this study provide robust evidence in favor of using Gabor-based methods for initializing the receptive fields of CNN architectures.

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Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness

Cited by 12 publications

References 18 publications

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

Enhancing CNNs Performance on Object Recognition Tasks with Gabor Initialization

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