A multimedia image edge extraction algorithm based on flexible representation of quantum

Lu, Zexin; Wang, Xiaoming; Shang, Jianzhong; Luo, Zirong; Sun, Chongfei; Wu, Guoheng

doi:10.1007/s11042-019-7173-8

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…For the image edge extraction different types of pixels are judged according to Sobel gradient. It is proved through experimental result that proposed algorithm has quicker edge extraction speed than current edge extraction algorithm [9].…”

Section: Introductionmentioning

confidence: 90%

Intelligent computational techniques for multimodal data

Kumar

Mahanti

Wang

2019

Multimed Tools Appl

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Section: Introductionmentioning

confidence: 90%

Intelligent computational techniques for multimodal data

Kumar

Mahanti

Wang

2019

Multimed Tools Appl

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“…As shown in figure 2, through quantitative and qualitative comparison with the corresponding contour detection method, it is found that this method makes full use of the multi-source feature signal fusion coding ability under multi-scale, so that the main contour of the detected image is complete and continuous, and the irrelevant texture around the contour is effectively suppressed , which is consistent with the corresponding manual marking diagram. This method selects RCF [11] , COB [12] , HED [13] , HFL [14] , DeepContour [15] , DeepEdge [16] , OEF [17] and other deep learning methods in contour detection applications, And MCG, EGB, Canny, MShift and other traditional methods based on biological vision mechanism [18,19] to compare the experimental results. As shown in Figure 3 and Table 1, the evaluation indicators of the detection performance of each method are displayed.…”

Section: Experiments and Analysismentioning

confidence: 99%

Saliency Contour Extraction Based on Multi-level Feature Channel Optimization Coding

Fang¹,

Yi²

2021

Electron Sci Technol Appl

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<p>A biomimetic vision computing model based on multi-level feature channel optimization coding is proposed and applied to image contour detection, combining the end-to-end detection method of full convolutional neural network and the traditional contour detection method based on biological vision mechanism. Considering the effectiveness of the Gabor filter in perceiving the scale and direction of the image target, the Gabor filter is introduced to simulate the multi-level feature response on the visual path. The optimal scale and direction of the Gabor filter are obtained based on the similarity index, and they are used as the frequency separation parameter of the NSCT transform. The contour sub-image obtained by the NSCT transform is combined with the original image for feature enhancement and fusion to realize the primary contour response. The low-dimensional and low-redundancy primary contour response is used as the input sample of the network model to relieve network pressure and reduce computational complexity. A fully improved convolutional neural network model is constructed for multi-scale training, through feature encoder to feature decoder, to achieve end-to-end pixel prediction, and obtain a complete and continuous detection image of the subject contour. Using the BSDS500 atlas as the experimental sample, the average accuracy index is 0.85, which runs on the device CPU at a detection rate of 20+ FPS to achieve a good balance between training efficiency and detection effect.</p>

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“…Basically, they designed an algorithm of flexible representation of quantum. [ 1 ] They simply use quantum flexibility to represent the image, whose pixels are processed as quantum states sequence. This provides amazing computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

et al. 2023

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Image enhancement is important given that it can be used to highlight the area of interest in the images. This article designs four filters via special function for realizing image enhancement. Firstly, a filter based on the exponential function is designed. When the value of the progression is even, the edge feature can be extracted. When the value of the progression is odd, sharp contrast can be obtained. Secondly, a filter is built using hyperbolic cosine and its inverse function, where a printmaking feature can be extracted. Thirdly, a filter is made via a hyperbolic secant function and its inverse. It can lead to the extraction of image edge. When the progression value is increasing, marginal effect can be found and the brightness is decreasing. Ripple morphology can be found. Fourthly, a filter is constructed through a hyperbolic sine function and its inverse, where marginal features can be extracted. Furthermore, these filters are useful for extracting the marginal features even when a high noise density of 0.9 is added to the original images. They are useful for highlighting the images acquired from near infrared imaging.

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A multimedia image edge extraction algorithm based on flexible representation of quantum

Cited by 8 publications

References 12 publications

Intelligent computational techniques for multimodal data

Intelligent computational techniques for multimodal data

Saliency Contour Extraction Based on Multi-level Feature Channel Optimization Coding

Image Enhancement via Special Functions and Its Application for Near Infrared Imaging

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