scholarly journals Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaochun Zou ◽  
Xinbo Zhao ◽  
Yongjia Yang ◽  
Na Li
Keyword(s):  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Retinal Image ◽  
Visual Saliency ◽  
Saliency Map ◽  
Support Vector ◽  
Salient Region Detection ◽  
Region Detection ◽  
Saliency Model ◽  
Visual Saliency Model

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist’s image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden’sJstatistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.

Salient Region Detection for Biometric Watermarking

2011 ◽  
pp. 218-236
Author(s):  
Ma Bin ◽  
Li Chun-lei ◽  
Wang Yun-hong ◽  
Bai Xiao
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

Salient Region Detection for Biometric Watermarking

2013 ◽  
pp. 201-219
Author(s):  
Bin Ma ◽  
Chun-lei Li ◽  
Yun-hong Wang ◽  
Xiao Bai
Keyword(s):  
Vision System ◽  
Region Of Interest ◽  
Visual Saliency ◽  
Saliency Map ◽  
Human Vision ◽  
Identification Accuracy ◽  
Biometric Data ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

scholarly journals A Bioinspired Visual Saliency Model

2019 ◽  
Vol 37 (3) ◽  
pp. 503-508
Author(s):  
Ke Zhang ◽  
Xinbo Zhao ◽  
Rong Mo
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Saliency ◽  
Saliency Map ◽  
Input Image ◽  
Visual Signals ◽  
Bottom Up ◽  
Saliency Model ◽  
Visual Saliency Model ◽  
Public Datasets

This paper presents a bioinspired visual saliency model. The end-stopping mechanism in the primary visual cortex is introduced in to extract features that represent contour information of latent salient objects such as corners, line intersections and line endpoints, which are combined together with brightness, color and orientation features to form the final saliency map. This model is an analog for the processing mechanism of visual signals along from retina, lateral geniculate nucleus(LGN)to primary visual cortex V1:Firstly, according to the characteristics of the retina and LGN, an input image is decomposed into brightness and opposite color channels; Then, the simple cell is simulated with 2D Gabor filters, and the amplitude of Gabor response is utilized to represent the response of complex cell; Finally, the response of an end-stopped cell is obtained by multiplying the response of two complex cells with different orientation, and outputs of V1 and LGN constitute a bottom-up saliency map. Experimental results on public datasets show that our model can accurately predict human fixations, and the performance achieves the state of the art of bottom-up saliency model.

scholarly journals Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 239
Author(s):  
Hongmei Liu ◽  
Jinhua Liu ◽  
Mingfeng Zhao
Keyword(s):  
Simulation Analysis ◽  
Image Watermarking ◽  
Additive White Gaussian Noise ◽  
Visual Saliency ◽  
High Energy ◽  
Distribution Model ◽  
Saliency Model ◽  
Detection Approach ◽  
Laplacian Distribution ◽  
Visual Saliency Model

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

A Novel Biologically Inspired Visual Saliency Model

2014 ◽  
Vol 6 (4) ◽  
pp. 841-848 ◽  
Author(s):  
Jingjing Zhao ◽  
Shujin Sun ◽  
Xingtong Liu ◽  
Jixiang Sun ◽  
Afeng Yang
Keyword(s):  
Visual Saliency ◽  
Biologically Inspired ◽  
Saliency Model ◽  
Visual Saliency Model
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