A Fast Algorithm For Region of Interest Detection based on Visual Perception Principles

2013 ◽  
Vol 8 (10) ◽  
pp. 861-868
Author(s):  
Pengyu Liu ◽  
Kebin Jia
Author(s):  
JING ZHANG ◽  
LI ZHUO ◽  
YINGDI ZHAO

According to human vision theory, the image is conveyed from human visual system to brain when people have a look at. Different from previous work, the study reported in this paper attempts to simulate a more real and complex method for region of interest (ROI) detection and quantitatively analyze the correlation between users' visual perception and ROI. In this paper, a visual perception model-based ROI detection is proposed, which can be realized with an ordinary web camera. Visual perception model employs a combination of visual attention model and gaze tracking data to objectively detect ROIs. The work includes pre-ROI estimation using visual attention model, gaze data collection and ROI detection. Pre-ROIs are segmented by the visual attention model. Since eye feature extraction is critical to the accuracy and performance of gaze tracking, adaptive eye template and neural network are employed to predict gaze points. By computing the density of the gaze points, ROIs are ranked. Experimental results show that the accuracy of our ROI detection method can be raised as high as 97% and it is also demonstrated that our model can efficiently adapt to users' interests and match the objective ROI.


2006 ◽  
Vol 45 (7) ◽  
pp. 077201 ◽  
Author(s):  
Huibao Lin

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Chi Yoon Jeong ◽  
Hyun S Yang ◽  
KyeongDeok Moon

In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-of-interest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with state-of-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.


2018 ◽  
Vol 24 (2) ◽  
pp. 1005-1011 ◽  
Author(s):  
Zuraini Othman ◽  
Azizi Abdullah ◽  
Anton Satria Prabuwono

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