A low complexity visual saliency model based on in-focus regions and centre sensitivity

Chilukamari, Jayachandra; Kannangara, Sampath; Maxwell, Grant

doi:10.1109/icce-berlin.2014.7034250

Cited by 3 publications

(2 citation statements)

References 23 publications

(23 reference statements)

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“…The rationale behind selecting is its simplicity, having an appearance of ground truth, seemingly worthy of approval and its natural parallelism property. Different studies show that it performs better compared to other saliency models [48].…”

Section: Proposed Experimental Set-upmentioning

confidence: 98%

Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

2022

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The Region of interest (ROI) analysis is widely used in image analytics, video coding, computer graphics, computer vision, medical imaging, nuclear medicine, computer tomography and many more areas in medical applications. This ROI determination process using subjective method (e.g. using human vision) often differ from the objective ones (e.g. using mathematical modelling). However, there is no existing method in the literature that could provide a single decision when both method's ROI data are available. To address this limitation, in this paper, a robust algorithm is developed through a combining process of human eye tracking (subjective) and graph-based saliency modelling (objective) information to determine a more realistic ROI for a scene. To carry out this process, in one hand, a number of different independent human visual saliency factors such as pupil size, pupil dilation, central tendency, fixation pattern, and gaze plot for a group of twenty-two participants are collected by applying on a set of publicly available eighteen video sequences. On the other hand, the features of Graph based visual saliency (GBVS) highlights conspicuity in the scene. Gleaned from this two information, the proposed algorithm determines the final ROI based on some heuristics. Experimental results show that for a wide range of video sequences and compared to the existing deep learning based (MxSalNet) and depth pixel (DP) based ROI, the proposed ROI is consistent to the benchmark ROI, which was previously decided by a group video coding expert. As the subjective and objective options frequently create an ambiguity to reach a single decision on ROI, the proposed algorithm could determine an ultimate decision, which is eventually validated by experts' opinion.

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Section: Proposed Experimental Set-upmentioning

confidence: 98%

Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

2022

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“…Achanta et al [18] proposed a frequency model, in which the difference of arithmetic mean vector of an image and its Gaussian blurred version is used to define image saliency. Chilukamari et al proposed frequency models in [19,20] to define saliency based on in-focus regions of an image.…”

Section: Introductionmentioning

confidence: 99%

A Low-complexity Wavelet-based Visual Saliency Model to Predict Fixations

Narayanaswamy

Zhao

Fung

et al. 2020

2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

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A low-complexity wavelet-based visual saliency model to predict the regions of human eye fixations in images using low-level features is proposed. Unlike the existing wavelet-based saliency detection models, the proposed model requires only two channels-luminance (Y) and chrominance (Cr) in YCbCr colour space for saliency computation. These two channels are decomposed to their lowest resolution using Discrete Wavelet Transform (DWT) to extract local contrast features at multiple scales. These features are integrated at multiple levels using 2D entropy based combination scheme to derive a combined map. The combined map is normalised and enhanced using natural logarithm transformation to derive a final saliency map. The experimental results show that the proposed model has achieved better prediction accuracy with significant complexity reduction compared to the existing benchmark models over two large public image datasets.

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An Analysis of Human Engagement Behaviour Using Descriptors from Human Feedback, Eye Tracking, and Saliency Modelling

Podder

Paul

Debnath

et al. 2015

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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A low complexity visual saliency model based on in-focus regions and centre sensitivity

Cited by 3 publications

References 23 publications

Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

A Low-complexity Wavelet-based Visual Saliency Model to Predict Fixations

An Analysis of Human Engagement Behaviour Using Descriptors from Human Feedback, Eye Tracking, and Saliency Modelling

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