ORCID Profile
0000-0002-0190-0700
Current Organisation
National University of Sciences and Technology
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Publisher: Indian Society for Education and Environment
Date: 23-06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 09-12-2019
DOI: 10.3390/APP9245378
Abstract: Saliency is the quality of an object that makes it stands out from neighbouring items and grabs viewer attention. Regarding image processing, it refers to the pixel or group of pixels that stand out in an image or a video clip and capture the attention of the viewer. Our eye movements are usually guided by saliency while inspecting a scene. Rapid detection of emotive stimuli an ability possessed by humans. Visual objects in a scene are also emotionally salient. As different images and clips can elicit different emotional responses in a viewer such as happiness or sadness, there is a need to measure these emotions along with visual saliency. This study was conducted to determine whether the existing available visual saliency models can also measure emotional saliency. A classical Graph-Based Visual Saliency (GBVS) model is used in the study. Results show that there is low saliency or salient features in sad movies with at least a significant difference of 0.05 between happy and sad videos as well as a large mean difference of 76.57 and 57.0, hence making these videos less emotionally salient. However, overall visual content does not capture emotional salience. The applied Graph-Based Visual Saliency model notably identified happy emotions but could not analyze sad emotions.
Publisher: MDPI AG
Date: 13-03-2020
DOI: 10.3390/S20061601
Abstract: Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.
Publisher: IEEE
Date: 05-05-2019
No related grants have been discovered for Asim Waris.