Search

Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively.

Moving object detection (MOD) has become a popular topic in video analysis due to its use in several applications, including video coding in wireless surveillance. However, implementing MOD in constrained sensors is challenging due to their high complexity and energy consumption. Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems. In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed. The algorithm ensures a trade-off between detection accuracy and computational complexity. To this end, we propose constructing an activity map by measuring each block activity between successive frames. The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-the-art techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.

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 other 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 methods’ ROI data is available. To address this limitation, a robust algorithm is developed by combining the human eye tracking (subjective) and the graph-based visual saliency modelling (objective) information to determine a more realistic ROI for a scene. To carry out this process, in one hand, several 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 these two pieces of 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 more consistent to the benchmark ROI, which was previously decided by a group of video coding experts. 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.

Light field videos provide a rich representation of real-world, thus the research of this technology is of urgency and interest for both the scientific community and industries. Light field applications such as virtual reality and post-production in the movie industry require a large number of viewpoints of the captured scene to achieve an immersive experience, and this creates a significant burden on light field compression and streaming. In this paper, we first present a light field video dataset captured with a plenoptic camera. Then a new region-of-interest (ROI)-based video compression method is designed for light field videos. In order to further improve the compression performance, a novel view synthesis algorithm is presented to generate arbitrary viewpoints at the receiver. The experimental evaluation of four light field video sequences demonstrates that the proposed ROI-based compression method can save 5%-7% in bitrates in comparison to conventional light field video compression methods. Furthermore, the proposed view synthesis-based compression method not only can achieve a reduction of about 50% in bitrates against conventional compression methods, but the synthesized views can exhibit identical visual quality as their ground truth.