This paper presents an original methodology for extracting semantic features from X-rays images that correlate to severity from a data set with patient ICU admission labels through interpretable models. The validation is partially performed by a proposed method that correlates the extracted features with a separate larger data set that does not contain the ICU-outcome labels. The analysis points out that a few features explain most of the variance between patients admitted in ICUs or not. The methods herein can be viewed as a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. In between features shown to be over-represented in the external data set were ones like ‘Consolidation’ (1.67), ‘Alveolar’ (1.33), and ‘Effusion’ (1.3). A brief analysis on the locations also showed higher frequency in labels like ‘Bilateral’ (1.58) and Peripheral (1.28) in patients labelled with higher chances to be admitted in ICU. To properly handle the limited data sets, a state-of-the-art lung segmentation network was also trained and presented, together with the use of low-complexity and interpretable models to avoid overfitting.

Affective computing is an emerging multidisciplinary research field that is increasingly drawing the attention of researchers and practitioners in various fields, including artificial intelligence, natural language processing, cognitive and social sciences. Research in affective computing includes areas such as sentiment, emotion, and opinion modelling. The internet is an excellent source of data required for sentiment analysis, such as customer reviews of products, social media, forums, blogs, etc. Most of these data, called big data, are unstructured and unorganized. Hence there is a strong demand for developing suitable data processing techniques to process these rich and valuable data to produce useful information. Early surveys on sentiment and emotion recognition in the literature have been limited to discussions using text, audio, and visual modalities. So far, to the author's knowledge, a comprehensive survey combining physiological modalities with these other modalities for affective computing has yet to be reported. The objective of this paper is to fill the gap in this surveyed area. The usage of physiological modalities for affective computing brings several benefits in that the signals can be used in different environmental conditions, more robust systems can be constructed in combination with other modalities, and it has increased anti-spoofing characteristics. The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.

With the advancement of displaying technologies, virtual viewpoint video needs to be synthesized from adjacent viewpoints to provide immersive perceptual viewing experience of a scene. View synthesized techniques suffer poor rendering quality due to holes created by occlusion in the warping process. Currently, spatial and temporal correlation techniques are used to improve the quality of the synthesized view. However, spatial correlation e. g. inpainting and inverse mapping (IM) techniques cannot fill holes efficiently due to low spatial correlation in the edge between foreground and background pixels. On the other hand, the temporal correlation among already synthesized frames through learning by Gaussian mixture modelling (GMM) may fill occluded areas efficiently. However, there are no frames for GMM learning when the user switches view instantly. To address the aforementioned issues, in the proposed view synthesis technique, we apply GMM on the adjacent viewpoint videos. Then, we utilize the number of GMM models to refine pixel intensities of the synthesized view by using a weighting factor between pixel intensities in GMM models and warped images. This technique provides a better pixel correspondence, which improves 0.47~0.58dB PSNR compared to the IM technique.

High quality virtual views need to be synthesized from adjacent available views for free viewpoint video and multiview video coding (MVC) to provide users with a more realistic 3D viewing experience of a scene. View synthesis techniques suffer from poor rendering quality due to holes created by occlusion and rounding integer error through warping. To remove the holes in the virtual view, the existing techniques use spatial and temporal correlation in intra/inter-view images and depth maps. However, they still suffer quality degradation in the boundary region of foreground and background areas due to the low spatial correlation in texture images and low correspondence in inter-view depth maps. To overcome the above-mentioned limitations, we use a number of models in the Gaussian mixture modeling (GMM) to separate background and foreground pixels in our proposed technique. Here, the missing pixels introduced from the warping process are recovered by the adaptive weighted average of the pixel intensities from the corresponding GMM model(s) and warped image. The weights vary with time to accommodate the changes due to a dynamic background and the motions of the moving objects for view synthesis. We also introduce an adaptive strategy to reset the GMM modeling if the contributions of the pixel intensities drop significantly. Our experimental results indicate that the proposed approach provides 5.40-6.60-dB PSNR improvement compared with the relevant methods. To verify the effectiveness of the proposed view synthesis technique, we use it as an extra reference frame in the motion estimation for MVC. The experimental results confirm that the proposed view synthesis is able to improve PSNR by 3.15-5.13 dB compared with the conventional three reference frames.

This paper addresses the problem of error-resilient source coding for 3-D video transmission over packet-loss networks. The proposed approach jointly optimizes the texture coding mode and the depth coding mode for each macroblock in the reference views. Firstly, a distortion model is developed to capture the effect of the texture distortion and depth distortion on the synthesized view. Then, joint optimization of texture and depth coding modes is derived based upon an operational rate-distortion framework using Lagrange multiplier method. In particular, a dual trellis-based algorithm is introduced in order to overcome the macroblock interdependencies of texture and depth map in the optimization procedure. Simulation results demonstrate that significant and consistent gains can be achieved over currently used techniques.