Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

Flood verification and mitigation based on ground survey has been impeded by many factors. For instance, damage assessment exclusively by onsite expedition could be delayed by restricted access, especially during heavy flooding. To devise appropriate mitigation measures, resources required to thoroughly investigate the incidents in response to disaster could be overwhelming. Moreover, inadequate equipment and non-standardized flood reporting process could also undermine the maneuver. As a consequent, rescue mission was inevitably deferred until the reported incidents had been confirmed. To address these issues, this paper presents an internetworking system for assisting flooding disaster mitigation. Its main contributions were two folds. Firstly, its data intelligent was driven by convergence of both user and official reports of flood incidents and those automatically detected from remotely sensed images. Secondly, the reliability of reported information was ensured by two-factor verification, i.e. by using a state-of-the-art deep learning strategy and by official investigations. The verified incidents were then presented in augmented graphical reports, which were practical in public inquiry and official uses in devising efficient and cost-effective disaster mitigation measures. The developed system was deployed in different cities, most affected by recent floods. Numerical and subjective assessments herein demonstrate the merits of the system.

Digital watermarking is a technique of hiding information for copyright protection and authentication. In this paper, an invisible digital watermarking algorithm on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) domains is presented. Herein, a binary watermark image was embedded in the middle sub-band coefficients of a video stream. The experimental results of proposed algorithm indicates that the PSNR values of the watermarked videos were as high as almost up to 37 dB with the optimal watermarking strength. The proposed algorithm was proven robust against HEVC stream compression. It is hence anticipated that it could be effectively applied for copyright protection and authentication.

Green space areas are one of the key factors in people’s livelihoods. Their number and size have a significant impact on both the environment and people’s quality of life, including their health. Accordingly, government agencies often rely on information relating to green space areas when devising suitable plans and mandating necessary regulations. At present, obtaining information on green space areas using conventional ground surveys faces a number of limitations. This approach not only requires a lengthy period, but also tremendous human and financial resources. Given such restrictions, the status of a green space is not always up to date. Although software applications, especially those based on geographical information systems and remote sensing, have increasingly been applied to these tasks, the capability to use crowdsourcing data and produce real-time reports is lacking. This is partly because the quantity of data required has, to date, prohibited effective verification by human operators. To address this issue, this paper proposes a novel geospatial platform for green space area management by means of GIS and artificial intelligence. In the proposed system, all user-submitted data are automatically verified by deep learning classification and analyses of the greenness areas on satellite imagery. The experimental results showed that the classification and analyses can identify green space areas at accuracies of 93.50% and 97.50%, respectively. To elucidate the merits of the proposed approach, web-based application software was implemented to demonstrate multimodal data management, cleansing, and reporting. This geospatial system was thus proven to be a viable tool for assisting governmental agencies to devise appropriate plans toward sustainable development goals.