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Pixel‐based generative image inpainting has been widely researched over recent years and certain level of success via deep learning of feature representations and hallucinations of missing pixel values from surrounding backgrounds have also been reported in the literature. However, existing approaches rely on context‐based attentions and progressive inferences to capture the pixel correlations yet such pixel‐based approaches often fail to adapt to the constantly varying ranges and distances among surrounding background pixels. On the other hand, the modelling cost is also increasingly expensive whenever correlations of those pixels at longer distance away are to be exploited. To resolve the problem, we implement the principle of learning and hallucinating frequency components rather than pixel values. Therefore, we can avoid the dilemma that, on one hand the wish is to exploit all correlated pixels inside the image no matter how far away they are spatially located, but on the other, the price of increasing the modelling cost incurred by those pixels far away from the missing regions has to be paid. Extensive experiments carried out verify the effectiveness of the proposed method, which outperforms the representative existing state of the arts in terms of all assessment metrics. [ABSTRACT FROM AUTHOR]

Visual tracking using correlation filters can achieve a high running speed. However, it is still a challenging problem to deal with drastic appearance changes (e.g. large and rapid scale variation of a target) during tracking. In this Letter, a novel dual‐directional scaling state predicting method based on correlation filtering is proposed. The adaptive horizontal and vertical searches are adopted to separately and accurately estimate the scale changes in different directions. The online scale to size module is designed to enhance the prediction robustness of the target appearance. Experimental comparisons with several state‐of‐the‐art object trackers show the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Several recent deep network architectures tried to handle the depth estimation process as an image reconstruction problem, in order to overcome the shortfall that the ground truth depth data is not sufficiently available. The authors introduce and validate an efficient deeper network architecture for unsupervised depth estimation with an automated parameter optimisation. In addition, a hybrid appearance loss function is also proposed to improve the depth estimation accuracy and effectiveness. The authors' proposed model achieves the advantage that individual element of the loss function is weighted using normal distribution characteristics of a Gaussian model. The proposed ideas are validated on KITTI dataset achieving best reported results among recent state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Intrusion detection in real‐time systems is a problem without a profound solution. In supervisory control and data acquisition (SCADA) systems the absence of a defence mechanism that can cope with different types of intrusions is of great importance. False positive alarms or mistakes regarding the origin of the intrusion mean severe costs for the system. An integrated one‐class support vector machine (OCSVM) mechanism that is distributed in a SCADA network is presented, as a part of an intrusion detection system, providing accurate information about the origin and the time of an intrusion. The module reads the network traffic, splits traffic according to the source of the packets and creates a cluster of OCSVM models. These trained models run in parallel and can accurately and fast recognise different types of attacks. [ABSTRACT FROM AUTHOR]