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Scene analysis is a relevant research field for its several applications in the area of computer vision. This paper attempts to analyze scene information present in the image by augmenting salient object information with background information. The salient object is initially identified using a method called as Minimum Directional Contrast (MDC). The underlying assumption behind using this method for defining salient objects is that salient pixels have higher minimum directional contrast than nonsalient pixels. Finding MDC provides us with a raw salient metric. The gradient vector flow (GVF) model of image segmentation inculcates the raw saliency information. The gradient of MDC is calculated and added to the data term of the energy functional of GVF so that the contour formation utilizes not only edge formation but also saliency information. The result obtained gives us not only the salient object but also added background information. Three public datasets have been used to evaluate the results obtained. The comparative study of the proposed method for salient object detection with other state-of-the-art methods available in the literature is presented in terms of precision, recall, and F1-Score.

Medicine information retrieval has grown significantly and is based on the structural similarity of medicine molecules. The chemical structural formula (CSF) is a primary search target as a unique identifier for each compound in the research field of medical information. This paper introduces a graph-based CSF retrieval system, PharmKi, which accepts the photos taken from smartphones and the sketches drawn on the tablet PCs as inputs. To establish a compact yet efficient hypergraph representation for molecules, we propose a graph-isomorphism-based algorithm for evaluating the spatial similarity between graphical CSFs. An indexing strategy based on the graph TF-IDF technology is also introduced to achieve a high efficiency for large-scale molecule retrieval. The results of comparative study demonstrate that the proposed method outperforms the existing methods on accuracy, and performs well on efficiency.

We propose a novel feature extraction method and a cost function for stereo matching, that is robust and stable in matching images from different photographing conditions. Based on the Spearman rank correlation, for the pixel in the window centered around a certain pixel, the code is proposed as a rank sequence obtained from an ordered set of the pixel values. We also apply the same approach to X-gradient image and Y-gradient image. Finally, three rank sequences are obtained from the matching windows of original image, X-gradient image and Y-gradient image. They are used as the matching features of the center pixel. Then, the matching cost of two pixels will be defined as the weighted combination of the difference between their features. Our algorithm has a better matching effect than existing algorithms, especially in low-texture areas and object boundaries. We conducted experiments on Middlebury dataset that has different illumination and exposure images and KITTI dataset whose images were taken outdoor under radiometric distortions. The experimental results indicate that our algorithm is superior to the recently developed algorithms under radiometric variations, such as fuzzy encoding pattern1 and robust soft rank transform,3 whereas the speed is still fast.

We show how to perform automatic attribution of movie's authorship by the statistical analysis of two simple formal features: the length of camera takes (shot duration), and the distance between the camera and the subject (shot scale). Experiments include 143 films by eight distinguishable directors over 70 years of historiography of author cinema.

The big picture of 5G will bring a range of new unique service capabilities, where ensuring Quality of Experience (QoE) continuity in challenging situations such as high mobility, e.g., on-board user equipments (UEs) in high-speed train (HST) are one of the sharp killer applications. In this paper, we propose a mobile edge computing (MEC) driven solution to improve the QoE for UEs in the HST with perceived dynamic adaptive streaming over HTTP video demands. Considering the challenging wireless communication conditioning (e.g., path loss and Doppler Effect due to high mobility) between HST and base station along the railway for enabling progress and seamless video consuming, the case study shows the benefit of MEC functions mainly from content prefetching and complementarily from content caching, over benchmark solution where UEs solely download video segments through challenging wireless channel.

We proposed a residual-based post-processing for high-efficiency video coding (HEVC). Based on the proposed network, residual-based video restoration network (residual-VRN), the decoded image quality, has been improved significantly. We experimentally verified the superiority of the method.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.