Visualizing and Comparing Online Travel Reviews of the Great Walls: A Data Mining Approach

This research employs two samples of heritage sites of the Great Wall of China (Ba daling Great Wall and Mu tianyu Great Wall) and their 21000 reviews on TripAdvisor to visualize and induce feature-related comparisons. Word2vec and D3.js are applied for statistical computing and graphing Minimal Spanning Tree (MST) and ThemeRiver. The applications of MST and ThemeRiver are used to delineate outstanding features and clearer feature relationships. In terms of methodology, we applied an innovative research route to combine MST with ThemeRiver to visualize travellers’ online comments. At the same time, the visual results obtained are combined with qualitative analysis to generate valuable, intuitive summaries that can be used for reference in future research. Practically, the results disclose that although both sites are highly enjoyed by tourists, they are significantly different in terms of service, infrastructure and scenery. This article has implications for policymakers and practitioners with regard to making use of online reviews to gather authentic visitor comments on the Great Wall.

Vitrectomy in Uveitis

Nowadays, with the development of modern vitreoretinal microsurgery techniques, indications of vitrectomy in uveitis have expanded significantly and visual results have improved. In selected cases, surgeries performed by suppressing inflammation in the required indications affect the visual prognosis positively. However, it should be kept in mind that anatomical and functional success rates are lower in eyes with uveitis compared to other eyes, and the risk of developing postoperative complications is higher. The purpose of this review is to discuss the role of vitrectomy in the treatment of uveitis.

Saliency-Guided Remote Sensing Image Super-Resolution

Deep learning has recently attracted extensive attention and developed significantly in remote sensing image super-resolution. Although remote sensing images are composed of various scenes, most existing methods consider each part equally. These methods ignore the salient objects (e.g., buildings, airplanes, and vehicles) that have more complex structures and require more attention in recovery processing. This paper proposes a saliency-guided remote sensing image super-resolution (SG-GAN) method to alleviate the above issue while maintaining the merits of GAN-based methods for the generation of perceptual-pleasant details. More specifically, we exploit the salient maps of images to guide the recovery in two aspects: On the one hand, the saliency detection network in SG-GAN learns more high-resolution saliency maps to provide additional structure priors. On the other hand, the well-designed saliency loss imposes a second-order restriction on the super-resolution process, which helps SG-GAN concentrate more on the salient objects of remote sensing images. Experimental results show that SG-GAN achieves competitive PSNR and SSIM compared with the advanced super-resolution methods. Visual results demonstrate our superiority in restoring structures while generating remote sensing super-resolution images.

A Video Quality Testing : Review of Human Visual Aspect

Various types of video player applications have been widely used by the community. The emergence of the latest version and a variety of features make people need to make a choice to use a video player application with a good visual level. The type of video that is often played is a file with an MP4 extension. This file type is not heavy but is usually intended for long file durations such as movies. In this paper, we will use a dataset in the form of a movie file with an MP4 extension. The video player applications used include VLC, Quick time, Potplayer, KMPLayer, Media Player Classic (MPC), DivX Player, ACG Player, Kodi, MediaMonkey. Through various empirical calculations, such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structutral Similarity Index Measurement (SSIM), Threshold F-ratio, Visual Signal to Noise Ratio (VSNR), Visual Quality Metric (VQM), and Multiscale - Structutral Similarity Index Measurement (MS-SSIM) has analyzed the visual capabilities of each video player application. Experimental results prove that the KMPlayer application gets the best visual results compared to other selected applications.

Systematic Design Applied in Outdoor Spatiotemporal Lighting

Outdoor lighting design consists of many different objectives, depending on the area that is illuminated. In addition, besides functional lighting, extra messages may be superimposed to a lighting configuration. This adds to the complexity of the lighting design. Furthermore, temporal variations in lighting may be used as an additional tool of expressivity. All the above require some basic training and also some familiarity with expression tools. In this work, a framework is given for seamless communication through lighting, including both spatial and temporal lighting patterns. To this end, two different kinds of time scales are considered, leading to case studies for both seasonal lighting and communication through rapid spatiotemporal differentiations in it. The framework is two-fold, allowing for both diagrammatic and quasi-algebraic elaboration, leading to interesting visual results and providing the first step towards optimization. Different cases of outdoor lighting are considered as case studies, namely façade lighting and glass cases. These are used to illustrate the applicability and the added value of the current framework, that is, the systematization of the lighting procedure taking into account artistic interventions, which can be considered an extension of utilizing semantics.

Compression and Visualization Interactive of 3D Mesh

The combination of compression and visualization is mentioned as perspective, very few articles treat with this problem. Indeed, in this paper, we proposed a new approach to multiresolution visualization based on a combination of segmentation and multiresolution mesh compression. For this, we proposed a new segmentation method that benefits the organization of faces of the mesh followed by a progressive local compression of regions of mesh to ensure the refinement local of the three-dimensional object. Thus, the quantization precision is adapted to each vertex during the encoding /decoding process to optimize the rate-distortion compromise. The optimization of the treated mesh geometry improves the approximation quality and the compression ratio at each level of resolution. The experimental results show that the proposed algorithm gives competitive results compared to the previous works dealing with the rate-distortion compromise and very satisfactory visual results.

Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison

Due to the huge demand for textile production in China, fabric defect detection is particularly attractive. At present, an increasing number of supervised deep-learning methods are being applied in surface defect detection. However, the annotation of datasets in industrial settings often depends on professional inspectors. Moreover, the methods based on supervised learning require a lot of annotation, which consumes a great deal of time and costs. In this paper, an approach based on self-feature comparison (SFC) was employed that accurately located and segmented fabric texture images to find anomalies with unsupervised learning. The SFC architecture contained the self-feature reconstruction module and the self-feature distillation. Accurate fiber anomaly location and segmentation were generated based on these two modules. Compared with the traditional methods that operate in image space, the comparison of feature space can better locate the anomalies of fiber texture surfaces. Evaluations were performed on the three publicly available databases. The results indicated that our method performed well compared with other methods, and had excellent defect detection ability in the collected textile images. In addition, the visual results showed that our results can be used as a pixel-level candidate label.

Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

Revisiting and Optimising a CNN Colour Constancy Method for Multi-Illuminant Estimation

The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a ‘white’ illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.