Escher transmutation is a graphic art that smoothly transforms one tile pattern into another tile pattern with dual perception. A classic example is the artwork called Sky and Water, in which a compelling figure-ground arrangement is applied to portray the transmutation of a bird in sky and a fish in water. The shape of a bird is progressively deformed and dissolves into the background while the background gradually reveals the shape of a fish. This paper introduces a system to create a variety of Escher-like transmutations, which includes the algorithms for initializing a tile pattern with dual figure-ground arrangement, for searching for the best matched shape of a user-specified motif from a database, and for transforming the content and shapes of tile patterns using a content-aware warping technique. The proposed system, integrating the graphic techniques of tile initialization, shape matching, and shape warping, allows users to create various Escher-like transmutations with minimal user interaction. Experimental results and conducted user studies demonstrate the feasibility and flexibility of the proposed system in Escher art generation.

Heart failure (HF) is one of the most common causes of hospitalization for people over 65 years old, and over half of patients who diagnosed with severe HF conditions at first time cannot survive over 5 years. It is also noticed that rehospitalization rate of heart failure patients may increase to 50% in three months and the mortality rate from 33% to 50% within five years if HF patients are not treated with proper medication and physical therapies. Here we provide a classification system and an early warning mechanism for detecting HF disease based on integrating 6-minute walking test (6MWT), Internet of medical thing devices, and cloud computing technologies. This study performed 6MWTs for 50 HF patients accompanied by medical staffs for recording walkway distance, walking heart rate, and resting heart rate. All retrieved features and classified functional levels of heart organ of HF patients are trained as a target referencing dataset. According to the selected features and trained results, the clustered information representing various heart conditions is applied for detecting potential HF patients at earlier stages. In addition, the newly obtained self-exercise rehabilitation records from HF patients in a real-time manner will be compared to her/his previous 6MWT patterns. The compared differences are considered as important information for doctors to arrange medical treatment and adjusted physical therapy during the next follow-up visit in hospital.

Ubiquitous Internet environment and matured key technologies of sensing devices provide diversified applications of Internet of Thing (IoT). IoT in intelligent healthcare is one of the major topics for convincing powerful functions of IoT regarding the promotion of national health policies, strategies, and plans. However, according to statistics reports, annual global mortality for cardiovascular diseases remained as high as 29%, of which coronary artery disease (CAD) is accounted for 42%. Unfortunately, almost half of the patients not knowing they are at risk of cardiovascular diseases until serious symptoms occurred. Here, we proposed an early warning system by integrating IoT, big data, and cloud computing technologies which physically linked among personal communication devices of patient/doctor, cloud systems, and hospital medical information systems. The system collects personal heart rate and metabolic equivalent (MET) features from calibrated fitness devices wore by users, and corresponding fitted curves are dynamically calculated and compared to previously trained curve patterns from 213 patients with heart diseases and 124 healthy individuals. The prototype system was already successfully developed and validated by a couple of testing cases with excellent performance. To ensure the robustness of the proposed system, now we are evaluating system reliability and trying to collect more testing samples for further validating effectiveness of the proactive healthcare mechanism.

The key to content-aware warping and cropping is adapting data to fit displays with various aspect ratios while preserving visually salient contents. Most previous studies achieve this objective by cropping insignificant contents near frame boundaries and consistently resizing frames through an optimization technique with various preservation constraints and fixed boundary conditions. These strategies significantly improve retargeting quality. However, warping under fixed boundary conditions may bound/limit the preservation of visually salient contents. Moreover, dynamic frame cropping and frame alignment may result in unnatural object/camera motions. In this paper, a floating boundary with volumetric warping and object-aware cropping is proposed to address these problems. In the proposed scheme, visually salient objects in the space-time domain are deformed as rigidly and as consistently as possible using information from matched objects and content-aware boundary constraints. The content-aware boundary constraints can retain visually salient contents in a fixed region with a desired resolution and aspect ratio, called critical region, during warping. Volumetric cropping with the fixed critical region is then performed to adjust stereoscopic videos to the desired aspect ratios. The strategies of warping and cropping using floating boundaries and spatiotemporal constraints enable our method to consistently preserve the temporal motions and spatial shapes of visually salient volumetric objects in the left and right videos as much as possible, thus leading to good content-aware retargeting. In addition, by considering shape, motion, and disparity preservation, the proposed scheme can be applied to various media, including images, stereoscopic images, videos, and stereoscopic videos. Qualitative and quantitative analyses of stereoscopic videos with diverse camera and considerable motions demonstrate a clear superiority of the proposed method over related methods in terms of retargeting quality.

Quick response (QR) code is generally used for embedding messages such that people can conveniently use mobile devices to capture the QR code and acquire information through a QR code reader. In the past, the design of QR code generators only aimed to achieve high decodability and the produced QR codes usually look like random black-and-white patterns without visual semantics. In recent years, researchers have been tried to endow the QR code with aesthetic elements and QR code beautification has been formulated as an optimization problem that minimizes the visual perception distortion subject to acceptable decoding rate. However, the visual quality of the QR code generated by existing methods still leaves much to be desired. In this work, we propose a two-stage approach to generate QR code with high quality visual content. In the first stage, a baseline QR code with reliable decodability but poor visual quality is first synthesized based on the Gauss-Jordan elimination procedure. In the second stage, a rendering mechanism is designed to improve the visual quality while avoiding affecting the decodability of the QR code. The experimental results show that the proposed method substantially enhances the appearance of the QR code and the processing complexity is near real-time.

A word cloud is a visual representation of a collection of text documents that uses various font sizes, colors, and spaces to arrange and depict significant words. The majority of previous studies on time-varying word clouds focuses on layout optimization and temporal trend visualization. However, they do not fully consider the spatial shapes and temporal motions of word clouds, which are important factors for attracting people's attention and are also important cues for human visual systems in capturing information from time-varying text data. This paper presents a novel method that uses rigid body dynamics to arrange multi-temporal word-tags in a specific shape sequence under various constraints. Each word-tag is regarded as a rigid body in dynamics. With the aid of geometric, aesthetic, and temporal coherence constraints, the proposed method can generate a temporally morphable word cloud that not only arranges word-tags in their corresponding shapes but also smoothly transforms the shapes of word clouds over time, thus yielding a pleasing time-varying visualization. Using the proposed frame-by-frame and morphable word clouds, people can observe the overall story of a time-varying text data from the shape transition, and people can also observe the details from the word clouds in frames. Experimental results on various data demonstrate the feasibility and flexibility of the proposed method in morphable word cloud generation. In addition, an application that uses the proposed word clouds in a simulated exhibition demonstrates the usefulness of the proposed method.

This paper addresses the topic of content-aware stereoscopic image retargeting. The key to this topic is consistently adapting a stereoscopic image to fit displays with various aspect ratios and sizes while preserving visually salient content. Most methods focus on preserving the disparities and shapes of visually salient objects through nonlinear image warping, in which distortions caused by warping are propagated to homogenous and low-significance regions. However, disregarding the consistency of object deformation sometimes results in apparent distortions in both the disparities and shapes of objects. An object-coherence warping scheme is proposed to reduce this unwanted distortion. The basic idea is to utilize the information of matched objects rather than that of matched pixels in warping. Such information implies object correspondences in a stereoscopic image pair, which allows the generation of an object significance map and the consistent preservation of objects. This strategy enables our method to consistently preserve both the disparities and shapes of visually salient objects, leading to good content-aware retargeting. In the experiments, qualitative and quantitative analyses of various stereoscopic images show that our results are better than those generated by related methods in terms of consistency of object preservation.

Tourist and destination maps are thematic maps designed to represent specific themes in maps. The road network topologies in these maps are generally more important than the geometric accuracy of roads. A road network warping method is proposed to facilitate map generation and improve theme representation in maps. The basic idea is deforming a road network to meet a user-specified mental map while an optimization process is performed to propagate distortions originating from road network warping. To generate a map, the proposed method includes algorithms for estimating road significance and for deforming a road network according to various geometric and aesthetic constraints. The proposed method can produce an iconic mark of a theme from a road network and meet a user-specified mental map. Therefore, the resulting map can serve as a tourist or destination map that not only provides visual aids for route planning and navigation tasks, but also visually emphasizes the presentation of a theme in a map for the purpose of advertising. In the experiments, the demonstrations of map generations show that our method enables map generation systems to generate deformed tourist and destination maps efficiently.

Image retargeting is the process of adapting images to fit displays with various aspect ratios and sizes. Most studies on image retargeting focus on shape preservation, but they do not fully consider the preservation of structure lines, which are sensitive to human visual system. In this paper, a patch-based retargeting scheme with an extended significance measurement is introduced to preserve shapes of both visually salient objects and structure lines while minimizing visual distortions. In the proposed scheme, a similarity transformation constraint is used to force visually salient content to undergo as-rigid-as-possible deformation, while an optimization process is performed to smoothly propagate distortions. These processes enable our approach to yield pleasing content-aware warping and retargeting. Experimental results and a user study show that our results are better than those generated by state-of-the-art approaches.

A novel content-aware warping approach is introduced for video retargeting. The key to this technique is adapting videos to fit displays with various aspect ratios and sizes while preserving both visually salient content and temporal coherence. Most previous studies solve this spatiotemporal problem by consistently resizing content in frames. This strategy significantly improves the retargeting results, but does not fully consider object preservation, sometimes causing apparent distortions on visually salient objects. We propose an object-preserving warping scheme with object-based significance estimation to reduce this unpleasant distortion. In the proposed scheme, visually salient objects in 3D space-time space are forced to undergo as-rigid-as-possible warping, while low-significance contents are warped as close as possible to linear rescaling. These strategies enable our method to consistently preserve both the spatial shapes and temporal motions of visually salient objects and avoid overdeformations on low-significance objects, yielding a pleasing motion-aware video retargeting. Qualitative and quantitative analyses, including a user study and experiments on complex videos containing diverse cameras and dynamic motions, show a clear superiority of our method over related video retargeting methods.

In this paper, a novel classification processing based on the spatial information and the concept of Adaboost for hyperspectral image classification is proposed. This classification process is named as adaptive feature extraction with spatial information (AdaFESI). The main idea is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by spectral or spatial classifiers in the previous feature space. All training samples are projected into these feature spaces to train various classifiers and then constitute a multiple classifier system. The experimental results based on two hyperspectral data sets show that the proposed algorithm can generate better classification results.