Human emotions and behaviors are reciprocal components that shape each other in everyday life. While the past research on each element has made use of various physiological sensors in many ways, their interactive relationship in the context of daily life has not yet been explored. In this work, we present a wearable affective life-log system (ALIS) that is robust as well as easy to use in daily life to accurately detect emotional changes and determine the cause-and-effect relationship between emotions and emotional situations in users' lives. The proposed system records how a user feels in certain situations during long-term activities using physiological sensors. Based on the long-term monitoring, the system analyzes how the contexts of the user's life affect his/her emotional changes and builds causal structures between emotions and observable behaviors in daily situations. Furthermore, we demonstrate that the proposed system enables us to build causal structures to find individual sources of mental relief suited to negative situations in school life.

This study addresses a view-path-planning problem during 3-D scanning of a large-scale structure based on multiview stereo (MVS) for unmanned aerial platforms. Recently, most studies have adopted an explore-then-exploit strategy for 3-D scanning. The strategy first generates a coarse model from a simple overhead scanning and then plans an inspection path to cover the entire surface of the coarse model. However, even though the inspection path may be optimal, it is difficult to guarantee a complete and accurate reconstruction result due to defective factors of MVS such as occlusions, textureless surfaces, and insufficient parallaxes. Furthermore, the entire procedure of this strategy is inefficiently slow because of path redundancies and long MVS processing time. Therefore, we propose a novel view-path-planning method for 3-D scanning based on an online MVS reconstruction algorithm. The suggested method incrementally reconstructs the target model online and iteratively plans view paths by analyzing the current partial reconstructions. The method continuously analyzes the quality of the model and detects inaccurately reconstructed surfaces. It then plans an inspection path that provides a complete coverage of the detected surfaces while maximizing the performance of MVS. This method can construct a complete 3-D model in a single scanning trial without the need for rescanning. Extensive experiments show that our method outperforms the other state-of-the-art methods, especially in terms of the model completeness of complex structures.

Recent approaches to deep learning-based music analysis have had significant impact on procedural content generation in music-based games. However, the lack of understanding of the unique features of various platforms and interfaces makes auto-generated content less valuable than manually designed content. Hand-crafted datasets are required, to enhance the quality of content in various platforms, but most rhythm games permit only indirect access to the dataset, as a form of player's experience and its replay video. We develop a vision-based approach to content extraction through video analysis, using a format named beatmap. We cover some common visualized features in well-known rhythm games, and construct a mapping from their content to our beatmap model, using multiple object detection. Our method correctly detects each action button, type, and time, and extracts beatmap representations for our target game.

Modern image files are usually progressively transmitted and provide a preview before downloading the entire image for improved user experience to cope with a slow network connection. In this paper, with a similar goal, we propose a progressive transmission framework for deep learning models, especially to deal with the scenario where pre-trained deep learning models are transmitted from servers and executed at user devices (e.g., web browser or mobile). Our progressive transmission allows inferring approximate models in the middle of file delivery, and quickly provide an acceptable intermediate outputs. On the server-side, a deep learning model is divided and progressively transmitted to the user devices. Then, the divided pieces are progressively concatenated to construct approximate models on user devices. Experiments show that our method is computationally efficient without increasing the model size and total transmission time while preserving the model accuracy. We further demonstrate that our method can improve the user experience by providing the approximate models especially in a slow connection.

A color palette is one of the simplest and most intuitive descriptors that can be extracted from images or videos. This paper proposes a method to assess the similarity between color palettes by sorting colors. While previous palette similarity measures compare only colors without considering the overall palette combination, we sort palettes to minimize the geometric distance between colors and align them to share a common color tendency. We propose dynamic closest color warping (DCCW) to calculate the minimum distance sum between colors and the graph connecting the colors in the other palette. We evaluate the proposed palette sorting and DCCW with several datasets and demonstrate that DCCW outperforms previous methods in terms of accuracy and computing time. We validate the effectiveness of the proposed sorting technique by conducting a perceptual study, which indicates a clear preference for the results of our approach. We also demonstrate useful applications enabled by DCCW, including palette interpolation, palette navigation, and image recoloring.

A color scheme is an association of colors, i.e., a subset of all possible colors, that represents a visual identity. We propose an automated method to extract a color scheme from a movie. Since a movie is a carefully edited video with different objects and heterogeneous content embodying the director's messages and values, it is a challenging task to extract a color scheme from a movie as opposed to a general video filmed at once without distinction of shots or scenes. Despite such challenges, color scheme extraction plays a very important role in film production and application. The color scheme is an interpretation of the scenario by the cinematographer and it can convey a mood or feeling that stays with the viewer after the movie has ended. It also acts as a contributing factor to describe a film, like the metadata fields of a film such as a genre, director, and casting. Moreover, it can be automatically tagged unlike metadata, so it can be directly applied to the existing movie database without much effort. Our method produces a color scheme from a movie in a bottom-up manner from segmented shots. We formulate the color extraction as a selection problem where perceptually important colors are selected using saliency. We introduce a semi-master-shot, an alternative unit defined as a combination of contiguous shots taken in the same place with similar colors. Using real movie videos, we demonstrate and validate the plausibility of the proposed technique.

We introduce a new type of 3D text-based CAPTCHA, called DotCHA, which relies on human interaction and overcomes the limitations of existing 2D and 3D CAPTCHAs. DotCHA asks users to rotate a 3D text model to identify the correct letters. The 3D text model is a twisted form of sequential 3D letters around a center pivot axis, and it shows different letters depending on the rotation angle. Because each model consists of many small spheres instead of a solid letter model, DotCHA is classified as a scatter-type CAPTCHA and resists character segmentation attacks. Moreover, DotCHA is resistant to machine learning attacks because each letter is only identified in a particular direction. We demonstrate that DotCHA is resistant to existing types of attacks while maintaining usability.

Median filtering is used as an anti-forensic technique to erase processing history of some image manipulations such as JPEG, resampling, etc. Thus, various detectors have been proposed to detect median filtered images. To counter these techniques, several anti-forensic methods have been devised as well. However, restoring the median filtered image is a typical ill-posed problem, and thus it is still difficult to reconstruct the image visually close to the original image. Also, it is further hard to make the restored image have the statistical characteristic of the raw image for the anti-forensic purpose. To solve this problem, we present a median filtering anti-forensic method based on deep convolutional neural networks, which can effectively remove traces from median filtered images. We adopt the framework of generative adversarial networks to generate images that follow the underlying statistics of unaltered images, significantly enhancing forensic undetectability. Through extensive experiments, we demonstrate that our method successfully deceives the existing median filtering forensic techniques.

As computer graphics technology advances, it is becoming increasingly difficult to determine whether a given picture was taken by camera or via computer graphics. In this work, we propose a method to using simple CNN structures to identify photorealistic computer graphics (PRCG) using convolutional neural networks (CNN). This network trained to identify the source of image patches. We showed the network without pooling layer showed 98.2% accuracy, which is 2.1% higher than the result of using conventional object-recognition network. Testing random patches from image, the accuracy of identifying image reached 98.5%. Furthermore, it is possible to detect the photograph-PRCG synthesized regions from the image.

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.

3D printing content is a new form of content being distributed in digital as well as analog domains. Therefore, its security is the biggest technical challenge of the content distribution service. In this paper, we analyze the 3D print-scan process, and we organize possible distortions according to the processes with respect to 3D mesh watermarking. Based on the analysis, we propose a circular shift coding structure for the 3D model. When the rotating disks of the coding structure are aligned in parallel to the layers of the 3D printing, the structure preserves a statistical feature of each disk from the layer dividing process. Based on the circular shift coding structure, we achieve a 3D print-scan resilient watermarking scheme. In experimental tests, the proposed scheme is robust against such signal processing, and cropping attacks. Furthermore, the embedded information is not lost after 3D print-scan process.

Femtocell networks can supplement the conventional cellular system with massive data communication in an indoor environment. In this paper, we consider the indoor coverage and load balance problem of femto base stations in order to provide better quality of service. By exploiting the Voronoi diagram, we can achieve maximal indoor coverage with minimized maximum required transmit power, which results in reduced interference. Our algorithm can adopt any radio propagation model to achieve more accurate coverage for an indoor environment with various obstacles. Time varying data traffic may cause unbalanced data load of base stations producing traffic overload. Our dynamic power control algorithm redistributes the data load by automatically adjusting transmit power levels according to data traffic estimation while preserving coverage. Simulations show that the proposed scheme achieves better coverage, reduced interference, and good load balance compared to previous algorithms.

In this paper, we propose a new class of message routing scheme for a distributed network which can scale up to the needs of a very large number of mobile users. We leverage the work in the GeoKad paper which uses the Distributed Geographic Table, allowing node retrieval from resources that are close to any given region. The paper addresses the problem regarding efficient message passing and peer misses. We propose a new routing scheme that addresses this kind of limitation. In fact, the problem can be solved in two ways, either by preserving the state of peer and further use a prediction algorithm or by better message routing. The proposed scheme is based on swarm intelligence (SI) and its attribute of exploit and explore. We leverage the statistical properties of time-varying network connectivity for opportunistic message forwarding. Considering the present 3G or Wi-Fi environment this approach can also help in optimizing energy as it can prevent query over flooding which is mostly encountered in such network scenarios.

The best-case and the worst-case coverage were proposed originally for a single source and destination pair in a sensor network. In this paper, we propose a new coverage measure of the sensor network considering arbitrary paths. Surprisingly, this new measure captures both the best-case and the worst-case coverage of the sensor network simultaneously, enabling us to evaluate the given network in a global viewpoint. Accordingly, we pose the evaluation and the deployment problems; the former is to evaluate the new coverage measure of a given sensor network, and the latter is to find an optimal placement of k additional sensor nodes to improve the coverage for a given positive integer k. We present several algorithms solving the problems that are either centralized or localized with theoretical proofs and simulation results, showing that our algorithms are efficient and easy to implement in practice while the quality of outputs is guaranteed by formal proofs. Our algorithms are based on an interesting relation between our new coverage measure and a certain quantity of a point set, called the bottleneck, which has been relatively well studied in other disciplines. In doing so, we prove that a maximal support path can always be found in the minimum spanning tree; this is another contribution of ours.

Abstract We propose a novel, multi‐resolution method to efficiently perform large‐scale cloth simulation. Our cloth simulation method is based on a triangle‐based energy model constructed from a cloth mesh. We identify that solutions of the linear system of cloth simulation are smooth in certain regions of the cloth mesh and solve the linear system on those regions in a reduced solution space. Then we reconstruct the original solutions by performing a simple interpolation from solutions computed in the reduced space. In order to identify regions where solutions are smooth, we propose simplification metrics that consider stretching, shear, and bending forces, as well as geometric collisions. Our multi‐resolution method can be applied to many existing cloth simulation methods, since our method works on a general linear system. In order to demonstrate benefits of our method, we apply our method into four large‐scale cloth benchmarks that consist of tens or hundreds of thousands of triangles. Because of the reduced computations, we achieve a performance improvement by a factor of up to one order of magnitude, with a little loss of simulation quality.

Abstract Given a still picture, tour into the picture (TIP) generates a walk‐through animation of a 3D scene constructed from the picture. In this paper, we generalize TIP to deal with water surface reflection while allowing foreground objects to move. We formulate a non‐linear optimization problem to find the 3D scene parameters with respect to the camera position, to automatically construct a reasonable 3D scene model, provided with a set of points and their corresponding points on the water surface. To synthesize a stream of reflected images on the water surface in accordance with the camera movement, we propose a novel image‐based approach, which makes the best of the limited information available in the input picture. Furthermore, we incorporate water surface movement data acquired from water simulation on top of stochastic motion textures for objects such as trees and plants, to create a dynamic scene. Copyright © 2006 John Wiley & Sons, Ltd.

Randomized incremental constructions are widely used in computational geometry, but they perform very badly on large data because of their inherently random memory access patterns. We define a biased randomized insertion order which removes enough randomness to significantly improve performance, but leaves enough randomness so that the algorithms remain theoretically optimal.

Given a surface mesh F in R 3 with vertex set S and consisting of Delaunay triangles, we want to construct the Delaunay tetrahedralization of S.We present an algorithm which constructs the Delaunay tetrahedralization of S given a bounded degree spanning subgraph T of F. It accelerates the incremental Delaunay triangulation construction by exploiting the connectivity of the points on the surface. If the expected size of the Delaunay triangulation is linear, we prove that our algorithm runs in O(n log* n) expected time, speeding up the standard randomized incremental Delaunay triangulation algorithm, which is O(n log n) expected time in this case.We discuss how to find a bounded degree spanning subgraph T from surface mesh F and give a linear time algorithm which obtains a spanning subgraph from any triangulated surface with genus g with maximum degree at most 12g for g>0 or three for g=0.

The problem of computing a piecewise linear approximation to a surface from a set of sample points is important in solid modeling, computer graphics and computer vision. A recent algorithm [1] using the Voronoi diagram of the sample points gave a guarantee on the distance of the output surface from the original sampled surface assuming the sample was `suciently dense'. We give a similar algorithm, simplifying the computation and the proof of the geometric guarantee. In addition, we guarantee that our output surface is homeomorphic to the original surface; to our knowledge this is the rst such topological guarantee for this problem. 1 Introduction A number of applications in CAD, computer graphics, computer vision and mathematical modeling involve the computation of a piecewise lin- Dept. of Computer Science, U. of Texas, Austin TX 78712. e-mail: [email protected], supported by NSF grant CCR-9731977 y Dept. of Computer Science, U. of Texas, Austin, TX 78712. e-mail: sunghe...