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The vector graphics with gradient mesh can be attributed to their compactness and scalability; however, they tend to fall short when it comes to real-time editing due to a lack of real-time rasterization and an efficient editing tool for image details. In this paper, we encode global manipulation geometries and local image details within a hybrid vector structure, using parametric patches and detailed features for localized and parallelized thin-plate spline interpolation in order to achieve good compressibility, interactive expressibility, and editability. The proposed system then automatically extracts an optimal set of detailed color features while considering the compression ratio of the image as well as reconstruction error and its characteristics applicable to the preservation of structural and irregular saliency of the image. The proposed real-time vector representation makes it possible to construct an interactive editing system for detail-maintained image magnification and color editing as well as material replacement in cross mapping, without maintaining spatial and temporal consistency while editing in a raster space. Experiments demonstrate that our representation method is superior to several state-of-the-art methods and as good as JPEG, while providing real-time editability and preserving structural and irregular saliency information.

Periodontal diagnosis requires discovery of the relations among teeth, gingiva (i.e., gums), and alveolar bones, but alveolar bones are inside gingiva and not visible for inspection. Traditional probe examination causes pain, and X-ray based examination is not suited for frequent inspection. This work develops an automatic non-invasive periodontal inspection framework based on gum penetrative Optical Coherence Tomography (OCT), which can be frequently applied without high radiation. We sum up interference responses of all penetration depths for all shooting directions respectively to form the shooting amplitude projection. Because the reaching interference strength decays exponentially with tissues&rsquo, penetration depth, this projection mainly reveals the responses of the top most gingiva or teeth. Since gingiva and teeth have different air-tissue responses, the gumline, revealing itself as an obvious boundary between teeth and gingiva, is the basis line for periodontal inspection. Our system can also automatically identify regions of gingiva, teeth, and alveolar bones from slices of the cross-sectional volume. Although deep networks can successfully and possibly segment noisy maps, reducing the number of manually labeled maps for training is critical for our framework. In order to enhance the effectiveness and efficiency of training and classification, we adjust Snake segmentation to consider neighboring slices in order to locate those regions possibly containing gingiva-teeth and gingiva&ndash, alveolar boundaries. Additionally, we also adapt a truncated direct logarithm based on the Snake-segmented region for intensity quantization to emphasize these boundaries for easier identification. Later, the alveolar-gingiva boundary point directly under the gumline is the desired alveolar sample, and we can measure the distance between the gumline and alveolar line for visualization and direct periodontal inspection. At the end, we experimentally verify our choice in intensity quantization and boundary identification against several other algorithms while applying the framework to locate gumline and alveolar line in vivo data successfully.

Digital dental reconstruction can be a more efficient and effective mechanism for artificial crown construction and period inspection. However, optical methods cannot reconstruct those portions under gums, and X-ray-based methods have high radiation to limit their applied frequency. Optical coherence tomography (OCT) can harmlessly penetrate gums using low-coherence infrared rays, and thus, this work designs an OCT-based framework for dental reconstruction using optical rectification, fast Fourier transform, volumetric boundary detection, and Poisson surface reconstruction to overcome noisy imaging. Additionally, in order to operate in a patient&rsquo, s mouth, the caliber of the injector is small along with its short penetration depth and effective operation range, and thus, reconstruction requires multiple scans from various directions along with proper alignment. However, flat regions, such as the mesial side of front teeth, may not have enough features for alignment. As a result, we design a scanning order for different types of teeth starting from an area of abundant features for easier alignment while using gyros to track scanned postures for better initial orientations. It is important to provide immediate feedback for each scan, and thus, we accelerate the entire signal processing, boundary detection, and point-cloud alignment using Graphics Processing Units (GPUs) while streamlining the data transfer and GPU computations. Finally, our framework can successfully reconstruct three isolated teeth and a side of one living tooth with comparable precisions against the state-of-art method. Moreover, a user study also verifies the effectiveness of our interactive feedback for efficient and fast clinic scanning.

Dynamic Chinese Painting is a rendering style for animations. However, the production method today which relies on the artists to generate suitable textures and sceneries is time-consuming. In this paper, we propose a system to generate a realistic appearance of Yangzhou School Chinese Painting koi (decorative carp) animation in 3D space. Our system includes repainting the original texture, enhancing the contour of the 3D input models, and adding fin-stripes to fit the target style. For the interactive part, our system not only allows users to add ripples to the scene as the user-scene interaction but also generates water streamline as the koi-scene interaction. Through our system, a new Dynamic Chinese Painting scene can be automatically generated based on the 3D input models and user-inputs.

A large dataset of outdoor panoramas with ground truth labels of sun position (SP) can be a valuable training data for learning outdoor illumination. In general, the sun position (if exists) in an outdoor panorama corresponds to the pixel with highest luminance and contrast with respect to neighbor pixels. However, both image-based estimation and manual annotation can not obtain reliable SP due to complex interplay between sun light and sky appearance. Here, we present an efficient and reliable approach to estimate a SP from an outdoor panorama with accessible metadata. Specifically, we focus on the outdoor panoramas retrieved from Google Street View and leverages built-in metadata as well as a well-established Solar Position Algorithm to propose a set of candidate SPs. Next, a custom made luminance model is used to rank each candidate and a confidence metric is computed to effectively filter out trivial cases (e.g., cloudy day, sun is occluded). We extensively evaluated the efficacy of our approach by conducting an experimental study on a dataset with over 600 panoramas.

Human factor causes most of the road accidents; for improving traffic safety, advanced driver assistance system and autonomous driving have become active research. The most significant challenge of these systems is the development of a reliable obstacle detection which can recognize pedestrians and pedestrian-like objects on the road from color images and thermal images. Consequently, the object detection based on neural networks, such as YOLO [1] , is emerging as a solution for identifying obstacles with various appearance. Although the convolutional neural network provides a robust and reliable result, it still involves substantial computational costs and hard to port to an embedded device. This work intends to improve the performance of YOLO and aims at presenting a real-time obstacle detection system on an integrated device.

It is a necessary but challenging task for creative producers to have an idea of the depth perception of the target audience when watching a stereoscopic film in a cinema during production. This paper proposes a novel metric, geometric perceived depth percentage (GPDP), to numerate and depict the depth perception of a scene before rendering. In addition to the geometric relationship between the object depth and focal distance, GPDP takes the screen width and viewing distance into account. As a result, it provides a more intuitive means for predicting stereoscopy and is universal across different viewing conditions. Based on GPDP, we design a practical tool to visualize the stereoscopic perception without the need for any 3-D device or special environment. The tool utilizes the stereoscopic comfort volume, GPDP-based shading schemes, depth perception markers, and GPDP histograms as visual cues so that animators can set stereoscopic parameters more easily. The tool is easily implemented in any modern rendering pipeline, including interactive Autodesk Maya and offline Pixar’s RenderMan renderer. It has been used in several production projects including commercial ones. Finally, two user studies show that GPDP is a proper depth perception indicator and the proposed tool can make the stereoscopic parameter setting process easier and more efficient.

Hemispherical integrals are important for the estimation of direct lighting which has a major impact on the results of global illumination. This work proposes the population Monte Carlo hemispherical integral (PMC-HI) sampler to improve the efficiency of direct lighting estimation. The sampler is unbiased and derived from the population Monte Carlo framework which works on a population of samples and learns to be a better sampling function over iterations. Information found in one iteration can be used to guide subsequent iterations by distributing more samples to important sampling techniques to focus more efforts on the sampling sub-domains which have larger contributions to the hemispherical integrals. In addition, a cone sampling strategy is also proposed to enhance the success rate when complex occlusions exist. The images rendered with PMC-HI are compared against those rendered with multiple importance sampling (Veach and Guibas In: SIGGRAPH '95, pp 419---428, 1995), adaptive light sample distributions (Donikian et al. IEEE Trans Vis Comput Graph 12(3):353---364, 2006), and multidimensional hemispherical adaptive sampling (Hachisuka et al. ACM Trans Graph 27(3):33:1---33:10, 2008). Our PMC-HI sampler can improve rendering efficiency.

Introducing motion into existing static paintings is becoming a field that is gaining momentum. This effort facilitates keeping artworks current and translating them to different forms for diverse audiences. Chinese ink paintings and Japanese Sumies are well recognized in Western cultures, yet not easily practiced due to the years of training required. We are motivated to develop an interactive system for artists, non-artists, Asians, and non-Asians to enjoy the unique style of Chinese paintings. In this paper, our focus is on replacing static water flow scenes with animations. We include flow patterns, surface ripples, and water wakes which are challenging not only artistically but also algorithmically. We develop a data-driven system that procedurally computes a flow field based on stroke properties extracted from the painting, and animate water flows artistically and stylishly. Technically, our system first extracts water-flow-portraying strokes using their locations, oscillation frequencies, brush patterns, and ink densities. We construct an initial flow pattern by analyzing stroke structures, ink dispersion densities, and placement densities. We cluster extracted strokes as stroke pattern groups to further convey the spirit of the original painting. Then, the system automatically computes a flow field according to the initial flow patterns, water boundaries, and flow obstacles. Finally, our system dynamically generates and animates extracted stroke pattern groups with the constructed field for controllable smoothness and temporal coherence. The users can interactively place the extracted stroke patterns through our adapted Poisson-based composition onto other paintings for water flow animation. In conclusion, our system can visually transform a static Chinese painting to an interactive walk-through with seamless and vivid stroke-based flow animations in its original dynamic spirits without flickering artifacts.

Although 3D printing is becoming more popular, but there are two major problem. The first is the slowness of the process because of requirement of processing information of an extra axis comparing to tradition 2D printers. The second is the printable dimension of 3D printers. Generally, the larger the model is printed, the larger a 3D printer has to be and the more expensive it is. Furthermore, it would also require a large amount of extra inflation materials. With the entrance of cheap 3D printers, such as OLO 3D printers [Inc. 2016], parallel printing with multiple cheap printers can possibly be the solution. In order to parallel print a 3D model, we must decompose a 3D model into smaller components. After printing out all the components, we assemble them together by attaching them to the skeleton through supporters and joints to form the final result. As shown in our results, our designed shell-and-bone-based model printing can not only save the printing time but also use lesser material than the original whole model printing.

In this paper, the orthogonal frequency-division multiplexing signal without inserting guard subintervals (NG-OFDM) is considered and combined with spectral precoding to achieve high spectrum compactness. In order to reject interblock interference among received NG-OFDM blocks over dispersive channels, a guard subinterval in each received block is removed at the receiver and this turns the resultant receiver to be nonorthogonal over subcarrier subchannels and thus subject to artificial intercarrier interference. It is shown that the proposed spectrally precoded NG-OFDM system can provide channel capacity very close to nonprecoded and spectrally precoded OFDM using cyclic prefix (CP-OFDM) when the removal guard ratio is small, and much higher channel capacity per unit bandwidth than spectrally precoded CP- OFDM conveying the same data rate provided that a small out-of-band power proportion is required.

The researchers have used an ordinary digital camera or video camera to capture and recognize barcode. However, from the published literatures, we found that almost all of the studies recognized the barcode by reading the “bar” part only and leaving the “code” part unprocessed. When the barcode is stained or broken, it cannot be fully recognized. Therefore, we develop a new approach for recognizing barcode using digital camera, in which the “bar” part and “code” part are both recognizable. Moreover, the system is integrated with database to further enhance the recognition rate. The system is implemented on an embedded platform.

A sensor system is developed in the paper to measure the position and orientation of planar robots. In the sensor system, a monocular vision is integrated with a detection method for abstracting the scale- and orientation-invariant image features. Instead of using multiple cameras, a monocular vision is utilized as the only sensing device to reduce the computation cost. The scale- and orientation-invariant method is employed to guarantee a robust detection and description of features abstracted from an image. Experiment is carried out on a free-moving monocular camera to verify the performances of the proposed system.

Orthogonal frequency-division multiplexing (OFDM) signaling format is commonly associated with guard subintervals to reject interblock interference (IBI) and enable simple one-tap frequency-domain equalization (FDE) at the receiver when received over dispersive channels. Despite the advantages, the insertion of guard subintervals expands the signal spectrum and thus trades off the spectral efficiency significantly. In this paper, the nonguarded OFDM (NG-OFDM) signaling format, which is exactly OFDM without inserting guard subintervals, is considered to avoid the spectrum expansion and combined with spectral precoding to further achieve high spectrum compactness. In particular, spectral precoders are designed for NG-OFDM to provide very small spectral sidelobes, and thus extremely compact signal spectrum, and associate the transmitted waveform with low peak-to-average power ratio. To reject IBI among received NG-OFDM blocks over dispersive channels, a guard subinterval in each received block is removed at the receiver, and this turns the resultant receiver as nonorthogonal over subcarrier subchannels, thus resulting in intercarrier interference (ICI). The corresponding channel capacity and linear minimum-mean-square-error (LMMSE) data estimation are analyzed. It is shown that the proposed spectrally precoded NG-OFDM (SP-NG-OFDM) system can provide channel capacity very close to nonprecoded and spectrally precoded OFDM using the cyclic prefix (CP-OFDM) when the removal guard ratio is small, and much higher channel capacity per unit bandwidth than spectrally precoded CP-OFDM conveying the same data rate provided that a small out-of-band power proportion is required. When a sufficiently small precoding rate is adopted, the SP-NG-OFDM receiver, using guard removal and LMMSE-FDE, is also shown to counteract ICI effectively and exploit frequency diversity over dispersive channels.

This paper is concerned with minimizing the delay due to data communication during the execution of a parallel algorithm on an SIMD computer with a two-way circular unit-shift interconnection network. Algorithms are developed which determine, for a given parallel procedure, the order of computation within that procedure, for every parallel arithmetic expression, the alignment of operands for every binary operation, and the mapping and remapping of data into physical memories so that the communication cost is minimized. The proposed algorithms are applicable to array variables with special types of index functions.