創意媒體研究中，圖像/視頻再藝術作為有表現力的用戶定制外觀的創作手段受到了很大關注。交互設計中，特別是在圖像空間只有單張圖像或視頻輸入的情況下，運用計算機輔助設計虛擬地再渲染關注物體的風格化外觀來實現紋理替換是很強大的。現行的紋理替換往往通過操作圖像空間中像素的間距來處理紋理扭曲，原始圖像中潛在的紋理扭曲總是被破壞，因為現行的方法要麼存在由於手動網格拉伸導致的不恰當扭曲，要麼就由於紋理合成而導致不可避免的紋理開裂。圖像/視頻拼貼畫是被發明用以支持在顯示畫布上並行展示多個物體和活動。隨著數字視頻俘獲裝置的快速發展，相關的議題就是快速檢閱和摘要大量的視覺媒體數據集來找出關注的資料。這會是一項繁瑣的任務來審查長且乏味的監控視頻並快速把握重要信息。以關鍵信息和縮短視頻形式為交流媒介，視頻摘要是增強視覺數據集瀏覽效率和簡易理解的手段。 / 本文首先將圖像/視頻再藝術聚焦在高效紋理替換和風格化上。我們展示了一種交互紋理替換方法，能夠在不知潛在幾何結構和光照環境的情況下保持相似的紋理扭曲。我們運用SIFT 棱角特徵來自然地發現潛在紋理扭曲，並應用梯度深度圖復原和皺褶重要性優化來完成扭曲過程。我們運用GPU-CUDA 的並行性，通過實時雙邊網格和特徵導向的扭曲優化來促成交互紋理替換。我們運用基於塊的實時高精度TV-L¹光流，通過基於關鍵幀的紋理傳遞來完成視頻紋理替換。我們進一步研究了基於GPU 的風格化方法，並運用梯度優化保持原始圖像的精細結構。我們提出了一種能夠自然建模原始圖像精細結構的圖像結構圖，並運用基於梯度的切線生成和切線導向的形態學來構建這個結構圖。我們在GPU-CUDA 上通過並行雙邊網格和結構保持促成最終風格化。實驗中，我們的方法實時連續地展現了高質量的圖像/視頻的抽象再藝術。 / 當前，視頻拼貼畫大多創作靜態的基於關鍵幀的拼貼圖片，該結果只包含動態視頻有限的信息，會很大程度影響視覺數據集的理解。爲了便於瀏覽，我們展示了一種在顯示畫布上有效並行摘要動態活動的動態視頻拼貼畫。我們提出應用活動長方體來重組織及提取事件，執行視頻防抖來生成穩定的活動長方體，實行時空域優化來優化活動長方體在三維拼貼空間的位置。我們通過在GPU 上的事件相似性和移動關係優化來完成高效的動態拼貼畫，允許多視頻輸入。擁有再序核函數CUDA 處理，我們的視頻拼貼畫爲便捷瀏覽長視頻激活了動態摘要，節省大量存儲傳輸空間。實驗和調查表明我們的動態拼貼畫快捷有效，能被廣泛應用于視頻摘要。將來，我們會擴展交互紋理替換來支持更複雜的具大運動和遮蔽場景的一般視頻，避免紋理跳動。我們會採用最新視頻技術靈感使視頻紋理替換更加穩定。我們未來關於視頻拼貼畫的工作包括審查監控業中動態拼貼畫應用，並研究含有大量相機運動和不同種視頻過度的移動相機和一般視頻。 / Image/video restyling as an expressive way for producing usercustomized appearances has received much attention in creative media researches. In interactive design, it would be powerful to re-render the stylized presentation of interested objects virtually using computer-aided design tools for retexturing, especially in the image space with a single image or video as input. The nowaday retexturing methods mostly process texture distortion by inter-pixel distance manipulation in image space, the underlying texture distortion is always destroyed due to limitations like improper distortion caused by human mesh stretching, or unavoidable texture splitting caused by texture synthesis. Image/ video collage techniques are invented to allow parallel presenting of multiple objects and events on the display canvas. With the rapid development of digital video capture devices, the related issues are to quickly review and brief such large amount of visual media datasets to find out interested video materials. It will be a tedious task to investigate long boring surveillance videos and grasp the essential information quickly. By applying key information and shortened video forms as vehicles for communication, video abstraction and summary are the means to enhance the browsing efficiency and easy understanding of visual media datasets. / In this thesis, we first focused our image/video restyling work on efficient retexturing and stylization. We present an interactive retexturing that preserves similar texture distortion without knowing the underlying geometry and lighting environment. We utilized SIFT corner features to naturally discover the underlying texture distortion. The gradient depth recovery and wrinkle stress optimization are applied to accomplish the distortion process. We facilitate the interactive retexturing via real-time bilateral grids and feature-guided distortion optimization using GPU-CUDA parallelism. Video retexturing is achieved through a keyframe-based texture transferring strategy using accurate TV-L¹ optical flow with patch motion tracking techniques in real-time. Further, we work on GPU-based abstract stylization that preserves the fine structure in the original images using gradient optimization. We propose an image structure map to naturally distill the fine structure of the original images. Gradientbased tangent generation and tangent-guided morphology are applied to build the structure map. We facilitate the final stylization via parallel bilateral grids and structure-aware stylizing in real-time on GPU-CUDA. In the experiments, our proposed methods consistently demonstrate high quality performance of image/video abstract restyling in real-time. / Currently, in video abstraction, video collages are mostly produced with static keyfame-based collage pictures, which contain limited information of dynamic videos and in uence understanding of visual media datasets greatly. We present dynamic video collage that effectively summarizes condensed dynamic activities in parallel on the canvas for easy browsing. We propose to utilize activity cuboids to reorganize and extract dynamic objects for further collaging, and video stabilization is performed to generate stabilized activity cuboids. Spatial-temporal optimization is carried out to optimize the positions of activity cuboids in the 3D collage space. We facilitate the efficient dynamic collage via event similarity and moving relationship optimization on GPU allowing multi-video inputs. Our video collage approach with kernel reordering CUDA processing enables dynamic summaries for easy browsing of long videos, while saving huge memory space for storing and transmitting them. The experiments and user study have shown the efficiency and usefulness of our dynamic video collage, which can be widely applied for video briefing and summary applications. In the future, we will further extend the interactive retexturing to more complicated general video applications with large motion and occluded scene avoiding textures icking. We will also work on new approaches to make video retexturing more stable by inspiration from latest video processing techniques. Our future work for video collage includes investigating applications of dynamic collage into the surveillance industry, and working on moving camera and general videos, which may contain large amount of camera motions and different types of video shot transitions. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Li, Ping. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 109-121). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgements --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Main Contributions --- p.5 / Chapter 1.3 --- Thesis Overview --- p.7 / Chapter 2 --- Efficient Image/video Retexturing --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- Related Work --- p.11 / Chapter 2.3 --- Image/video Retexturing on GPU --- p.16 / Chapter 2.3.1 --- Wrinkle Stress Optimization --- p.19 / Chapter 2.3.2 --- Efficient Video Retexturing --- p.24 / Chapter 2.3.3 --- Interactive Parallel Retexturing --- p.29 / Chapter 2.4 --- Results and Discussion --- p.35 / Chapter 2.5 --- Chapter Summary --- p.41 / Chapter 3 --- Structure-Aware Image Stylization --- p.43 / Chapter 3.1 --- Introduction --- p.43 / Chapter 3.2 --- Related Work --- p.46 / Chapter 3.3 --- Structure-Aware Stylization --- p.50 / Chapter 3.3.1 --- Approach Overview --- p.50 / Chapter 3.3.2 --- Gradient-Based Tangent Generation --- p.52 / Chapter 3.3.3 --- Tangent-Guided Image Morphology --- p.54 / Chapter 3.3.4 --- Structure-Aware Optimization --- p.56 / Chapter 3.3.5 --- GPU-Accelerated Stylization --- p.58 / Chapter 3.4 --- Results and Discussion --- p.61 / Chapter 3.5 --- Chapter Summary --- p.66 / Chapter 4 --- Dynamic Video Collage --- p.67 / Chapter 4.1 --- Introduction --- p.67 / Chapter 4.2 --- Related Work --- p.70 / Chapter 4.3 --- Dynamic Video Collage on GPU --- p.74 / Chapter 4.3.1 --- Activity Cuboid Generation --- p.75 / Chapter 4.3.2 --- Spatial-Temporal Optimization --- p.80 / Chapter 4.3.3 --- GPU-Accelerated Parallel Collage --- p.86 / Chapter 4.4 --- Results and Discussion --- p.90 / Chapter 4.5 --- Chapter Summary --- p.100 / Chapter 5 --- Conclusion --- p.101 / Chapter 5.1 --- Research Summary --- p.101 / Chapter 5.2 --- Future Work --- p.104 / Chapter A --- Publication List --- p.107 / Bibliography --- p.109

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

Indiana University-Purdue University Indianapolis (IUPUI) / Current mainstream computing systems have become increasingly complex. Most of which have Central Processing Units (CPUs) that invoke multiple threads for their computing tasks. The growing issue with these systems is resource contention and with resource contention comes the risk of encountering a deadlock status in the system. Various software and hardware approaches exist that implement deadlock detection/avoidance techniques; however, they lack either the speed or problem size capability needed for real-time systems. The research conducted for this thesis aims to resolve issues present in past approaches by converging the two platforms (software and hardware) by means of the Graphics Processing Unit (GPU). Presented in this thesis are two GPU-based deadlock detection algorithms and one GPU-based deadlock avoidance algorithm. These GPU-based algorithms are: (i) GPU-OSDDA: A GPU-based Single Unit Resource Deadlock Detection Algorithm, (ii) GPU-LMDDA: A GPU-based Multi-Unit Resource Deadlock Detection Algorithm, and (iii) GPU-PBA: A GPU-based Deadlock Avoidance Algorithm. Both GPU-OSDDA and GPU-LMDDA utilize the Resource Allocation Graph (RAG) to represent resource allocation status in the system. However, the RAG is represented using integer-length bit-vectors. The advantages brought forth by this approach are plenty: (i) less memory required for algorithm matrices, (ii) 32 computations performed per instruction (in most cases), and (iii) allows our algorithms to handle large numbers of processes and resources. The deadlock detection algorithms also require minimal interaction with the CPU by implementing matrix storage and algorithm computations on the GPU, thus providing an interactive service type of behavior. As a result of this approach, both algorithms were able to achieve speedups over two orders of magnitude higher than their serial CPU implementations (3.17-317.42x for GPU-OSDDA and 37.17-812.50x for GPU-LMDDA). Lastly, GPU-PBA is the first parallel deadlock avoidance algorithm implemented on the GPU. While it does not achieve two orders of magnitude speedup over its CPU implementation, it does provide a platform for future deadlock avoidance research for the GPU.

Indiana University-Purdue University Indianapolis (IUPUI) / Adaptive-optics optical coherence tomography (AOOCT) is a technology that has been rapidly advancing in recent years and offers amazing capabilities in scanning the human eye in vivo. In order to bring the ultra-high resolution capabilities to clinical use, however, newer technology needs to be used in the image reconstruction process. General purpose computation on graphics processing units is one such way that this computationally intensive reconstruction can be performed in a desktop computer in real-time. This work shows the process of AOOCT image reconstruction, the basics of how to use NVIDIA's CUDA to write parallel code, and a new AOOCT image reconstruction technology implemented using NVIDIA's CUDA. The results of this work demonstrate that image reconstruction can be done in real-time with high accuracy using a GPU.

Indiana University-Purdue University Indianapolis (IUPUI) / Adaptive optics-optical coherence tomography (AO-OCT) is a non-invasive method of imaging the human retina in vivo. It can be used to visualize microscopic structures, making it incredibly useful for the early detection and diagnosis of retinal disease. The research group at Indiana University has a novel multi-camera AO-OCT system capable of 1 MHz acquisition rates. Until this point, a method has not existed to process data from such a novel system quickly and accurately enough on a CPU, a GPU, or one that can scale to multiple GPUs automatically in an efficient manner. This is a barrier to using a MHz AO-OCT system in a clinical environment. A novel approach to processing AO-OCT data from the unique multi-camera optics system is tested on multiple graphics processing units (GPUs) in parallel with one, two, and four camera combinations. The design and results demonstrate a scalable, reusable, extensible method of computing AO-OCT output. This approach can either achieve real time results with an AO-OCT system capable of 1 MHz acquisition rates or be scaled to a higher accuracy mode with a fast Fourier transform of 16,384 complex values.