How the different levels of government and the local population react to the impact and pressure of environmental degradation and management of the water resources?

With the explosion in the number of digital images taken every day, people are demanding more accurate and visually pleasing images. However, the captured images by modern cameras are inevitably degraded by noise. Besides deteriorating image visual quality, noise also degrades the performance of high-level vision tasks such as object recognition and tracking. Therefore, image denoising is a critical preprocessing step. This thesis presents novel contributions to the field of image denoising.

Image denoising is a highly ill-posed inverse problem. To alleviate the ill-posedness, an effective prior plays an important role and is a key factor for successful image denoising. With abundance of images available online, we propose to obtain priors from external image databases. In this thesis, we perform statistical analyses and rigorous derivations on how to obtain effective priors by utilizing external databases. For three denoising applications under different external settings, we show how we can explore effective priors and accordingly we present adaptive patch-based image denoising algorithms. In specific, we propose three adaptive algorithms: (1) adaptive non-local means for multiview image denoising; (2) adaptive image denoising by targeted databases; (3) adaptive image denoising by mixture adaption.

In (1), we present how to improve the non-local prior by finding more relevant patches in the multiview image denoising setting. We propose a method that uses a robust joint-view distance metric to measure the similarity of patches and derive an adaptive procedure to determine the optimal number of patches for final non-local means denoising. In (2), we propose to switch from generic database to targeted database, i.e., for specific objects to be denoised, only targeted databases with relevant images should be used. We explore both the group sparsity prior and the localized Bayesian prior, and show how a near optimal and adaptive denoising filter can be designed so that the targeted database can be maximally utilized. In (3), we propose an adaptive learning procedure called Expectation-Maximization (EM) adaptation. The adaptive process takes a generic prior learned from a generic database and transfers it to the image of interest to create a specific prior. This adapted prior better captures the distribution of the image of interest and is consistently better than the un-adapted one. For all the three denoising applications, we conduct various denoising experiments. Our proposed adaptive algorithms have some superior denoising performance than some state-of-the-art algorithms.

Enabling machines to understand non-verbal facial behavior from visual data is crucial for building smart interactive systems. This thesis focusses on human behavior analysis in videos. Previous state-of-the-art methods generally employed global temporal pooling approaches that, (i) assume presence of a single uniform event spanning the sequence, and (ii) discard temporal ordering by squashing all information along the temporal dimension. In this dissertation we focus on two specific modeling challenges unaddressed by previous approaches. First issue is training with weak labels that only provide video-level annotations and are much cheaper to obtain than fine (frame-level) annotations. The second concerns modeling temporal dynamics during prediction, as facial expressions are dynamic actions with sub-events. We propose to tackle these issues by proposing methods based on Weakly Supervised Latent Variable Models (WSLVM) and evaluate them on real-world spontaneous expressions.

We begin with addressing these challenges by combining Multiple Instance Learning (MIL) framework and Multiple Segment representation (MS-MIL). MS-MIL can simultaneously classify and localize target behavior in videos despite training with weak annotations. However, this method lacks the capability to explicitly model multiple latent concepts or global temporal order. We address this issue in the next chapter by explicitly modeling temporal orderings by learning an exemplar Hidden Markov Model for each sequence. This algorithm models dependencies between segments but is limited in its modeling capacity due to the use of generative modeling. Chapter~4 extends MIL to learn multiple discriminative concepts in a novel formulation for joint clustering and classification. This algorithm shows consistent performance improvement but does not capture temporal structure.

We finally present a unified learning framework that combines the strengths of the previously proposed algorithms in that it (i) addresses weakly labeled data (ii) learns multiple discriminative concepts, and (iii) models the temporal ordering structure of the concepts. This method is a novel WSLVM that models a video as a sequence of automatically mined, multiple discriminative sub-events with a loose temporal structure. We show both qualitative and quantitative results highlighting improvements over state-of-the-art algorithms by jointly addressing weak labels and temporal dynamics.

Digital humans play the leading roles in every aspect of the virtual world. In recent years, multi-view reconstruction is emerging as a cutting-edge technique to generate human models. It reveals the potential to restore intricate surfaces for a wide range of applications requiring more than minimalist avatars. Even with many promising results, the related systems are still cumbersome with many requirements on devices and the environment. The capture often has to be performed in a large-scale studio with dozens of HD camera rigs in order to preserve the surface details as well as mitigate artifacts introduced by occlusion.

Our research aims to simplify the multi-view system and bring it to wider application scenarios. This goal requires a significant upgrade to the state-of-the-art algorithms in order to address gap-region surface and large-displacement registration challenges introduced by reducing input coverage and curbing computing capability. We tackle these challenges by leveraging learning-based prior knowledge on 3D human geometries as well as a more robust deformation framework.

Firstly, we explore learning-based 3D reasoning methods. The current end-to-end framework shows the capability to generate arbitrary 3D shapes with even monocular inputs. We propose a novel back-propagation method for differentiable rasterizer that improves the accuracy and decreases the complexity significantly.

With the foundation of reliable surface inference, in the next step, we investigate completing the gap-region surface. We realize this purpose in an innovative way that transfers the time-consuming 3D procedures into a simple 2D image-domain processing. It can work compatibly with the traditional multi-view depth fusion method.

After that, given a complete key model, we work on fusing the surface information from other frames in the sequence to further enhance the details. Based on the state-of-the-art method, we boost the robustness of the embedded deformation in the large-displacement cases when the models have the significant pose and appearance differences. We achieve this with a novel adaptive regularization and an efficient two-step optimization.

Finally, we design a new framework specifically for human head reconstruction. Instead of sculpturing the complicated geometries of faces, we focus on generating HD textures and fusing input sequences with a geometry-aware texture stitching framework. The results preserve high resolutions details and can be executed in real-time.

Introduction: Chest pain is one of the most common causes of emergency department visits on an annual basis and carries a high degree of morbidity and mortality if managed inappropriately.

Case Report: A 36-year-old male presented with four months of left-sided chest pain with dyspnea on exertion. Physical examination and laboratory values were within normal limits. Chest radiograph depicted diffuse interstitial nodular opacities throughout the lungs bilaterally with bilateral perihilar consolidations. Computed tomography of the chest demonstrated mid and upper lung nodularity with a perilymphatic distribution involving the central peribronchial vascular regions as well as subpleural and fissural surfaces causing conglomerate in the upper lobes centrally with associated hilar and mediastinal lymphadenopathy. The next day the patient underwent bronchoscopy with endotracheal ultrasound and transbronchial biopsies and pathology revealed non-necrotizing, well-formed granulomas embedded in dense hyaline sclerosis consistent with sarcoidosis.

Discussion: Sarcoidosis is a multi-system granulomatous disease characterized by noncaseating granulomas on pathology. The worldwide epidemiology of sarcoidosis is currently unknown due to many patients being asymptomatic. However, patients may present with a persistent cough, dyspnea, or chest pain. Emergency department management should be aimed at minimizing long-term sequelae of the disease through obtaining labs and imaging after specialist consultation and arranging urgent follow-up.

Conclusion: Although not one of the six high-risk causes of chest pain, sarcoidosis should be included in the differential to minimize the risk of long-term morbidity associated with advanced forms of the disease.