View Synthesis from Image and Video for Object Recognition Applications

Object recognition is one of the most important and successful applications in computer vision community. The varying appearances of the test object due to different poses or illumination conditions can make the object recognition problem very challenging. Using view synthesis techniques to generate pose-invariant or illumination-invariant images or videos of the test object is an appealing approach to alleviate the degrading recognition performance due to non-canonical views or lighting conditions. In this thesis, we first present a complete framework for better synthesis and understanding of the human pose from a limited number of available silhouette images. Pose-normalized silhouette images are generated using an active virtual camera and an image based visual hull technique, with the silhouette turning function distance being used as the pose similarity measurement. In order to overcome the inability of the shape from silhouettes method to reonstruct concave regions for human postures, a view synthesis algorithm is proposed for articulating humans using visual hull and contour-based body part segmentation. These two components improve each other for better performance through the correspondence across viewpoints built via the inner distance shape context measurement. Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. We propose two algorithms to address this scenario. For a single light source, we demonstrate a pose-normalized face synthesis approach on a pixel-by-pixel basis from a single view by exploiting the bilateral symmetry of the human face. For more complicated illumination condition, the spherical harmonic representation is extended to encode pose information. An efficient method is proposed for robust face synthesis and recognition with a very compact training set. Finally, we present an end-to-end moving object verification system for airborne video, wherein a homography based view synthesis algorithm is used to simultaneously handle the object's changes in aspect angle, depression angle, and resolution. Efficient integration of spatial and temporal model matching assures the robustness of the verification step. As a byproduct, a robust two camera tracking method using homography is also proposed and demonstrated using challenging surveillance video sequences

Parameter Optimization for Image Denoising Based on Block Matching and 3D Collaborative Filtering

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation

POSE-NORMALIZED VIEW SYNTHESIS OF A SYMMETRIC OBJECT USING A SINGLE IMAGE

Object recognition under varying pose is a challenging problem, especially when illumination variations are also present. In this paper, we propose a pose-normalized view synthesis method under varying illuminations for symmetric objects. For a given non-frontal view of a symmetric object under non-frontal illumination, the mirror image of the original view is equivalent to the view when the object rotates around the Y-axis by the same angle as the original view but in the opposite direction, and under opposite illumination condition in the X direction. Exploiting the bilateral symmetry of the object, we generate the mirror view of the object under the same illumination condition as the original view on a pixel-by-pixel basis. The frontal view under the same illumination is then easily obtained using view morphing techniques. 1

Pose-Encoded Spherical Harmonics for Face Recognition and Synthesis Using a Single Image

Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. In this paper, we propose to address one of the most challenging scenarios in face recognition. That is, to identify a subject from a test image that is acquired under different pose and illumination condition from only one training sample (also known as a gallery image) of this subject in the database. For example, the test image could be semifrontal and illuminated by multiple lighting sources while the corresponding training image is frontal under a single lighting source. Under the assumption of Lambertian reflectance, the spherical harmonics representation has proved to be effective in modeling illumination variations for a fixed pose. In this paper, we extend the spherical harmonics representation to encode pose information. More specifically, we utilize the fact that 2D harmonic basis images at different poses are related by close-form linear transformations, and give a more convenient transformation matrix to be directly used for basis images. An immediate application is that we can easily synthesize a different view of a subject under arbitrary lighting conditions by changing the coefficients of the spherical harmonics representation. A more important result is an efficient face recognition method, based on the orthonormality of the linear transformations, for solving the above-mentioned challenging scenario. Thus, we directly project a nonfrontal view test image onto the space of frontal view harmonic basis images. The impact of some empirical factors due to the projection is embedded in a sparse warping matrix; for most cases, we show that the recognition performance does not deteriorate after warping the test image to the frontal view. Very good recognition results are obtained using this method for both synthetic and challenging real images

The population structural transition effect on rising per capita CO₂ emissions:evidence from China

The per capita CO2 emissions (PCCE) of many developing countries like China have been rising faster than total CO2 emissions, and display spatial divergence. Such temporal growth and spatial divergence will have a significant influence on efforts to mitigate CO2 emissions. Given the research gap on the impact of the structural transition in population on PCCE, we constructed an econometric model using the dynamic panel method. The results reveal that the population structural transition has a significant nonlinear impact on PCCE, as the rate of population growth in China decelerates. Both demographic ageing and urban-rural migration have a stronger impact on PCCE than other factors. This effect, however, decreases beyond a certain threshold. An increase in the number of households due to urbanization and family downsizing has resulted in a positive effect on PCCE, without a threshold turning point. The research also finds that an increased share of the service sector in employment can reduce PCCE only if the sector employs more than 31.56% of the total employed population. Overall, these findings indicate that policymakers should pay attention to the prominence of the demographic structural transition for effective climate policy. Key policy insights Policymakers should address rising per capita carbon emissions (PCCE) and their spatial divergence in future climate policies, not just total CO2 emissions. The transitioning demographics of ageing and urbanization in China show a nonlinear, inverted U-shaped effect on PCCE instead of a continuously positive effect. Based on the nonlinear effect of employment structure on PCCE, policymakers should focus on the relationship between the structural transition of the economy and PCCE in future climate mitigation policies