SUMMARYAn adaptive finite element procedure is developed for modelling transient phenomena in elastic solids, including both wave propagation and structural dynamics. Although both temporal and spatial adaptivity are addressed, the novel feature of the formulation is the use of mesh superposition to produce spatial refinement (referred to as s-adaptivity) in transient problems. Spatial error estimation is based on superconvergent patch recovery of higher-order accurate stresses and is used to guide mesh adaptivity, while the temporal error estimation is based on the assumption of linearly varying third-order time derivatives of the displacement field and is used to adjust the time step size for the HHT-variant of the Newmark direct numerical integration method. Spatial adaptivity of the mesh is performed using a hierarchical h-refinement scheme that is efficiently implemented using a structured version of finite element mesh superposition. This particular spatial adaptivity scheme is extremely fast and consequently makes it feasible to repeatedly update both the mesh and the time increment as required in an adaptive transient analysis. This work represents the initial effort in applying this type of spatial adaptivity to transient problems. Three example problems are given to demonstrate the performance characteristics of the s-adaptive procedure.
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.
SUMMARYAn s-adaptive finite element procedure is developed for the transient analysis of 2-D solid mechanics problems with material non-linearity due to progressive damage. The resulting adaptive method simultaneously estimates and controls both the spatial error and temporal error within user-specified tolerances. The spatial error is quantified by the Zienkiewicz-Zhu error estimator and computed via superconvergent patch recovery, while the estimation of temporal error is based on the assumption of a linearly varying third-order time derivatives of the displacement field in conjunction with direct numerical time integration. The distinguishing characteristic of the s-adaptive procedure is the use of finite element mesh superposition (s-refinement) to provide spatial adaptivity. Mesh superposition proves to be particularly advantageous in computationally demanding non-linear transient problems since it is faster, simpler and more efficient than traditional h-refinement schemes. Numerical examples are provided to demonstrate the performance characteristics of the s-adaptive method for quasi-static and transient problems with material non-linearity.
Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. Under Lambertian model, spherical harmonics representation has proved to be effective in modelling illumination variations for a given pose. In this paper, we extend the spherical harmonics representation to encode pose information. More specifically, we show that 2D harmonic basis images at different poses are related by close-form linear combinations. This enables an analytic method for generating new basis images at a different pose which are typically required to handle illumination variations at that particular pose. Furthermore, the orthonormality of the linear combinations is utilized to propose an efficient method for robust face recognition where only one set of front-view basis images per subject is stored. In the method, we directly project a rotated testing image onto the space of front-view basis images after establishing the image correspondence. Very good recognition results have been demonstrated using this method.
In surveillance, reconnaissance and numerous other video applications, enhancing the resolution and quality enhances the usability of captured video. In many such applications, video is often acquired from low cost legacy sensors that offer low resolution due to modest optics and low-resolution arrays, providing imagery that may be grainy and missing important details -and still face transmission bottlenecks. Many post-processing techniques have been proposed to enhance the quality of the video and superresolution is one such technique. In this paper, we extend previous work on a real-time superresolution application implemented in ASIC/FPGA hardware. A gradient based technique is used to register the frames at the sub-pixel level. Once we get the high resolution grid, we use an improved regularization technique in which the image is iteratively modified by applying back-projection to get a sharp and undistorted image. The matlab/simulink proven algorithm was migrated to hardware, to achieve 320x240 -> 1280x960, at more than 38 fps, a stunning superresolution by 16X in total pixels. This significant advance beyond real-time is the main contribution of this paper. Additionally the algorithm is implemented in C to achieve real-time performance in software with optimization for Intel I7 processor. Fixed 32 bit processing structure is used to achieve easy migration across platforms. This gives us a fine balance between the quality and performance. The proposed system is robust and highly efficient. Superresolution greatly decreases camera jitter to deliver a smooth, stabilized, high quality video.
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