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Co-occurring rational disability along with compound utilize issues.
Furthermore, to improve the evaluation accuracy of the column with a large receptive field, we propose a transform dilated convolution. The transform dilated convolution breaks the fixed sampling structure of the deep network. BTK inhibitor Moreover, it needs no extra parameters and training, and the offsets are constrained in a local region, which is designed for the congested scenes. The proposed method achieves state-of-the-art performance on five datasets (ShanghaiTech, UCF CC 50, WorldEXPO'10, UCSD, and TRANCOS).Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.General image super-resolution techniques have difficulties in recovering detailed face structures when applying to low resolution face images. Recent deep learning based methods tailored for face images have achieved improved performance by jointly trained with additional task such as face parsing and landmark prediction. However, multi-task learning requires extra manually labeled data. Besides, most of the existing works can only generate relatively low resolution face images (e.g., 128×128 ), and their applications are therefore limited. In this paper, we introduce a novel SPatial Attention Residual Network (SPARNet) built on our newly proposed Face Attention Units (FAUs) for face super-resolution. Specifically, we introduce a spatial attention mechanism to the vanilla residual blocks. This enables the convolutional layers to adaptively bootstrap features related to the key face structures and pay less attention to those less feature-rich regions. This makes the training more effective and efficient as the key face structures only account for a very small portion of the face image. Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e.g., 16×16 ). Quantitative comparisons on various kinds of metrics (including PSNR, SSIM, identity similarity, and landmark detection) demonstrate the superiority of our method over current state-of-the-arts. We further extend SPARNet with multi-scale discriminators, named as SPARNetHD, to produce high resolution results (i.e., 512×512 ). We show that SPARNetHD trained with synthetic data can not only produce high quality and high resolution outputs for synthetically degraded face images, but also show good generalization ability to real world low quality face images. Codes are available at https//github.com/chaofengc/Face-SPARNet.Separable nonlinear least squares (SNLLS) problems have attracted interest in a wide range of research fields such as machine learning, computer vision, and signal processing. During the past few decades, several algorithms, including the joint optimization algorithm, alternated least squares (ALS) algorithm, embedded point iterations (EPI) algorithm, and variable projection (VP) algorithms, have been employed for solving SNLLS problems in the literature. The VP approach has been proven to be quite valuable for SNLLS problems and the EPI method has been successful in solving many computer vision tasks. However, no clear explanations about the intrinsic relationships of these algorithms have been provided in the literature. In this paper, we give some insights into these algorithms for SNLLS problems. We derive the relationships among different forms of the VP algorithms, EPI algorithm and ALS algorithm. In addition, the convergence and robustness of some algorithms are investigated. Moreover, the analysis of the VP algorithm generates a negative answer to Kaufman's conjecture. Numerical experiments on the image restoration task, fitting the time series data using the radial basis function network based autoregressive (RBF-AR) model, and bundle adjustment are given to compare the performance of different algorithms.The impact of Pb on the environment and human health and recent restrictions on its use in electronic devices are generating demand for Pb-free piezoelectric materials. Examples are now available commercially, but the full elastic-piezoelectric-dielectric (EPD) matrices needed for device design, including over a range of operating conditions, have not yet been published. The standard IEEE EPD matrix measurement method needs four sample geometries, making it inconvenient and increasing errors. Here, we present an alternative method combining resonant ultrasound spectroscopy with optimization algorithms to measure the EPD matrix from a single exact cube sample. The Levenberg-Marquardt (LM) and Nelder-Mead (NM) optimizations are compared in refining the independent parameters. Both give convergent solutions, but the LM algorithm is more accurate and efficient. The single-sample approach was used to obtain results from Pb-free Na1/2Bi1/2TiO3 (PIC 700, PI Ceramics, Lederhose, Germany) piezoceramic ( ∞ mm sample symmetry) characterized with the standard IEEE method at ambient temperature and with the single-sample method at ambient temperature and additionally up to 80 °C. The results are validated with the laser Doppler vibrometry via mode shape reconstruction and comparison with finite-element analysis (FEA). They demonstrate that convenient measurement of the EPD matrix of Pb-free materials with temperature dependence is possible, providing a crucial capability for the adoption of these materials in devices.
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