The evaluation of clinical status and vital signs is a crucial component remote medical care. Radar provides non-contact monitoring measurement without consideration lighting or privacy. However, the multipath effect generated by multiple people gathered in small indoor space seriously affects detection sign information. This study presents based on stepped-frequency continuous wave ultra-wideband(SFCW-UWB) impulse radio ultra-wideband(IR-UWB) radars. Additionally, this suggests Spectral...

Face recognition has achieved remarkable success owing to the development of deep learning. However, most existing face models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution training samples between profile faces and near-frontal faces. Additionally, self-occlusion nonlinear warping facial textures large variations also increase difficulty in learning discriminative features In this study, we propose a...

Face frontalization is the process of converting a face image under arbitrary pose to an with frontal pose. Benefited from significant improvement generative adversarial networks (GAN), models can use overcome problem model degradation owing variation head in recognition. Existing GAN based generate synthesis same identity as input, while those are hard capture geometry structure or facial patterns via pixel-wise constraint, e.g. contour. In this paper, we propose Geometry Structure...

Face recognition systems can be tricked by photos or videos with virtual faces. It is crucial for a safe face system to distinguish genuine user's faces (i.e., the first captured images of real scene) and spoof recaptured photographs videos). Existing liveness methods often use single image feature address spoofing problems, which are not reliable robust. In this paper, we analyze differences between images, propose extract three types features, i.e., specular reflection ratio, Hue channel...

This paper proposes a multilevel Green's function interpolation method (MLGFIM) to solve electromagnetic scattering from objects comprising both conductor and bi-isotropic using volume/surface integral equation (VSIE).Based on equivalence principle, the volume (VIE) in terms of electric magnetic flux densities surface (SIE) current density are first formulated for inhomogeneous conducting objects, respectively, then discretized moments (MoM).The MLGFIM is adopted speed up iterative solution...

The accurate segmentation of brain tissue in Magnetic Resonance Image (MRI) slices is essential for assessing neurological conditions and diseases. However, it challenging to segment MRI because the low contrast between different tissues partial volume effect. 2-Dimensional (2-D) convolutional networks cannot handle such volumetric image data well they overlook spatial information slices. Although 3-Dimensional (3-D) convolutions capture information, have not been fully exploited enhance...

Multi-focus image fusion (MFIF) creates an from different source images with various sensors or optical settings as the devices can't focus all objects at distances. Most of MFIF methods have several limitations in encoder enough features and result are not robust. To overcome primary issue, we present a robust algorithm based on Frequency mask Hyperdimensional computing. We propose Mask Filter (FMF) to get narrow-band signals by encoding frequency domain vector through filter domain. The...

Face normalization from large pose is a challenging problem. Many Generative Adversarial Network (GAN) based models can infer frontal view of profile faces, while they require paired faces and label. Instead, we focus on face synthesis with unpaired unlabeled training data. We present Frontal View Reconstruction GAN (FVR-GAN) for recognition. The generator FVR-GAN be considered as dual-input auto-encoder, where the identity encoder extracts features an image template contour image. decoder...

As an important building block in automatic medical applications, image segmentation has made a great progress due to the data-driving mechanism of deep architecture. Recently, numerous methods have been proposed boost performance based on U-shape network. However, they often built feature encoders with only one data routine, which limited representation ability networks. Although some applied multiple learning paths fix this problem, supervision techniques are required monitor training...

With the development of technology, using radar for gesture recognition is feasible and valuable. However, ensuring that can be applied to a wide range scenarios with sufficient accuracy still challenging. Due lack efficiency traditional methods, we propose scheme based on deep learning. We converted signals into pictures designed lightweight network called self-reparameterization distance velocity aware binary coding(SR-DVBNet) match them. use Self-reparameterization Encoder signal as...

Maximum a posteriori (MAP)-based single-image blind motion deblurring methods are extensively studied in the past years, and have achieved great progress. However, because of imperfect salient edges selection, most state-of-the-art still cannot estimate blur kernel (BK) accurately, especially large cases. In this paper, we propose novel spatial-scale-based approach to an accurate BK from single blurred image by combining spatial scale L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML"...

From a perspective of feature extraction, we present histogram-based sparsity descriptor (HSD) which is derived from the robust principal component analysis (RPCA) and histogram technique. Given test image, sparse error images with respect to each class can be obtained by using RPCA decomposition. In order extract facial features in terms intensity distribution, sparseness measure based on then introduced computing those images. By doing this, firstly choose t candidates similar face image....

To address the problem of non-well controlled face recognition, such as illumination changes, pose variation and random pixel corruption, we propose a robust recognition method based on representation feature extraction residual images. Represented by sparse linear regression, methods typically use training samples to represent reconstruct test samples, determine classification results according distance between reconstruction samples. In this paper, consider regression obtain sample with...

3D point cloud registration attempt to establish spatial correspondences between the source and target cloud. It is a fundamental task in computer vision multimedia applications. Recently, many learning-based methods have been proposed achieved promising performance. However, Partial-to-Partial (PtP) problem, existence of large number external points may greatly handicap effectiveness these methods. In this paper, we propose address PtP issue under novel multi-task cognition framework. At...

Feature representation is highly important for many computer vision tasks. A broad range of prior studies have been proposed to strengthen ability architectures via built-in blocks. However, during the forward propagation, reduction in feature map scales still leads lack ability. In this paper, we focus on boosting representational power a convolutional network by multi-branch framework that term BranchNet. Each branch directly supervised label information enrich hierarchy features Based...