A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

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Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling.

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.

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Light Field Image Processing: An Overview

numerical methods for partial differential equations is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the numerical methods for partial differential equations is universally compatible with any devices to read.

Numerical Methods For Partial Differential Equations

The scale-invariant feature transform algorithm and its many variants are widely used in feature-based remote sensing image registration. However, it may be difficult to find enough correct correspondences for remote image pairs in some cases that exhibit a significant difference in intensity mapping. In this letter, a new gradient definition is introduced to overcome the difference of image intensity between the remote image pairs. Then, an enhanced feature matching method by combining the position, scale, and orientation of each keypoint is introduced to increase the number of correct correspondences. The proposed algorithm is tested on multispectral and multisensor remote sensing images. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the number of correct correspondences and aligning accuracy.

Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching

The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.

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Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Fast stereo matching using adaptive guided filtering

With the development of 3DTV, the conversion of existing 2D videos to 3D videos becomes an important component of 3D content production. One of the key steps in 2D to 3D conversion is how to generate a dense depth map. In this paper, we propose a novel depth extraction method based on motion and geometric information for 2D to 3D conversion, which consists of two major depth extraction modules, the depth from motion and depth from geometrical perspective. The H.264 motion estimation result is utilized and cooperates with moving object detection to diminish block effect and generates a motion-based depth map. On the other hand, a geometry-based depth map is generated by edge detection and Hough transform. Finally, the motion-based depth map and the geometry-based depth map are integrated into one depth map by a depth fusion algorithm.

A Depth Extraction Method Based on Motion and Geometry for 2D to 3D Conversion

Motion estimation (ME) is the most critical component of a video coding system, and it also dominates the major part of computation complexity and memory bandwidth. For H.264/AVC integer motion estimation (IME), this paper presents a novel memory-access and computation efficient full-search block-matching hardware architecture. With the highest level of on-chip data reuse, one-access for off-chip reference pixels is achieved, and the off-chip memory bandwidth is thus minimized. By distributed data caching and virtual connection of reference picture boundaries, the data traffic scheduling is simple, regular and efficient. The computation engine employs a two-dimensional (2-D) systolic processor array to calculate the absolute differences in single-instruction multiple-data (SIMD) manner, and 2-D adder trees to sum up the absolute differences, all with 100% utilization. The proposed architecture fully supports variable block-size matching of H.264/AVC, and can produce 41 sums of absolute differences (SADs) for one search point every cycle without bubble. The architecture is described in parameterized design, and an implementation for standard-definition digital TV encoding applications is presented. Theoretical analysis and experimental results show that, the proposed architecture can achieve the minimum off-chip memory bandwidth and the maximum computational performance.

Architecture Design for H.264/AVC Integer Motion Estimation with Minimum Memory Bandwidth

A novel full-image guided filtering method is proposed. Different with many existing neighborhood filters, all input elements are employed during the proposed filtering approach. In addition, a novel scheme called weight propagation is proposed to compute support weights. It fulfills the requirements of edge preserving and low complexity. It is applied to the cost-volume filtering in the local stereo matching framework. The algorithm utilizing the proposed filtering method is currently one of the best local algorithms on the Middlebury stereo testbed in terms of both speed and accuracy.

Dongxiao Li

Papers

Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Fast stereo matching using adaptive guided filtering

A Depth Extraction Method Based on Motion and Geometry for 2D to 3D Conversion

Architecture Design for H.264/AVC Integer Motion Estimation with Minimum Memory Bandwidth

Full-Image Guided Filtering for Fast Stereo Matching