Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart univer...
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ISBN:
(数字)9781665485104
ISBN:
(纸本)9781665485104
Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart university is to adopt technology to help with teaching and learning, especially during the Covid-19 pandemic. In this paper, a smart attendance system using face recognition algorithms with deep learning is proposed and used in the university's classroom. Instead of calling names to confirm the identity of students, our system does it automatically. The system was tested in 3 scenarios, namely, in online classes, in on-site classes, and in problematic cases using a standard dataset. The performances of the 3 scenarios were compared in the experiment in terms of precision, recall, F1 score, and percentage accuracy. Our result revealed that in online classes the recognition accuracy is as high as 100%. The implemented system is inexpensive and practical. The application can be used on any portable device such as tablets or smartphones. History viewing, multiple subjects handling, and file exporting features are also incorporated into the system.
Inspection in photovoltaic generation systems is of great importance for maintenance and corresponding generation efficiency. The combined use of unmanned aerial vehicles and deep learning computational algorithms can...
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High-frequency radio networks, including those operating in the millimeter-wave bands, are sensible to Line-of-Sight (LoS) obstructions. Computer Vision (CV) algorithms can be leveraged to improve network performance ...
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High Dynamic Range (HDR) imaging has become a significant technological advancement in visual data processing, allowing for the capture of a wider dynamic range of luminance levels in images. This paper explores vario...
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ISBN:
(数字)9798331529505
ISBN:
(纸本)9798331529512
High Dynamic Range (HDR) imaging has become a significant technological advancement in visual data processing, allowing for the capture of a wider dynamic range of luminance levels in images. This paper explores various HDR processing techniques and their potential applications in automation and machine vision. By using methods such as multiple image fusion, image registration, and tone mapping, the paper demonstrates how HDR processing can enhance visual data in automated systems, improving accuracy in environments requiring complex lighting conditions. This work applies HDR algorithms to real-world scenarios, showcasing their potential in industrial automation and robotics, where accurate visual data plays a crucial role.
The paper discusses objects (images) segmentation algorithms that are applicable in mobile applications with augmented reality. An example of imageprocessing with virtual objects by different algorithms (the MeanShif...
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The paper discusses objects (images) segmentation algorithms that are applicable in mobile applications with augmented reality. An example of imageprocessing with virtual objects by different algorithms (the MeanShift algorithm, the GrabCut algorithm, the k- means algorithm) is considered. Different libraries, tools, and environments to implement segmentation algorithms were analyzed, such as Scikit-image, Pixellib, OpenCV, Point Cloud Library. The application was created for mobile devices running iOS 10 and higher. The GrabCut algorithm turned out to be the best algorithm for imageprocessing. The processing result was the closest to the expected one. Although the algorithm has some errors. Despite the fact that the area that was contoured turned out to be the clearest and most complete in comparison with other algorithms, this area also includes areas of the image that do not belong to the objects under study.
image segmentation is critical to object-oriented imageprocessing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and ...
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ISBN:
(纸本)9781665464956
image segmentation is critical to object-oriented imageprocessing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both largescaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for oversegmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.
This paper introduces a novel approach to video prediction and object recognition based on treating image signals as dynamic system operators. We develop algorithms that extract invariant features from pixel patches t...
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ISBN:
(数字)9798350360868
ISBN:
(纸本)9798350360875
This paper introduces a novel approach to video prediction and object recognition based on treating image signals as dynamic system operators. We develop algorithms that extract invariant features from pixel patches to construct numerical matrices for image frame transitions. Our method diverges from conventional 2D signal processing by viewing images as operators rather than initial conditions. This perspective aligns more closely with biological visual systems and offers potential for efficient electronic implementations. We demonstrate the efficacy of our approach through experiments in mental rotation, affine transformations, and rotated MNIST digit recognition. Our results show that deformation invariance can be obtained without prior knowledge of the transformation. This work contributes to the broader goal of developing biologically plausible computer vision systems, with implications for video prediction, object recognition, and abstract concept formation for AI.
The SpectRx system has been developed to measure sphero-cylindrical spectacle lens power as an alternative to clinical lensmeters. This work was inspired by the ongoing global pandemic, which limited physical access t...
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ISBN:
(纸本)9781510654174;9781510654167
The SpectRx system has been developed to measure sphero-cylindrical spectacle lens power as an alternative to clinical lensmeters. This work was inspired by the ongoing global pandemic, which limited physical access to eye care facilities for regular eye exams The SpectRx system aims to bypass this limitation by providing at-home prescription measurements. The power and orientation of the spectacle lenses are obtained by the use of readily available objects such as a cell phone camera, a displayed or printed target, and a fixed-dimension magnetic stripe card. The magnification of the lenses can be calculated by examining the image captured through the lens of the target at a fixed distance. The magnification may be spatially varying due to the cylinder component of the lens. processing the pictures captured with a cell phone camera is done automatically with standard imageprocessingalgorithms. The processed images, in turn, are used to calculate a clinical prescription, i.e., Sph/CylxAxis. The SpectRx may expand access to quality eye care in not only the current pandemic situation but also in locations where eye care may not be easily accessible, such as some rural or remote areas. The imageprocessing and clinical prescription calculation are discussed here.
The paper describes an approach for estimation of inertial measurement unit using imageprocessing algorithm to determine the position of an object in space. The results of measurements of the angular velocities of a ...
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By combining optical systems and imageprocessing, wavefront coding can greatly expand the depth of focus and depth of field of optical systems. It has been widely used in iris detection, high-power microscopic object...
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ISBN:
(数字)9781510652095
ISBN:
(纸本)9781510652095;9781510652088
By combining optical systems and imageprocessing, wavefront coding can greatly expand the depth of focus and depth of field of optical systems. It has been widely used in iris detection, high-power microscopic objective lens, infrared optical system athermalized, and so on. At present, the image restoration algorithms commonly used in wavefront coding are based on deconvolution, Wiener filtering, and so on. Although these algorithms can achieve an excellent image restoration effect, they will also bring boundary ringing effects and artifacts to the image. When the image is disturbed by strong noise, the restoration effect will also be seriously affected. To solve these problems, a wavefront coded image restoration algorithm based on compressed sensing is proposed in this paper. The strong data reconstruction ability of the compressed sensing restoration algorithm is used to restore the encoded image obtained by the wavefront coding system. This method can effectively suppress noise and reconstruct the image without artifact and boundary ringing effect. Through the comparison of simulation results, the effectiveness of the proposed method is verified.
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