Image Processing
Most of Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for an effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques have been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems.
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Computer vision systems are commonly used to design touch-less human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR) systems, which have a wide range of applications in several domains, such as, gaming, multimedia, automotive, home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce, often the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed.
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We provide two folders:
(1)The shallow depth of field image data set folder consists of 27 folders from 1 to 27.
In folder 1-27, each folder contains two test images and two word files. Img1 is the shallow depth of field image with the best focusing state taken with a 300 mm long focal lens, and img2 is the overall blurred image.
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The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.
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Rot corn grain image
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For comparing the performance of IQA methods, a database of confocal endoscopy image obtained in practical imaging conditions is proposed. There are 642 grayscale images with authentic distortion of 1024 × 1024 pixels in the database. Quality of the images were rated by 8 experienced researchers in operation and image processing of confocal endoscopy by the range of 1-5, where 1 denotes the lowest quality and 5 denotes the highest quality. Finally, the MOS of the images was computed by averaging the scores of the researchers.
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Experimental data of manuscript "CFAR algorithm based on different probabilit models for ocean target detection"
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The SoftCast scheme has been proposed as a promising alternative to traditional video broadcasting systems in wireless environments. In its current form, SoftCast performs image decoding at the receiver side by using a Linear Least Square Error (LLSE) estimator. Such approach maximizes the reconstructed quality in terms of Peak Signal-to-Noise Ratio (PSNR). However, we show that the LLSE induces an annoying blur effect at low Channel Signal-to-Noise Ratio (CSNR) quality. To cancel this artifact, we propose to replace the LLSE estimator by the Zero-Forcing (ZF) one.
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<p>Our data set contains five subsets, which are Seadata, RCSdata, RD_SeaImage, BP_SeaImage and SSHdata. Seadata is the data of simulated sea. RCSdata is the data of sea surface backward scattering coefficient. RD_SeaImage is the simulated images of sea surface. BP_SeaImage is the simulated images of sea surface. SSHdata is the sea surface height data.</p>
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