In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The feat...
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A large number of computer vision algorithms in their work use information about geometric parameters of objects in images. For such algorithms, an important element of their performance and efficiency is the calibrat...
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ISBN:
(数字)9798350364989
ISBN:
(纸本)9798350364996
A large number of computer vision algorithms in their work use information about geometric parameters of objects in images. For such algorithms, an important element of their performance and efficiency is the calibration process. It consists of determining camera and lens parameters on the basis of which further imageprocessing, including correction of optical distortions, is performed. This paper presents an attempt to estimate the accuracy of a standard algorithm for calibrating digital cameras by comparing its results with those of direct measurements. A series of measurements and calibrations were performed on six camera-lens pairs. The analysis of the obtained data showed that the results of calibration and direct measurements can be very different, even if the final reprojection error is small. Therefore, using calibration results with a small reprojection error does not guarantee a low measurement error due to incorrect determination of the physical parameters of the camera.
This paper focuses on eye tracking technology in application to augmented reality systems and considers the method and the algorithm for detecting the iris and calculating the coordinate of its centre. The algorithm w...
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ISBN:
(数字)9781510634794
ISBN:
(纸本)9781510634794
This paper focuses on eye tracking technology in application to augmented reality systems and considers the method and the algorithm for detecting the iris and calculating the coordinate of its centre. The algorithm was successfully tested on a prototype. The material base of the prototype for testing the algorithms was Raspberry Pi microcomputer and camera module. The software part of the system was Python programming language and OpenCv library for imageprocessing.
Soysauce-like aroms based wine needs to be stored in a dark place during long-term storage. When recognizing images of the base wine cellar, the quality of the collected images is greatly affected by environmental lig...
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ISBN:
(数字)9798350351033
ISBN:
(纸本)9798350351040
Soysauce-like aroms based wine needs to be stored in a dark place during long-term storage. When recognizing images of the base wine cellar, the quality of the collected images is greatly affected by environmental light, and the imaging quality is poor and there are many noise points in low light environments. The influence of ambient light poses significant challenges to computer vision. To address the issues of poor imagevisibility and high interference in low-illuminance environments, this article proposes an improved low-illuminance image enhancement method based on Retinex-Net network. Convert the input image from RGB domain to HSv color space for processing, introduce denoising convolutional neural network Deam-Net network into the reflection image of v component for denoising, enhance the color of the illumination image of v component through spatial attention module and channel attention module, and perform bilateral filtering and contrast stretching on the S component. Finally, fuse all components and convert to RGB for obtain the enhanced image. Prove through verification have shown that the low-illumination images enhanced by the algorithm proposed in this article have improved brightness, prominent details, minimal image distortion, and are realistic and natural. They are superior to other algorithms from subjective feelings and objective evaluation indicators.
Forthcoming large imaging surveys such as Euclid and the vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than 10(5) strong gravitational lens systems, including many rare and exotic po...
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Forthcoming large imaging surveys such as Euclid and the vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than 10(5) strong gravitational lens systems, including many rare and exotic populations such as compound lenses, but these 10(5) systems will be interspersed among much larger catalogues of similar to 10(9) galaxies. This volume of data is too much for visual inspection by volunteers alone to be feasible and gravitational lenses will only appear in a small fraction of these data which could cause a large amount of false positives. Machine learning is the obvious alternative but the algorithms' internal workings are not obviously interpretable, so their selection functions are opaque and it is not clear whether they would select against important rare populations. We design, build, and train several convolutional neural networks (CNNs) to identify strong gravitational lenses using vIS, Y, J, and H bands of simulated data, with F1 scores between 0.83 and 0.91 on 100 000 test set images. We demonstrate for the first time that such CNNs do not select against compound lenses, obtaining recall scores as high as 76 per cent for compound arcs and 52 per cent for double rings. We verify this performance using Hubble Space Telescope and Hyper Suprime-Cam data of all known compound lens systems. Finally, we explore for the first time the interpretability of these CNNs using Deep Dream, Guided Grad-CAM, and by exploring the kernels of the convolutional layers, to illuminate why CNNs succeed in compound lens selection.
Unmanned aerial vehicle (UAv) detection of moving vehicles is becoming into a significant study area in traffic control, surveillance, and military applications. The challenge arises in keeping minimal computational c...
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ISBN:
(纸本)9781665462198
Unmanned aerial vehicle (UAv) detection of moving vehicles is becoming into a significant study area in traffic control, surveillance, and military applications. The challenge arises in keeping minimal computational complexity allowing the system to be real-time as well. Applications of vehicle detection from UAvs include traffic parameter estimation, violation detection, number plate reading, and parking lot monitoring. The one stage detection model, YOLOv5 is used in this research work to develop a deep neural model-based vehicle detection system on highways from UAvs. In our system, several improvised strategies are put forth that are appropriate for small vehicle recognition under an aerial view angle which can accomplish real-time detection and high accuracy by incorporating an optimal pooling approach and dense topology method. Tilting the orientation of aerial photographs can improve the system's effectiveness. Metrics like hit rate, accuracy, and precision values are used to assess the performance of the proposed hybrid model, and performance is compared to that of other state-of-the-art algorithms.
The combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image proc...
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Traditional fire alarm systems are gradually losing their relevance in the fire safety domain as there is an increasing demand for Fire detection systems than systems that alarm after the incidence of fire. It has bec...
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ISBN:
(数字)9798331509828
ISBN:
(纸本)9798331509835
Traditional fire alarm systems are gradually losing their relevance in the fire safety domain as there is an increasing demand for Fire detection systems than systems that alarm after the incidence of fire. It has become inevitable to develop methodologies that can detect the early signs of fire. This review paper reviews several approaches by different authors. Many technological approaches combine sensors with microcontrollers, GSM devices, and cloud services for monitoring, prompt detection, and effective mitigation of dangers like gas leaks, fires, and harmful releases, in time. Innovations such as machine learning algorithms ensure worker safety are being developed alongside alert systems based on thresholds and requirements, for visualizing data remotely and for analysis purposes. The study also aims to enhance the accuracy of hazard predictions and decrease false alarms by using Machine Learning and imageprocessing technologies. This review paper gives an overall view of the role of machine learning methodologies in predicting fire and identifies the best-performing method. The study collectively highlights the significant impact of IoT in reducing risks and enhancing safety measures to safeguard lives and minimize property damage using reliable and cost-effective solutions.
To address the issues of complex background interference, multi-scale targets and poor detection accuracy of overlapping targets in apple image target detection, an apple fruit detection algorithm CGW-YOLOv8 based on ...
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ISBN:
(数字)9798350390254
ISBN:
(纸本)9798350390261
To address the issues of complex background interference, multi-scale targets and poor detection accuracy of overlapping targets in apple image target detection, an apple fruit detection algorithm CGW-YOLOv8 based on improved YOLOv8 is proposed. Firstly, the CBAM attention module is introduced based on the YOLOv8 detection model to enhance the saliency of the fruit in complex environments and reduce the computational cost of the network. Secondly, GFPN effectively combines feature information of different resolutions, so that the model can achieve higher detection accuracy when dealing with multi-scale targets. Finally, WIoU_v3 is used to assign different weights to each feature point to improve the matching accuracy between the predicted box and the real box, while significantly improving the positioning accuracy. The test results on the apple dataset show that this method achieves an average accuracy of 87.3 %, with an mAP improvement of 3.5% compared to the original YOLOv8 algorithm. The frame rate FPS is 357 frames per second, achieving real-time detection.
Children are most commonly affected by many neurological disorders now a days. One of the common disease is hydrocephalus occurring 1/1000 in infantile age group and also in adults as a result of congenital, acquired,...
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