This paper presents the design and implementation of a camera surveillance picture quality inspection system. The system assesses the video stream from surveillance cameras and provides immediate feedback on image qua...
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video surveillance requires simultaneous monitoring of multiple areas. Consequently, real-time automatic change detection of the monitored areas becomes very important. In the context of wide field-of-view conditions,...
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In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision imageprocessing algorithm flow, the image denoising process and target tracking process are opt...
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real-timevideo and imageprocessing are used in various industrial, medical, consumer electronics and embedded device applications. These applications typically demonstrate an increasing demand for computing power an...
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ISBN:
(纸本)9783031585012;9783031585029
real-timevideo and imageprocessing are used in various industrial, medical, consumer electronics and embedded device applications. These applications typically demonstrate an increasing demand for computing power and system complexity. Hence, edge detection is the most common and widely used technique in image or videoprocessing applications. Several traditional canny edge detection methods use fixed thresholding techniques to compare the pixel values. This sacrifices the edge detection performance and increases the computational complexity. Hence, the Canny Edge detection algorithm is preferred to enhance the image quality with reduced complexity. They adjust the quality of the image by manipulating the Sigma and Threshold parameters and detect the edges accurately by eliminating the noise. The reconfigurable canny edge detection algorithm presents a procedure for detecting edges without multipliers. The new algorithm uses a low-complex, non-uniform histogram gradient to compute thresholds and variable sigma values that replace the add and shift operator instead of multipliers to reduce the area and sigma. The simulation is done in the ModelSim platform using VHDL code which results in the output of bit sequences. By comparing the results of the reconfigurable canny edge detection and traditional algorithm, the new algorithm's performance can be observed with improvements of around 21% and 80% for consumed power and delay parameters respectively.
Monocular depth estimation algorithms aim to explore the possible links between 2D and 3D data, but challenges remain for existing methods to predict consistent depth from a casual video. Relying on camera poses and t...
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ISBN:
(纸本)9798400701788
Monocular depth estimation algorithms aim to explore the possible links between 2D and 3D data, but challenges remain for existing methods to predict consistent depth from a casual video. Relying on camera poses and the optical flow in the time-consuming testtime training phases makes these methods fail in many scenarios and cannot be used for practical applications. In this work, we present a data-driven post-processing method to overcome these challenges and achieve online processing. Based on a deep recurrent network, our method takes the adjacent original and optimized depth map as inputs to learn temporal consistency from the dataset and achieves higher depth accuracy. Our approach can be applied to multiple single-frame depth estimation models and used for various real-world scenes in real-time. In addition, to tackle the lack of a temporally consistent video depth training dataset of dynamic scenes, we propose an approach to generate the training video sequences dataset from a single image based on inferring motion field. To the best of our knowledge, this is the first datadriven plug-and-play method to improve the temporal consistency of depth estimation for casual videos. Extensive experiments on three datasets and three depth estimation models show that our method outperforms the state-of-the-art methods.
The article proposes an algorithm for processing parallel analysis of visual data obtained by a machine vision system, recorded information in the human visible spectrum, and information received by a range camera. An...
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ISBN:
(数字)9781510661714
ISBN:
(纸本)9781510661707;9781510661714
The article proposes an algorithm for processing parallel analysis of visual data obtained by a machine vision system, recorded information in the human visible spectrum, and information received by a range camera. An algorithm for the formation of stable features as elements of the human body, head and pupils of a person and parallel tracking of their increment is proposed. To highlight trend lines in element displacement and eliminate the high frequency component based on a combined criterion. The image is preliminarily processed to reduce the effect of the noise component based on a multi-criteria objective function. As test data used to evaluate the effectiveness, a video stream with a resolution of 1024x768 (8-bit, color image, visible range), 3D data, and expert evaluation data are used.
This research addresses urban parking challenges by allowing users to reserve parking spaces via a mobile app. The system integrates automated barriers and AI-powered cameras for accurate license plate recognition, en...
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Aiming at technical advantages of quickly discover and real-time tracking focused on targets with UAV video, we propose a multi-object tracking method based on spatial constraints. Utilizing the pre-training model of ...
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High Dynamic Range (HDR) imaging is a digital imageprocessing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables cap...
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ISBN:
(纸本)9798350324471
High Dynamic Range (HDR) imaging is a digital imageprocessing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in realtime. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.
videoprocessing is a specific type of signal processing that frequently uses video filters and video files or video streams as both the input and output signals. In real-time applications like Bio-medical application...
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