This article presents an intelligent vision sensor (IvS) with embedded tiny convolutional neural network (CNN) model and programmable processing-in-sensor (PIS) circuit for real-time inference applications of low-powe...
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Although image inpainting, or the art of restoring old and degraded photographs/images, has been around for a long time, it has lately acquired popularity as a consequence of technical advancements in imageprocessing...
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Computer vision enables for the detection of things even in densely populated areas. This work investigates autonomous object detection and activity recognition in static and dynamic images, as well as their potential...
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Early stage smoke detection using image and video analysis is an important area of research due to its enormous applications in mitigating fire hazards and ensuring environmental safety. Numerous solutions have been p...
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Early stage smoke detection using image and video analysis is an important area of research due to its enormous applications in mitigating fire hazards and ensuring environmental safety. Numerous solutions have been proposed for real-time smoke detection using conventional imageprocessing, machine learning, and deep learning techniques. Smoke pattern, motion analysis, color and texture are important characteristics that help identify it in the outdoor environment. vision-based Smoke detection algorithms can be broadly classified into three categories: smoke classification, segmentation, and bounding box estimation. This paper presents a comprehensive survey of existing techniques on smoke detection in the outdoor environment using image and video analysis. To perform the survey, initially 271 articles were collected from different sources like Google Scholar, Science Direct, IEEE Xplore, SpringerLink, Wiley and ACM Digital Library using the keyword search. Based on their focus on the vision-based solutions for the outdoor environment, 126 articles were identified as relevant to the present survey. Starting from the initial IP approaches that are frequently referred in the literature, machine learning and deep learning approaches have also been reviewed for each type of smoke detection. Performance of algorithms, datasets used in the research, evaluation metrics, challenges and future directions of research are also discussed.
In this paper, we propose an approach for synthesizing novel view images from a single RGBD (Red Green Blue-Depth) input. Novel view synthesis (NvS) is an interesting computer vision task with extensive applications. ...
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Computer vision, the cornerstone of modern artificial intelligence. Moving forward with the development of new tools and techniques, OpenCv (Open Source Computer vision Library) coupled with pandas' powerful data ...
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Object detection is an advanced area of imageprocessing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. T...
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The pervasive integration of modern artificial intelligence into daily life necessitates robust human-computer interaction, underscored by advancements in computer technology. As the primary human tool, hand position,...
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Plant diseases can be quickly detected to lessen the harm they cause to crops. Convolutional neural networks are widely used in deep learning, specifically for problems related to pattern recognition and machinevisio...
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The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a mul...
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ISBN:
(纸本)9781510667877;9781510667884
The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a multi-criteria image filtering block built on an objective function that minimizes the weighted average sum of the average square of the first-order finite difference, as well as the average square of the distance difference between the input implementation and the generated data;parallel separation of objects by analyzing local features, statistical analysis of histogram changes, building a mask of object detailing and frequency analysis;the formation of a feature mask and the search for similarity elements by analyzing the generated features. On the test data set, an example of determining small-sized objects on a complex background with their subsequent classification into class objects is presented. The data were obtained by a machinevision system installed on a robotic complex. Data on the required parameters of the formed machinevision systems are given, recommendations on the required parameters of the algorithms are presented.
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