- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Cognitive Cameras - [Opinion]
摘要： Today, computer vision can track cars, faces, and production processes as accurately as most people can. When there’s a lot of data to sift through, computer-vision models are better than people. But there are limits. Computers still need more time than a human to recognize a person or action. They can’t follow a person or object between multiple video cameras. They can be fooled easily. They can’t assign meaning to what they see. These are the limits engineers must overcome to make cameras more useful in manufacturing and in smart cities.
关键词： surveillance,image processing,adversarial attacks,computer vision,cognitive cameras
更新于2025-09-23 15:21:21
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Statistical evaluation of corner detectors: does the statistical test have an effect?
摘要： This study explores the use of several non-parametric statistical tests for evaluating the performances of computer vision algorithms, specifically corner detectors, as a more reliable alternative to the graphical approaches that have been commonly employed to date. Using synthetic images carrying corners of different internal angles and orientations and a carefully designed testing framework, a ranking of the performances of corner detectors was established. It was found that Harris & Stephens and SUSAN out-performed more modern detectors. These are one of the few examples where evaluation of vision operators independent of the application has predicted performance in a real-world problem. A similar exercise on real images of the same patterns produced similar results and the findings of a real-world application that uses corners to identify signage were also consistent. Together, all of the tests considered essentially perform pairwise comparisons of performance, so when many algorithms are involved it is important to take account of the potential for type I statistical errors. Several approaches were evaluated and none were found to affect the conclusions.
关键词： performance evaluation,computer vision algorithms,corner detectors,non-parametric statistical tests
更新于2025-09-23 15:21:21
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Application of A Computer Vision Method for Soiling Recognition in Photovoltaic Modules for Autonomous Cleaning Robots
摘要： It is well known that this soiling can reduce the generation efficiency in PV system. In some case according to the literature of loss of energy production in photovoltaic systems can reach up to 50%. In the industry there are various types of cleaning robots, they can substitute the human action, reducing cleaning cost, be used in places where access is difficult, and increasing significantly the gain of the systems. In this paper we present an application of computer vision method for soiling recognition in photovoltaic modules for autonomous cleaning robots. Our method extends classic CV algorithm such Region Growing and the Hough. Additionally, we adopt a pre-processing technique based on Top Hat and Edge detection filters. We have performed a set of experiments to test and validate this method. The article concludes that the developed method can bring more intelligence to photovoltaic cleaning robots.
关键词： Solar Panel,Computer Vision,Soiling Identification,Cartesian Robots,Autonomous Robots
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE Conference on Information and Communication Technology (CICT) - Allahabad, India (2019.12.6-2019.12.8)] 2019 IEEE Conference on Information and Communication Technology - LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture
摘要： Wireless Sensor Networks and Satellite Remote Sensing are some of the existing techniques that are used to collect, analyze and interpret data from the agricultural crop sites. However, there are certain limitations common to both of these techniques that are concerned with the latency and the resolution of the data collected. UAVs (Unmanned Aerial Vehicles) are becoming another alternative that has become integral nowadays due to its affordable and scalable nature while offering user friendly requirements and customizations. This proposes a novel and cost-effective technique (LEDCOM) that harnesses the capabilities of ground sensors and unmanned UAV while using computer vision methods to produce a qualitative data analysis system that describes the crop site under supervision. An UAV is assumed to collect the ground based sensor node data in the form of binary patterns on LED Arrays that is encoded in the image taken by a camera of a drone. Image processing techniques are used to identify and decode the LED sequences from the arrays. The performance of the proposed system is evaluated under different features and image resolutions within the same lighting conditions. A promising performance is observed for LED pattern identi?cation from the challenging images taken from a height.
关键词： Computer Vision,LED Pattern Identi?cation,UAVs,Wireless Sensor Networks,Precision Agriculture,Remote Sensing
更新于2025-09-23 15:21:01
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3D tracking of multiple onsite workers based on stereo vision
摘要： Varied sensing technologies have been delved in for positioning workers and equipment in construction sites. Vision-based technology has been received growing attentions by the virtue of its tag-free and inexpensive con?guration. One of the core research works in this area was the use of stereo camera system for tracking 3D locations of construction resources. However, the previous work was limited to tracking of a single entity. To overcome the limitation, this paper presents a new framework for tracking multiple workers. The proposed framework supplements the previous work by embedding an additional step, entity matching, which ?nds corresponding matches of tracked workers across two camera views. Entity matching takes advantage of the epipolar geometry and workers' motion directions for ?nding correct pairs of a worker's projections on two image planes. This paper also presents an e?ective approach of camera calibration for positioning entities located a few tens of meters away from the cameras. The proposed framework is evaluated based on completeness, continuity, and localization accuracy of the generated trajectories. The evaluation results have shown its capability of retrieving 96% of actual movements, within localization errors of 0.821 m with 99.7% con?dence.
关键词： Occlusion,Computer vision,Entity matching,Tracking,Construction worker,Site monitoring,Camera calibration
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) - Torino, Italy (2018.9.4-2018.9.7)] 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) - A single camera inspection system to detect and localize obstacles on railways based on manifold Kalman filtering
摘要： Railway line surveillance is important for providing safe and smooth travel of trains under effects of environmental or human-generated damages to the railway. This work presents a Structure from Motion pipeline specifically designed with the aim of supporting the monitoring operations of the railway infrastructure using a monocular camera mounted on the train’s tractor. Within this work we developed a dynamical reconstruction instrument based on the mathematics of the projective geometry for handling the problem of localization, by triangulation techniques of points, lines, whole objects and of other known elements. Exploiting the a-priori knowledge of the scene structure (known track gauge) and the camera intrinsic parameters it is possible to reconstruct in metric dimension the trajectory of the train and the position of the detected object. The approach proposed here combines Computer Vision techniques to detect the significant elements and to classify a set of features with Bayesian filtering. Algorithms for this specific purpose have been developed in order to identify the rail track geometry, and a line-based approach has been adopted to assess the camera poses. Starting from these first estimates, a manifold Unscented Kalman Filter operates on the set of robustly matched features, fusing heterogeneous cues about the camera orientation and using RANSAC to find the best solution. Consequently, the detected objects can be triangulated and localized. An analysis using real captures is reported to prove the quality of the results obtained.
关键词： RANSAC,Railway line surveillance,projective geometry,Bayesian filtering,monocular camera,Unscented Kalman Filter,Computer Vision,Structure from Motion
更新于2025-09-23 15:21:01
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - HESCNET: A Synthetically Pre-Trained Convolutional Neural Network for Human Embryonic Stem Cell Colony Classification
摘要： This paper proposes a method for improving the results of deep convolutional neural network classification using synthetic image samples. Generative adversarial networks are used to generate synthetic images from a dataset of phase-contrast, human embryonic stem cell (hESC) microscopy images. hESCnet, a deep convolutional neural network is trained, and the results are shown on various combinations of synthetic and real images in order to improve the classification results with minimal data.
关键词： Image Processing,Generative Adversarial Networks,Deep Learning,Computer Vision,Video Bioinformatics
更新于2025-09-23 15:21:01
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[Institution of Engineering and Technology 8th Renewable Power Generation Conference (RPG 2019) - Shanghai, China (24-25 Oct. 2019)] 8th Renewable Power Generation Conference (RPG 2019) - Power grid frequency regulation participated by photovoltaic generation to adapt to different control modes of grid-side AGC
摘要： Labeling connected components and calculating the Euler number in a binary image are two fundamental processes for computer vision and pattern recognition. This paper presents an ingenious method for identifying a hole in a binary image in the first scan of connected-component labeling. Our algorithm can perform connected component labeling and Euler number computing simultaneously, and it can also calculate the connected component (object) number and the hole number efficiently. The additional cost for calculating the hole number is only O(H), where H is the hole number in the image. Our algorithm can be implemented almost in the same way as a conventional equivalent-label-set-based connected-component labeling algorithm. We prove the correctness of our algorithm and use experimental results for various kinds of images to demonstrate the power of our algorithm.
关键词： image feature,the Euler number,image analysis,the hole number,pattern recognition,Computer vision
更新于2025-09-23 15:19:57
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A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications
摘要： In this paper, the authors propose an UAV-based automatic inspection method for photovoltaic plants analyzing and testing a vision-based guidance method developed to this purpose. The maintenance of PV plants represents a key aspect for the pro?tability in energy production and autonomous inspection of such systems is a promising technology especially for large utility-scale plants where manned techniques have signi?cant limitations in terms of time, cost and performance. In this light, an ad hoc ?ight control solution is investigated to exploit available UAV sensor data to enhance ?ight monitoring capability and correct GNSS position errors with respect to ?nal target needs. The proposed algorithm has been tested in a simulated environment with a software-in-the loop (SITL) approach to show its effectiveness and ?nal comparison with state of the art solutions.
关键词： PV plant monitoring,Unmanned Aerial Vehicles,image processing,?y-by-sensor,computer vision,automatic ?ight
更新于2025-09-23 15:19:57
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497 The automated and real time use of infrared thermography in the detection and correction of DFD and fevers in cattle.
摘要： can provide an effective tool for predicting color and marbling in the pork industry at online speeds.
关键词： Computer Vision,Image Processing,Pork Loin
更新于2025-09-19 17:15:36