machinevisionapplications for intelligent vision systems in manufacturing industries were reported based on imageprocessing and artificial intelligence technology. We propose the imaging and vision development plat...
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In this paper, deformation correction, feature extraction, image filtering, particle manipulation and other steps are used to achieve the relative positioning between the objects. The visually-assisted image processin...
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High-speed industrial machine-vision (MV) applications such as surface inspection of steel sheets necessitate synchronous operation of multiple high-resolution cameras. Synchronization of cameras in the microsecond ba...
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High-speed industrial machine-vision (MV) applications such as surface inspection of steel sheets necessitate synchronous operation of multiple high-resolution cameras. Synchronization of cameras in the microsecond band is necessary to ensure accurate frame matching while melding images together. Existing approaches for synchronization employ dedicated electronic circuits or network-time-protocol (NTP) whose accuracies are in the millisecond band. Conversely, IEEE-1508 precision-time-protocol (PTP) synchronizes computers in highly accurate industrial measurement and control networks. Synchronization algorithms using PTP involve synchronizing computers connected to cameras. Although the computers synchronize in the microsecond band, the cameras synchronize in the millisecond band. Moreover, PTP is practically not used for synchronizing multiple devices due to the high bandwidth utilization of the network. This paper proposes a temporal synchronization algorithm and framework with two-way communication with timestamps and estimates mean path delays. Unicast transmission forms the basis of the synchronization framework, so that the network utilization is minimal, thereby ensuring the necessary bandwidth is available for image transmission. Experimental results show that the proposed approach outperforms the existing methodologies with synchronization accuracies in the microsecond band.
With the characteristics of high I/O packaging density and excellent electrothermal performance, ceramic column grid array (CCGA) packaging has been widely used in highly reliable applications such as aerospace. For C...
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ISBN:
(纸本)9798400709234
With the characteristics of high I/O packaging density and excellent electrothermal performance, ceramic column grid array (CCGA) packaging has been widely used in highly reliable applications such as aerospace. For CCGA solder column, defect detection needs to be applied before it leaves the factory. The traditional manual detection method has low detection efficiency and the detect accuracy is greatly influenced by human subjective factors. Aiming at this problem, a set of algorithm consists of digital imageprocessing method, Yolov3 network and U-Net network has been combined to realize the surface and inner defect detection for CCGA solder column. The whole algorithm has been embedded into industrial software system based on Qt environment and field experiments have been applied. The experiment results show that the whole algorithm has good real-time performance and the detection accuracy is consistent with manual detection accuracy. The algorithm proposed in this paper can meet the needs of online defect detection for CCGA solder column.
Leaf wetness duration is a crucial factor in plant disease management. Current optical methods use standard RGB images to classify leaf wetness as a binary problem, i.e., wet or dry. Green leaves absorb red light, whe...
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Leaf wetness duration is a crucial factor in plant disease management. Current optical methods use standard RGB images to classify leaf wetness as a binary problem, i.e., wet or dry. Green leaves absorb red light, whereas water reflects it. Based on this difference, an experimental platform was built to semi-automatically measure droplet deposition on grape leaves while capturing red laser images using an RGB camera. The setup measured changes in leaf mass and area of scanned leaves to determine the water mass per leaf area as a measure of leaf wetness. A sprayer was used to apply water droplets to the leaves. As the amount of deposited water increased, the mean red channel intensity decreased, with more bright spots in the images. These bright spots were more distinguishable as droplets in the green channel. Segmented leaf area, mean red channel intensity, and the number of identified droplets were used as image features. A generalised additive model was employed to predict the leaf wetness value with extracted features. The R-squared value for the prediction of the validation dataset was 0.71. image resolution and leaf orientation were identified as factors that influenced the model accuracy. The measurement method introduced in this study shows potential for accurately quantifying leaf wetness, and implies that in practice detecting leaf wetness can be integrated into a multi-classification problem, thereby broadening the potential applications of optical methods.
Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucia...
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In this paper, a computer vision-based approach for optimizing component test benches in endurance testing of automotive components. As a use case, the paper explores testing of automotive throttle position sensor usi...
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Imaging systems work diversely in the imageprocessing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcom...
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ISBN:
(纸本)9798400709234
Imaging systems work diversely in the imageprocessing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcomes for different computer visionapplications. The multiple unified models have been developed for multiple tasks such as multi-focus (MF), multi-exposure (ME), and multimodal (MM) image fusion. The careful tuning of such models is required to get optimal results, which are still not applicable to diverse applications. We propose an automatic machine learning (AML) based multi-tasking image fusion approach to overcome this problem. Initially, we evaluate source images with AML and feed them to the task-based models. Then, the source images are fused with the pre-trained and fine-tuned models. The experimental results authenticate the consequences of our proposed approach compared to generic approaches.
Over the years, the objective of image and video compression has been to preserve perceived quality according to the Human Visual System (HVS) with minimal rate. Traditional encoders achieve this with the use of Rate-...
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ISBN:
(纸本)9798350338935
Over the years, the objective of image and video compression has been to preserve perceived quality according to the Human Visual System (HVS) with minimal rate. Traditional encoders achieve this with the use of Rate-Distortion Optimization (RDO) techniques along with image Quality Assessment (IQA) metrics that are correlated with human perception. Nowa-days, a fast-growing number of applications fall within the realm of Video Coding for machines (VCM), where the final recipient of compressed data is not a human but a machine performing a vision task. Recently, the lack of correlation between existing distortion measures and machine perception has been revealed, especially for RDO algorithms where distortion measures are computed on a local scale. In this paper, we propose a machine perception-aware metric designed to be incorporated into a standard-compliant Versatile Video Coding (VVC) encoder. Our proposed metric relies on a supervised training procedure as well as additional information available on the encoder side. In terms of correlation with machine perception, our metric significantly outperforms existing distortion measures in the literature.
In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence....
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