The goal of image enhancement is to improve specific features or details of an image and enhance its overall visual quality. We introduce a novel image enhancement algorithm based on block-rooting processing combined ...
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Approximate computing has become a widely recognized method for designing energy-efficient arithmetic architectures in the context of error-tolerant applications. This paper presents the design and analysis of a 4-bit...
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The challenge of multi-object tracking stands as a fundamental focus in computer vision research, finding widespread applications in areas such as public safety, transportation, autonomous vehicles, robotics, and othe...
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The challenge of multi-object tracking stands as a fundamental focus in computer vision research, finding widespread applications in areas such as public safety, transportation, autonomous vehicles, robotics, and other domains involving artificial intelligence. Given the intricate nature of natural scenes, the occurrence of object occlusion and semi-occlusion is commonplace in basic tracking tasks. These factors often result in challenges such as ID switching, object loss, detection errors, and misaligned bounding boxes, thereby significantly impacting the precision of multi-object *** paper aims to address the aforementioned issues and proposes a novel multi-object tracker, incorporating Relative location mapping (RLM) and Target region density (TRD) modeling. The new tracker is more sensitive to differences in the spatial relationships between targets, allowing it to dynamically introduce low-scoring detection boxes into different regions based on the density of target regions in the image. This improves the accuracy of target tracking while avoiding the consumption of a significant amount of computational *** research results indicate that when applying this method to state-of-the-art multi-object tracking approaches, the proposed model achieves improvements of 0.4 to 0.8 points in the HOTA and IDF1 metrics on the MOT17 and MOT20 datasets. This demonstrates the effectiveness of the proposed method in enhancing multi-object tracking performance.
The currency has a great meaning in everyday *** each of us using the Currency notes in our day to day lives through Cash or online Payment. Thus currency recognisation has gained a great interest for many researchers...
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Background: Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalizevaping. Despite restrictions on depicting or promoting controlled substances, vape-re...
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Background: Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalizevaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on *** is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences. Objective: This study seeks to comprehensively describe direct (ie, explicit promotional efforts) and indirect (ie, subtlerstrategies) themes promoting vaping on TikTok using a mixture of computational and qualitative thematic analyses of socialmedia posts. In addition, we aim to describe how these themes might play a role in normalizing vaping behavior on TikTok foryouth audiences, thereby informing public health communication and regulatory policies regarding vaping endorsements onTikTok. Methods: We collected 14,002 unique TikTok posts using 50 vape-related hashtags (eg, #vapetokand #boxmod). Using thek-means unsupervised machine learning algorithm, we identified clusters and then categorized posts qualitatively based on ***, we organized all videos from the posts thematically and extracted the visual features of each theme using 3 machinelearning-based model architectures: residual network (ResNet) with 50 layers (ResNet50), visual Geometry Group model with16 layers, and vision transformer. We chose the best-performing model, ResNet50, to thoroughly analyze the image clusteringoutput. To assess clustering accuracy, we examined 4.01% (441/10,990) of the samples from each video cluster. Finally, werandomly selected 50 videos (5% of the total videos) from each theme, which were qualitatively coded and compared with the machine-derived classification for validation. Results: We successfully identified 5 major themes from the TikTok posts. vape product marketing(1160/10,990, 8.28%)reflected direct marketing, while the other 4 themes reflected indirect marketing: TikTok influencer(3775/
The field of imageprocessing widely utilizes scene text segmentation technology, with applications extending to image editing and font style transfer. These applications enhance image understanding quality and aid in...
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Belt conveyors are widely used in multiple industries, including coal, steel, port, power, metallurgy, and chemical, etc. One major challenge faced by these industries is belt deviation, which can negatively impact pr...
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Belt conveyors are widely used in multiple industries, including coal, steel, port, power, metallurgy, and chemical, etc. One major challenge faced by these industries is belt deviation, which can negatively impact production efficiency and safety. Despite previous research on improving belt edge detection accuracy, there is still a need to prioritize system efficiency and light-weight models for practical industrial applications. To meet this need, a new semantic segmentation network called FastBeltNet has been developed specifically for real-time and highly accurate conveyor belt edge line segmentation while maintaining a light-weight design. This network uses a dual-branch structure that combines a shallow spatial branch for extracting high-resolution spatial information with a context branch for deep contextual semantic information. It also incorporates the Ghost blocks, Downsample blocks, and Input Injection blocks to reduce computational load, increase processing frame rate, and enhance feature representation. Experimental results have shown that FastBeltNet has performed comparatively better than some existing methods in different real-world production settings, achieving promising performance metrics. Specifically, FastBeltNet achieves 80.49% mIoU accuracy, 99.89 FPS processing speed, 895 k parameters, 8.23 GFLOPs, and 430.95 MB peak CUDA memory use, effectively balancing accuracy and speed for industrial production.
This manuscript proposes a novel video-based robust and constrained estimation framework using the convolutional neural network and optimistic moving horizon estimation, with applications in interface estimation of oi...
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This manuscript proposes a novel video-based robust and constrained estimation framework using the convolutional neural network and optimistic moving horizon estimation, with applications in interface estimation of oil sand primary separation vessels (PSv). Although convolutional neural networks have achieved notable success across various computer vision and image analysis tasks, image outliers (such as blocking, blurriness, and lighting variations) would inevitably affect recognition/tracking performance. To address this issue, this manuscript proposes a robust estimation approach by leveraging a convolutional neural network and moving horizon estimation. Along this line, the interface recognition results by the convolutional neural network can be modeled as the measurements corrupted by disturbances and outliers, and the internal states can be modeled through a discrete-time finite-dimensional state space model. More importantly, the ubiquitously present constraints in the estimation task can be explicitly and readily handled by the moving horizon estimation. The stability analysis of the proposed method is provided in the presence of disturbances and model-plant mismatch. The effectiveness of the proposed method is validated through a pilot-scale laboratory study and an industrial primary separation vessel case study.
With the advance in information technologies involving machinevisionapplications, the demand for energyand time-efficient acquisition, transfer, and processing of a large amount of image data has rapidly increased. ...
With the advance in information technologies involving machinevisionapplications, the demand for energyand time-efficient acquisition, transfer, and processing of a large amount of image data has rapidly increased. However, current architectures of the machinevision system have inherent limitations in terms of power consumption and data latency owing to the physical isolation of image sensors and processors. Meanwhile, synaptic optoelectronic devices that exhibit photoresponse similar to the behaviors of the human synapse enable insensor preprocessing, which makes the front-end part of the image recognition process more efficient. Herein, we review recent progress in the development of synaptic optoelectronic devices using functional nanomaterials and their unique interfacial characteristics. First, we provide an overview of representative functional nanomaterials and device configurations for the synaptic optoelectronic devices. Then, we discuss the underlying physics of each nanomaterial in the synaptic optoelectronic device and explain related device characteristics that allow for the in-sensor preprocessing. We also discuss advantages achieved by the application of the synaptic optoelectronic devices to image preprocessing, such as contrast enhancement and image filtering. Finally, we conclude this review and present a short prospect.
This research paper presents cutting-edge technologies and methodologies to enhance precision agriculture and support sustainable farming practices. The study incorporates Satellite imageprocessing for land classific...
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