This study presents an innovative approach to animal classification and recognition utilizing machine learning and deep learning methodologies. Leveraging advanced algorithms, the proposed system achieves remarkable a...
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Recommender systems make individualized suggestions for users based on their actions and preferences by utilizing machine learning (ML) and artificial intelligence (AI).. These systems have evolved significantly, inco...
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The universal transmission of pandemic COvID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbre...
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The universal transmission of pandemic COvID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COvID-19. In recent months, different techniques using imageprocessing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.
Recent years have seen a rapid development in machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are ima...
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Thanks to the emergence and continued devel-opment of machine learning, particularly deep learning, the research on visual question and answer, also known as vQA, has advanced dramatically, with great theoretical rese...
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Multi-focus image fusion, which is the fusion of two or more images focused on different targets into one clear image, is a worthwhile problem in digital imageprocessing. Traditional methods are usually based on freq...
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Multi-focus image fusion, which is the fusion of two or more images focused on different targets into one clear image, is a worthwhile problem in digital imageprocessing. Traditional methods are usually based on frequency domain or space domain, but they cannot guarantee the accurate measurement of all the image details of the activity level, and also cannot perfect the selection of image fusion rules. Therefore, the deep learning method with strong feature representation ability is called the mainstream of multi-focus image fusion. However, until now, most of the deep learning frameworks have not balanced the relationship between the two input features, the shallow features and the feature fusion. In order to improve the defects of previous work, we propose an end-to-end deep network, which includes an encoder and a decoder. Encoder is a pseudo-Siamese network. It extracts the same and different feature sets by using the features of double encoder, then reuses the shallow features and finally forms the coding. In decoder, the coding will be analyzed and dimensionally reduced enough to generate high-quality fusion image. We carried out extensive experiments. The results show that our network structure is better. Compared with various image fusion methods based on deep learning and traditional multi-focus image fusion methods in recent years, our method is slightly better than theirs in both objective metric contrast and subjective visual contrast.
image compression constitutes a significant challenge amid the era of information explosion. Recent studies employing deep learning methods have demonstrated the superior performance of learning-based image compressio...
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image compression constitutes a significant challenge amid the era of information explosion. Recent studies employing deep learning methods have demonstrated the superior performance of learning-based image compression methods over traditional codecs. However, an inherent challenge associated with these methods lies in their lack of interpretability. Following an analysis of the varying degrees of compression degradation across different frequency bands, we propose the end-to-end optimized image compression model facilitated by the frequency-oriented transform. The proposed end-to-end image compression model consists of four components: spatial sampling, frequency-oriented transform, entropy estimation, and frequency-aware fusion. The frequency-oriented transform separates the original image signal into distinct frequency bands, aligning with the human-interpretable concept. Leveraging the non-overlapping hypothesis, the model enables scalable coding through the selective transmission of arbitrary frequency components. Extensive experiments are conducted to demonstrate that our model outperforms all traditional codecs including next-generation standard H.266/vvC on MS-SSIM metric. Moreover, visual analysis tasks (i.e., object detection and semantic segmentation) are conducted to verify the proposed compression method that could preserve semantic fidelity besides signal-level precision.
This paper presents an approach for detection and quantification, with low latency, of the flow of leakage bubbles, in sub-surfaces, making use of video recorded by remote underwater vehicle using only image analysis ...
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ISBN:
(纸本)9798350319439
This paper presents an approach for detection and quantification, with low latency, of the flow of leakage bubbles, in sub-surfaces, making use of video recorded by remote underwater vehicle using only image analysis and under the premise of no overlapping bubbles. Implementation details are presented allowing its trial and reproduction. Results are confronted with videos acquired in a laboratory under controlled conditions and in real operational situation from literature, showing great efficiency in terms of processing time and all other important aspects for pipeline inspections, considering environment and safety in the oil industry.
Deep learning advancements have significantly enhanced computer visionapplications in precision agriculture. While RGB cameras operating in visible light are affordable, they provide limited information compared to m...
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object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion me...
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ISBN:
(数字)9798331506520
ISBN:
(纸本)9798331506537
object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5.
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