Court line extraction is one of the important steps in the analysis of sport videos. The court extraction is the foundation of the analysis of badminton video, and an efficient method with horizontal line projection K...
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Court line extraction is one of the important steps in the analysis of sport videos. The court extraction is the foundation of the analysis of badminton video, and an efficient method with horizontal line projection K-means machine learning algorithm to extract court lines from different broadcast badminton tournament videos is proposed in this paper. The horizontal lines are projected into 1-D histogram signal;then the signal is trained to learn the intensity of the histogram signal for locating the positions of the horizontal court lines. After the equations of the horizontal court lines and the court lines in the vertical direction have been formulized, the intersection points of the court lines can be calculated and the court line can be extracted. The experimental results show that the proposed method can extract the court lines more efficiently than that done by the Hough transform-related algorithms, which are widely applied in computer vision and self-driving car applications.
In the recent yeas, with the increase in the use of different social media platforms, image captioning approach play a major role in automatically describe the whole image into natural language sentence. image caption...
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A document layout can be more informative than merely a document’s visual and structural ***,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image ana...
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A document layout can be more informative than merely a document’s visual and structural ***,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different *** research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten *** proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative *** approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex *** experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten *** also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.
image segmentation is a crucial task in computer vision and imageprocessing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image an...
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image segmentation is a crucial task in computer vision and imageprocessing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models;look at the most popular datasets;and evaluate results in this Literature.
In the exploration of robot vision systems based on artificial neural networks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human...
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Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and va...
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Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. machine learning is automating human assistance by training an algorithm on relevant data. Supervised, Unsupervised, and Reinforcement Learning are the three fundamental categories of machine learning techniques. In this paper, we have discussed the different learning styles used in the field of Computer vision, Deep Learning, Neural networks, and machine learning. Some of the most recent applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos. Some machine learning techniques frequently include zero-shot learning, active learning, contrastive learning, self-supervised learning, life-long learning, semi-supervised learning, ensemble learning, sequential learning, and multi-view learning used in computer vision until now. There is a lack of systematic reviews about all learning styles. This paper presents literature analysis of how different machine learning styles evolved in the field of Artificial Intelligence (AI) for computer vision. This research examines and evaluates machine learning applications in computer vision and future forecasting. This paper will be helpful for researchers working with learning styles as it gives a deep insight into future directions.
This paper focuses on enhancing the captions generated by image captioning systems. We propose an approach for improving caption generation systems by choosing the most closely related output to the image rather than ...
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ISBN:
(纸本)9784885523434
This paper focuses on enhancing the captions generated by image captioning systems. We propose an approach for improving caption generation systems by choosing the most closely related output to the image rather than the most likely output produced by the model. Our model revises the language generation output beam search from a visual context perspective. We employ a visual semantic measure in a word and sentence level manner to match the proper caption to the related information in the image. This approach can be applied to any caption system as a post-processing method.
Currently, the welding process between electrical connectors and multi-core wires mainly relies on manual operation. This traditional method not only consumes a lot of time and manpower, but also long-term operation m...
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ISBN:
(纸本)9798350386783;9798350386776
Currently, the welding process between electrical connectors and multi-core wires mainly relies on manual operation. This traditional method not only consumes a lot of time and manpower, but also long-term operation may cause certain physical burden and health hazards to the operator. Therefore, researching and implementing automated welding between electrical connectors and multi-core wires has become an urgent problem to be solved. On the basis of summarizing the current research status at home and abroad, the software and hardware parts of the system were designed to meet the requirements of identifying and positioning welding circular electrical connectors. By introducing imageprocessing and machinevision technology, adopting a dual machine collaboration approach and based on machinevision methods, automatic wire welding of electrical connectors has been achieved, improving welding efficiency and reducing the labor intensity of operators. In addition, it is also conducive to promoting the development of industrial automation.
machine learning is the state of the art for many recurring tasks in several heterogeneous domains. In the last decade, it has been also widely used in Precision Agriculture (PA) and Wild Flora Monitoring (WFM) to add...
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machine learning is the state of the art for many recurring tasks in several heterogeneous domains. In the last decade, it has been also widely used in Precision Agriculture (PA) and Wild Flora Monitoring (WFM) to address a set of problems with a big impact on economy, society and academia, heralding a paradigm shift across the industry and academia. Many applications in those fields involve imageprocessing and computer vision stages. Remote sensing devices are very popular choice for image acquisition in this context, and in particular, Unmanned Aerial Vehicles (UAVs) offer a good tradeoff between cost and area coverage. For these reasons, research literature is rich of works that face problems in Precision Agriculture and Wild Flora Monitoring domains with machine learning/computer vision methods applied to UAV imagery. In this work, we review this literature, with a special focus on algorithms, model sizing, dataset characteristics and innovative technical solutions presented in many domain-specific models, providing the reader with an overview of the research trend in recent years.
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