Detecting Third Umpire Decisions &amp; Automated Scoring System of Cricket

Kowsher, Md.; Alam, Mahbub; Uddin, Md. Jashim; Ahmed, Faisal; Ullah, Wali; Islam, Md. Rafiqul

doi:10.1109/ic4me247184.2019.9036705

Cited by 23 publications

(17 citation statements)

References 7 publications

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“…CNN is popular for image classification applications [33], though it has also recently been used in text classification. CNN can extract the important information from a sequence of text, and then feed it into an FNN, which also works for text classification.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

An Enhanced Neural Word Embedding Model for Transfer Learning

et al. 2022

Self Cite

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Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However, until now, low-resource languages such as Bangla have had very limited resources available in terms of models, toolkits, and datasets. Considering this fact, in this paper, an enhanced BanglaFastText word embedding model is developed using Python and two large pre-trained Bangla models of FastText (Skip-gram and cbow). These pre-trained models were trained on a collected large Bangla corpus (around 20 million points of text data, in which every paragraph of text is considered as a data point). BanglaFastText outperformed Facebook’s FastText by a significant margin. To evaluate and analyze the performance of these pre-trained models, the proposed work accomplished text classification based on three popular textual Bangla datasets, and developed models using various machine learning classical approaches, as well as a deep neural network. The evaluations showed a superior performance over existing word embedding techniques and the Facebook Bangla FastText pre-trained model for Bangla NLP. In addition, the performance in the original work concerning these textual datasets provides excellent results. A Python toolkit is proposed, which is convenient for accessing the models and using the models for word embedding, obtaining semantic relationships word-by-word or sentence-by-sentence; sentence embedding for classical machine learning approaches; and also the unsupervised finetuning of any Bangla linguistic dataset.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

An Enhanced Neural Word Embedding Model for Transfer Learning

et al. 2022

Self Cite

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“…The convolutional neural network is one of the most popular deep learning models. It can realize parallel computing and has high speed and high efficiency characteristics [52,53]. CNN is widely used in medical image analysis, remote sensing image analysis, noise signal analysis, and other fields.…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

A Survey of Multi-Focus Image Fusion Methods

Zhou

Zhi

et al. 2022

Applied Sciences

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As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.

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“…With the rapid progress of deep learning technology, the application of CNNs is becoming more and more extensive. For example, CNNs are used for image classification/segmentation [ 11 , 31 ], object/face detection [ 9 , 32 ], speech recognition [ 33 ], intelligent judge systems [ 34 ], smart cities safety improvement [ 35 ], and so on. However, traditional 2D CNNs have some limitations which cannot be applied in video streams.…”

Section: Related Workmentioning

confidence: 99%

Multi-Perspective Representation to Part-Based Graph for Group Activity Recognition

Lang

Xiang

et al. 2022

Sensors

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Group activity recognition that infers the activity of a group of people is a challenging task and has received a great deal of interest in recent years. Different from individual action recognition, group activity recognition needs to model not only the visual cues of individuals but also the relationships between them. The existing approaches inferred relations based on the holistic features of the individual. However, parts of the human body, such as the head, hands, legs, and their relationships, are the critical cues in most group activities. In this paper, we establish the part-based graphs from different viewpoints. The intra-actor part graph is designed to model the spatial relations of different parts for an individual, and the inter-actor part graph is proposed to explore part-level relations among actors, in which visual relation and location relation are both considered. Furthermore, a two-branch framework is utilized to capture the static spatial and dynamic temporal representations simultaneously. On the Volleyball Dataset, our approach obtains a classification accuracy of 94.8%, achieving very competitive performance in comparison with the state of the art. As for the Collective Activity Dataset, our approach improves the accuracy by 0.3% compared with the state-of-the-art results.

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Detecting Third Umpire Decisions & Automated Scoring System of Cricket

Cited by 23 publications

References 7 publications

An Enhanced Neural Word Embedding Model for Transfer Learning

An Enhanced Neural Word Embedding Model for Transfer Learning

A Survey of Multi-Focus Image Fusion Methods

Multi-Perspective Representation to Part-Based Graph for Group Activity Recognition

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