A Hierarchical Graph Method Using in A* Algorithm for Vietnamese Parsing Technique

Thang, Le Quang; Tran, Duan

doi:10.1109/ialp.2012.12

Cited by 2 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hang [17] has presented research on the A * algorithm for parsing techniques and proposed an hierarchical graph method (HGM model) based on the same algorithm. is method can replace the existing virtual node method (VNM), which was used to overcome the problem of Chomsky standard forms.…”

Section: Literature Surveymentioning

confidence: 99%

“…Hence, a wireframe model is determined and used for further processing. e wireframe model is a skeletal or edge representation of 3D objects using lines and curves [14][15][16][17]. Other basic patterns are identified in the 3D image using the syntactic pattern approach, and then, finally, these patterns and frequency of these patterns are calculated for the classification purpose.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images

Rani

Ghai

Kumar

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%