An Iterative Convex Hull Approach for Image Segmentation and Contour Extraction

Zhao, Jian; An, Jian

doi:10.1142/s0218001412550130

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed system was able to recognize different alphabet signs with an accuracy value of 91.66%. The limitation of this system lies in the use of a resource-intensive contour detector that is not suitable for devices with restricted computational capacities, because the computation of convex hull is time consuming according to [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Lahiani¹,

Néji²

2022

Int. J. Recent Contrib. Eng. Sci. IT

View full text Add to dashboard Cite

Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Lahiani¹,

Néji²

2022

Int. J. Recent Contrib. Eng. Sci. IT

View full text Add to dashboard Cite

show abstract

Blood Vessel Segmentation Based on the 3D Residual U-Net

Xin

Wen

Wang

et al. 2021

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

In this paper, we propose blood vessel segmentation based on the 3D residual U-Net method. First, we integrate the residual block structure into the 3D U-Net. By exploring the influence of adding residual blocks at different positions in the 3D U-Net, we establish a novel and effective 3D residual U-Net. In addition, to address the challenges of pixel imbalance, vessel boundary segmentation, and small vessel segmentation, we develop a new weighted Dice loss function with a better effect than the weighted cross-entropy loss function. When training the model, we adopted a two-stage method from coarse-to-fine. In the fine stage, a local segmentation method of 3D sliding window is added. In the model testing phase, we used the 3D fixed-point method. Furthermore, we employ the 3D morphological closed operation to smooth the surfaces of vessels and volume analysis to remove noise blocks. To verify the accuracy and stability of our method, we compare our method with FCN, 3D DenseNet, and 3D U-Net. The experimental results indicate that our method has higher accuracy and better stability than the other studied methods and that the average Dice coefficients for hepatic veins and portal veins reach 71.7% and 76.5% in the coarse stage and 72.5% and 77.2% in the fine stage, respectively. In order to verify the robustness of the model, we conducted the same comparative experiment on the brain vessel datasets, and the average Dice coefficient reached 87.2%.

show abstract

An Iterative Convex Hull Approach for Image Segmentation and Contour Extraction

Cited by 2 publications

References 14 publications

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Blood Vessel Segmentation Based on the 3D Residual U-Net

Contact Info

Product

Resources

About