CU Depth Decision Based on FAST Corner Detection for HEVC Intra Prediction

Jeon, Seungsu; kim, Namuk; Jeon, Byeong-Moon

doi:10.5909/jbe.2016.21.4.484

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…There are several kinds of corner detection algorithms at present, Fast and Harris algorithms are mainly used in academic research [11,12]. Compared with Fast algorithm, Harris algorithm has stronger accuracy and stability [13,14], which make it is possible to avoid the influence from burrs and dusts in the process of features recognition.…”

Section: Corner Detectionmentioning

confidence: 99%

Research on conditional characteristics vision real‐time detection system for conveyor belt longitudinal tear

Qiao

Pang

et al. 2017

IET Science, Measurement & Technology

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Abstract:Conveyor belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of conveyor belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and conveyor belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of conveyor belt, thus the problem of starting warning only based on single feature can be effectively avoided.

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Section: Corner Detectionmentioning

confidence: 99%