most citedAutomatic Defect Segmentation on Leather with Deep Learning

37 citations · 61 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2019★ 9 cited

Efficient Neural Network Approaches for Leather Defect Classification

Sze-Teng Liong, Y. S. Gan, Kun-Hong Liu +5

Genuine leather, such as the hides of cows, crocodiles, lizards and goats usually contain natural and artificial defects, like holes, fly bites, tick marks, veining, cuts, wrinkles…

cs.CV2019★ 13 cited

Integrated Neural Network and Machine Vision Approach For Leather Defect Classification

Sze-Teng Liong, Y. S. Gan, Yen-Chang Huang +2

Leather is a type of natural, durable, flexible, soft, supple and pliable material with smooth texture. It is commonly used as a raw material to manufacture luxury consumer goods f…

cs.CV2019★ 37 cited

Automatic Defect Segmentation on Leather with Deep Learning

Sze-Teng Liong, Y. S. Gan, Yen-Chang Huang +2

Leather is a natural and durable material created through a process of tanning of hides and skins of animals. The price of the leather is subjective as it is highly sensitive to it…

cs.CV2019★ 2 cited

Automatic Surface Area and Volume Prediction on Ellipsoidal Ham using Deep Learning

Y. S. Gan, Sze-Teng Liong, Yen-Chang Huang

This paper presents novel methods to predict the surface and volume of the ham through a camera. This implies that the conventional weight measurement to obtain in the object's vol…

cs.CV2018

OFF-ApexNet on Micro-expression Recognition System

Sze-Teng Liong, Y. S. Gan, Wei-Chuen Yau +2

When a person attempts to conceal an emotion, the genuine emotion is manifest as a micro-expression. Exploration of automatic facial micro-expression recognition systems is relativ…