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Found 7 papers, 1 papers with code
Efficient Neural Network Approaches for Leather Defect Classification
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 and others.
Integrated Neural Network and Machine Vision Approach For Leather Defect Classification
Leather is a type of natural, durable, flexible, soft, supple and pliable material with smooth texture.
Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System
In this paper, we first review the processes of a conventional optical-flow-based recognition system, which comprised of facial landmarks annotations, optical flow guided images computation, features extraction and emotion class categorization.
Facial Expression Recognition
Micro Expression Recognition
+2
Automatic Defect Segmentation on Leather with Deep Learning
Then, a series of processes are conducted to predict the defect instances, including elicitation of the leather images with a robot arm, train and test the images using a deep learning architecture and determination of the boundary of the defects using mathematical derivation of the geometry.
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks.
Micro Expression Recognition
Micro-Expression Recognition
+1
Automatic Surface Area and Volume Prediction on Ellipsoidal Ham using Deep Learning
This paper presents novel methods to predict the surface and volume of the ham through a camera.
OFF-ApexNet on Micro-expression Recognition System
A new feature descriptor, Optical Flow Features from Apex frame Network (OFF-ApexNet) is introduced.
Micro Expression Recognition
Micro-Expression Recognition
+1
