An ameliorated deep dense convolutional neural network for accurate recognition of casting defects in X-ray images

Filtered Selective Search and Evenly Distributed Convolutional Neural Networks for Casting Defects Recognition

(2021) Xiaoyuan Ji et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

Resource-Constrained Neural Architecture Search on Edge Devices

(2021) Bo Lyu et al.   IEEE Transactions on Network Science and Engineering

DenseNet-201-Based Deep Neural Network with Composite Learning Factor and Precomputation for Multiple Sclerosis Classification

(2020) Shui-Hua Wang et al.   ACM Transactions on Multimedia Computing Communications and Applications

An ameliorated teaching–learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur’s entropy and Otsu’s between class variance

(2020) Bo Wu et al.   INFORMATION SCIENCES

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

(2019) Shuihua Wang et al.   Frontiers in Neuroscience

Deep learning

(2015) Yann LeCun et al.   NATURE

Computer vision technology for X-ray testing

(2014) D Mery   INSIGHT

Detection of flaws in aluminium castings: a comparative study between generative and discriminant approaches

(2013) F Ramírez et al.   INSIGHT

Statistical detection of defects in radiographic images using an adaptive parametric model

(2013) Rémi Cogranne et al.   SIGNAL PROCESSING

Quantification of the capability of micro-CT to detect defects in castings using a new test piece and a voxel-based comparison method

(2011) A. Staude et al.   NDT & E INTERNATIONAL

Flaw detection in aluminium die castings using simultaneous combination of multiple views

(2010) C Pieringer et al.   INSIGHT

Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels

(2010) Miguel Carrasco et al.   MACHINE VISION AND APPLICATIONS

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING