Machine Vision and Applications

Papers
(The H4-Index of Machine Vision and Applications is 18. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)
ArticleCitations
Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition72
A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis51
Graph neural networks in node classification: survey and evaluation49
Segmentation of photovoltaic module cells in uncalibrated electroluminescence images46
An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature43
RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging40
A cognitive vision method for the detection of plant disease images36
Optimal feature level fusion for secured human authentication in multimodal biometric system36
Interpretable visual transmission lines inspections using pseudo-prototypical part network34
Inception recurrent convolutional neural network for object recognition31
Aluminum Casting Inspection using Deep Object Detection Methods and Simulated Ellipsoidal Defects27
Inflated 3D ConvNet context analysis for violence detection25
Lesion-aware attention with neural support vector machine for retinopathy diagnosis24
A novel approach for ear recognition: learning Mahalanobis distance features from deep CNNs22
Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition22
EfficientLiteDet: a real-time pedestrian and vehicle detection algorithm19
Deblur and deep depth from single defocus image19
Convolutional neural network-based cross-corpus speech emotion recognition with data augmentation and features fusion18
Micro-concrete crack detection of underwater structures based on convolutional neural network18
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