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Found 3 papers, 2 papers with code
Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network
Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions and octave transposed convolutions for learning multiple-spatial-frequency features, thus can better capture retinal vasculatures with varying sizes and shapes.
Automated Steel Bar Counting and Center Localization with Convolutional Neural Networks
The proposed CNN-DC can achieve 99. 26% accuracy for steel bar counting and 4. 1% center offset for center localization on the established steel bar dataset, which demonstrates that the proposed CNN-DC can perform well on automated steel bar counting and center localization.
Object Sorting Using a Global Texture-Shape 3D Feature Descriptor
In this paper, we present a global texture-shape 3D feature descriptor which can be utilized in a system of object recognition and grasping, and can perform object sorting tasks well.
