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Found 4 papers, 1 papers with code
Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning
Additionally, we experimented with three pixel selection methods: Bins, K-means, and MeanShift.
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering
We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner.
On the well-posedness of uncalibrated photometric stereo under general lighting
While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution under general lighting.
Deep interpretable architecture for plant diseases classification
In this paper, we propose a new trainable visualization method for plant diseases classification based on a Convolutional Neural Network (CNN) architecture composed of two deep classifiers.
