6 research outputs found
Orderly Disorder in Point Cloud Domain
In the real world, out-of-distribution samples, noise and distortions exist
in test data. Existing deep networks developed for point cloud data analysis
are prone to overfitting and a partial change in test data leads to
unpredictable behaviour of the networks. In this paper, we propose a smart yet
simple deep network for analysis of 3D models using `orderly disorder' theory.
Orderly disorder is a way of describing the complex structure of disorders
within complex systems. Our method extracts the deep patterns inside a 3D
object via creating a dynamic link to seek the most stable patterns and at
once, throws away the unstable ones. Patterns are more robust to changes in
data distribution, especially those that appear in the top layers. Features are
extracted via an innovative cloning decomposition technique and then linked to
each other to form stable complex patterns. Our model alleviates the
vanishing-gradient problem, strengthens dynamic link propagation and
substantially reduces the number of parameters. Extensive experiments on
challenging benchmark datasets verify the superiority of our light network on
the segmentation and classification tasks, especially in the presence of noise
wherein our network's performance drops less than 10% while the
state-of-the-art networks fail to work
Remote sensing image fusion via compressive sensing
In this paper, we propose a compressive sensing-based method to pan-sharpen the low-resolution multispectral (LRM) data, with the help of high-resolution panchromatic (HRP) data. In order to successfully implement the compressive sensing theory in pan-sharpening, two requirements should be satisfied: (i) forming a comprehensive dictionary in which the estimated coefficient vectors are sparse; and (ii) there is no correlation between the constructed dictionary and the measurement matrix. To fulfill these, we propose two novel strategies. The first is to construct a dictionary that is trained with patches across different image scales. Patches at different scales or equivalently multiscale patches provide texture atoms without requiring any external database or any prior atoms. The redundancy of the dictionary is removed through K-singular value decomposition (K-SVD). Second, we design an iterative
l1-l2
minimization algorithm based on alternating direction method of multipliers (ADMM) to seek the sparse coefficient vectors. The proposed algorithm stacks missing high-resolution multispectral (HRM) data with the captured LRM data, so that the latter is used as a constraint for the estimation of the former during the process of seeking the representation coefficients. Three datasets are used to test the performance of the proposed method. A comparative study between the proposed method and several state-of-the-art ones shows its effectiveness in dealing with complex structures of remote sensing imagery
DeepPod: A convolutional Neural Network Based Quantification of Fruit Number in Arabidopsis
Images used for manual annotation, training and test of a convolutional neural network. Images are in *.png format and manual data in .csv