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jizhihang's Projects

colmap icon colmap

COLMAP - Structure-from-Motion and Multi-View Stereo

color-names icon color-names

An improved version of the color name model described here: http://lewisandquark.tumblr.com/post/160776374467/new-paint-colors-invented-by-neural-network.

color_transfer icon color_transfer

Performs color transfer between two images. Loosely based on the "Color Transfer between Images" paper by Reinhard et al., 2001.

comet icon comet

Code and data for the paper "Learning Sparse Metrics, One Feature at a Time", Y. Atzmon, U. Shalit, G. Chechik, JMLR 2015

componentspd icon componentspd

Component symmetric positive definite matrices (CSPD) matrices for describing image sets: A low-dimensional discriminative data descriptor than traditional covariance descriptors for image set classification.

computational_lab_manual icon computational_lab_manual

We wrote a getting-started file for reproducible research in computational science (computational biology).

computer-vision-blobdetection icon computer-vision-blobdetection

To detect blobs at multiple scales, there are two strategies to complete the task: (1) iteratively filter the image with kernel of increasing size. (2) iteratively downsample the image and filter it with the kernel of fixed size.

content-based-image-retrieval-1 icon content-based-image-retrieval-1

In this project I have used Color Difference Histogram, Local binary Pattern, Histogram of Oriented gradients, Edge Orientation Histogram, Color Layout descriptor and combinations of these to retrieve images from feature database of image dataset.

context-encoder icon context-encoder

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

contextprediction icon contextprediction

The authors' implementation of "Context as Supervisory Signal: Discovering Objects with Predictable Context"

convnet icon convnet

Convolutional Neural Networks for Matlab for classification and segmentation, including Invariang Backpropagation (IBP) and Adversarial Training (AT) algorithms. Trained on GPU, require cuDNN v5.

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