ZQH-sail's repositories
fast_seg
Fast Interactive Image Segmentation using Graph-cut.
image-classification
Basic Image Segmentation and Classification Using Superpixel Segmentation and K-means classification.
Image-segmentation-using-SLIC-superpixels-and-graph-cuts
Implemented code for semi-automatic binary segmentation based on SLIC superpixels and graph-cuts.
AFCF
We proposed an automatic fuzzy clustering framework (AFCF) for image segmentation. The proposed framework has threefold contributions. Firstly, the idea of superpixel is used for the density peak (DP) algorithm, which efficiently reduces the size of the similarity matrix and thus improves the computational efficiency of the DP algorithm. Secondly, we employ a density balance algorithm to obtain a robust decision-graph that helps the DP algorithm achieve fully automatic clustering. Finally, a fuzzy c-means clustering based on prior entropy is used in the framework to improve image segmentation results.
Traditional-Computer-Vision-Algorithm-
Getting superpixels of the image using the scikit-image python package.
Superpixel-based-Fast-Fuzzy-C-Means-Clustering-for-Color-Image-Segmentation
We propose a superpixel-based fast FCM (SFFCM) for color image segmentation. The proposed algorithm is able to achieve color image segmentation with a very low computational cost, yet achieve a high segmentation precision.
hfs
HFS: Hierarchical Feature Selection for Efficient Image Segmentation
slic-histogram
Constructs a color (RGB) histogram for each SLIC superpixel region in an image.
RAG-example
scikit-image RAG example
image_segmentation
Image Segmentation using k-means, n-cuts and superpixels
Superpixel_Extraction
Extracting superpixels and saving them
Linear-Spectral-Cluster-Superpixel
Experiments and comparisons for review of the paper on LSC: Superpixel Segmentation using Linear Spectral Clustering
Spectral-Clustering
Segment Ultrasonic tumor image by self-tuning spectral clustering
msrm-image-segmentation
Implementation of "Interactive image segmentation by maximal similarity based region merging" by Ning et al
segraph
The segraph library creates graphs from SLIC superpixels. It can be used for using CRF for image segmentation https://pypi.python.org/pypi/segraph/0.5
A-superpixel-merge-for-proposal-region-generation
A demo of learning gitskills
SLIC_segmentation
SLIC + Histogram + SVM
U-Net-Interactive-Object-Selection
CS337 Group Project
SGMP
An Unsupervised RGBD Superpixel Segmentation Algorithm
superpixels-colorfulness
analyze pixel clusters for colofulness with openframeworks
superpixels_seeds
超像素分割方法，采用论文 SEEDS: Superpixels Extracted via Energy-Driven Sampling 提出的方法，对于物体的边界具有较好的保留，如下图所示。可以辅助目标检测中制作Banchmark。
superpixel_feature
extract superpixel_feature
SPX_SVM
SVM classification on Superpixel