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a-superpixel-merge-for-proposal-region-generation

A demo of learning gitskills

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.

bsds500csv

computer-vision

fast_seg

Fast Interactive Image Segmentation using Graph-cut.

flic

hfs

HFS: Hierarchical Feature Selection for Efficient Image Segmentation

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.

image_complexity

image_segmentation

Image Segmentation using k-means, n-cuts and superpixels

linear-spectral-cluster-superpixel

Experiments and comparisons for review of the paper on LSC: Superpixel Segmentation using Linear Spectral Clustering

mergerofsuperpixels

msrm-image-segmentation

Implementation of "Interactive image segmentation by maximal similarity based region merging" by Ning et al

rag-example

scikit-image RAG example

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

sgmp

An Unsupervised RGBD Superpixel Segmentation Algorithm

slic-histogram

Constructs a color (RGB) histogram for each SLIC superpixel region in an image.

slic_segmentation

SLIC + Histogram + SVM

spectral-clustering

Segment Ultrasonic tumor image by self-tuning spectral clustering

spx_svm

SVM classification on Superpixel

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.

superpixel_extraction

Extracting superpixels and saving them

superpixel_feature

extract superpixel_feature

superpixel_grouping

superpixels-colorfulness

analyze pixel clusters for colofulness with openframeworks

superpixels_for_segmentation

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。

traditional-computer-vision-algorithm-

Getting superpixels of the image using the scikit-image python package.

u-net-interactive-object-selection

CS337 Group Project