guotianli's Stars
megvii-research/CADDM
Official implementation of ID-unaware Deepfake Detection Model
eriklindernoren/PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
MisterBooo/LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）
radishgiant/ThresholdAndSegment
自动阈值提取及分割的算法合集：目前已更新的8种算法
Madhumj14/-AUTOMATIC-SEGMENTATION-OF-TUMOUR-REGION-FROM-BRAIN-MRI-IMAGES
We have designed a tool for MRI brain image segmentation for tumour detection and feature extraction using Multi-thresholding , K-means algorithm and fuzzy-c means algorithm.
ideb1988/Image_Segmentation
texture and color based Image Segmentation using K-Means Clustering
murali1996/object_tracking_under_occlusion
Skin-colored objects are detected with a Bayesian classifier which is bootstrapped with a small set of training data. Then, an off-line iterative training procedure is employed to refine the classifier using additional training images. On-line adaptation of skin-color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique which can handle multiple skin-colored objects. Such objects may move in complex trajectories and occlude each other in the field of view of a possibly moving camera. Moreover, the number of tracked objects may vary in time. A prototype implementation of the developed system operates on 720x1080 pixel video with a frame rate of 30 per second.
lipiji/PG_Curve
Matlab code for computing and visualization: Confusion Matrix, Precision/Recall, ROC, Accuracy, F-Measure etc. for Classification.
lujian9328/LungCancerDetection
This project presents the better Computer Aided Diagnosing (CAD) system for automatic detection of lung cancer. The initial process is lung region detection by applying basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms to the CT scan images. After the lung region is detected, the segmentation is carried out with the help of Mean Shift clustering algorithm. With these, the features are extracted and the diagnosis rules are generated. These rules are then used for learning with the help of Random Forest. The experimentation is performed with 15, 000 images obtained from the kaggle contest. The experimental result shows that the proposed CAD system can able to tell the posterior probability of lung cancer for a patient based on the detection algorithm. Also the usage of Random Forest will increase the accuracy of detecting the cancer nodules.
ignaciorlando/fundus-vessel-segmentation-tbme
In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
mutual-ai/fundus-vessel-segmentation-tmbe
One of the first steps in automatic fundus image analysis is the segmentation of the retinal vasculature, which provides valuable information related to several diseases. In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. This task remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors, such as a Potts model or total variation. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. The evaluation of our method is performed both quantitatively and qualitatively on DRIVE, STARE, CHASEDB1 and HRF, showing its ability to deal with different types of images and outperforming other techniques, trained using state of the art features.
siddheshk/Faster-Kmeans
Code for a faster K-means clustering heuristic
aayush-bhandari/Image-Segmentation
Unsupervised learning technique to reduce the number of color points in an image using K-means
GeorgeSeif/Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
hellochick/ICNet-tensorflow
TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".
upul/Semantic_Segmentation
Semantic Segmentation using Fully Convolutional Neural Network.
kimoktm/U-Net
U-net segmentation network in Tensorflow
biergaizi/ChinaUnicom-NetSpeed-Client
A cross-platform and open source replacement of China Unicom Net Speed Client
milesial/Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
tzutalin/ImageNet_Utils
:arrow_double_down: Utils to help download images by id, crop bounding box, label images, etc.
zsdonghao/u-net-brain-tumor
U-Net Brain Tumor Segmentation
yeyun111/dlcv_for_beginners
《深度学习与计算机视觉》配套代码
sthalles/deeplab_v3
Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN
daijifeng001/R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
DUTFangXiang/ExtractFCNFeature
MATLAB toolbox Matconvnet extracts the FCN features for computer vision applications
hitzoro/FCN-ColorLabel
315386775/FCN_train
The code includes all the file that you need in the training stage for FCN
wangleihitcs/Papers
读过的CV方向的一些论文，图像生成文字、弱监督分割等
orobix/retina-unet
Retina blood vessel segmentation with a convolutional neural network
akirasosa/mobile-semantic-segmentation
Real-Time Semantic Segmentation in Mobile device