Dragonsky Wang's repositories
Single-Underwater-Image-Enhancement-and-Color-Restoration
Single Underwater Image Enhancement and Color Restoration, which is Python implementation for a comprehensive review paper "An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging"
Enhancement-of-Underwater-Images-with-Statistical-Model-of-BL-and-Optimization-of-TM
This is Python implementation for a underwater image enhancement paper "Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map"
underwater_datasets
Pointers to a collection of underwater image-based datasets and relevant resources.
External-Attention-pytorch
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
awesome-self-supervised-learning
A curated list of awesome self-supervised methods
machine_learning_python
通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯
100-Days-Of-ML-Code
100-Days-Of-ML-Code中文版
awesome-industrial-anomaly-detection
Paper list and datasets for industrial image anomaly detection (defect detection). 工业异常检测(瑕疵检测)论文及数据集检索库。
contextual-utterance-level-multimodal-sentiment-analysis
Context-Dependent Sentiment Analysis in User-Generated Videos
Emotion-detection
Real-time Facial Emotion Detection using Keras
Emotion-Detection-in-Videos
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
Face-Recogntion
Face recognition project using PCA and SVM
Facial-Expression-Detection
Facial Expression or Facial Emotion Detector can be used to know whether a person is sad, happy, angry and so on only through his/her face. This Repository can be used to carry out such a task.
Facial-Expression-Recognition
Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).
facial-expression-recognition-svm
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
multimodal-sentiment-analysis
Attention-based multimodal fusion for sentiment analysis
Salient-Object-Detection
This is tensorflow implementation for cvpr2017 paper "Deeply Supervised Salient Object Detection with Short Connections"
svm_mnist_digit_classification
MNIST digit classification with scikit-learn and Support Vector Machine (SVM) algorithm.
SVM_PCA_face_detection
PCA+SVM+KFold方法人脸识别（Face Detection using PCA+SVM）
the-gan-zoo
A list of all named GANs!
wangyanckxx.github.io
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