Showing results for FInal Background Vector Vector Vector
GitHub Repo
https://github.com/dhvanikotak/Emotion-Detection-in-Videos
dhvanikotak/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.
GitHub Repo
https://github.com/averule/Hand-Gesture-Recognition
averule/Hand-Gesture-Recognition
Digital image processing helps replace several mundane activities. My project in final year was ‘Application Control using Hand Gesture Recognition From 3-Dimensional Images’. We worked extensively on processing a 3-D image to recognize the encrypted gesture, with the added 3rd dimension for more number of gestures. We started with extensive image segmentation to suppress background, handle dynamic lighting conditions and extract only portion of hand from the 3-D image. A tedious task, we researched many papers on IEEE and books on Image processing, to come-up with a complex code logic involving a combination of three different segmentation techniques involving RGB, YCbCr and HSV models. Further, with mathematical calculations involving use of Eigen values and Eigen vectors were derived based on the co-variance matrix, generated as per captured image, we managed to extract maximum information. A Euclidean distance was calculated to denote the deviation of captured image from each set of pre-captured images for all defined gestures. Hectic code optimization helped save precious execution time. The gesture corresponding to minimal Euclidean distance was the identified gesture. This project acquainted me with MATLAB and I learnt about various image enhancement and image segmentation techniques. I presented a paper at IEEE’s International Conference on Convergence of Technology (I2CT), Pune, published in the journal with ISBN “978-1-4799-3759-2”.
GitHub Repo
https://github.com/Photon-gjq/Undergraduate-final-thesis
Photon-gjq/Undergraduate-final-thesis
This is my undergraduate thesis written in LaTeX. I copy Spinor and Spacetime Vol 1,Penrose 1984 (partically) to Chinese, and explain why massless spin 3/2 Rarita Schwinger Field can only exist in Ricci Flat spacetime(don't consider the backreaction with gravity, the background field). Moreover, the tex code of including a vectorized mathcha logo(tikz) is included.
GitHub Repo
https://github.com/FilitsaK/Master-Thesis-2019-Durham-Univesity