final_proj_pool.ipynb

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    "# Final Project Pool » \n",
    "\n",
    "### __1. Object Detection__  \n",
    "Object Detection involves detecting instances of objects in images or videos. \n",
    "It involves identifying and localizing objects within an image and classifying them into pre-defined categories.\n",
    "\n",
    "Codebase: https://github.com/tensorflow/models/tree/master/research/object_detection  \n",
    "Research paper: https://arxiv.org/abs/1506.01497  \n",
    "  \n",
    "### __2. Image Segmentation__  \n",
    "Image Segmentation involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of the image. It involves assigning a label or class to each pixel in the image, based on the pixel's characteristics and its relationship to other pixels in the image.\n",
    "  \n",
    "Codebase: https://github.com/divamgupta/image-segmentation-keras  \n",
    "Research paper: https://arxiv.org/abs/1505.04597  \n",
    "\n",
    "### __3. Single Shot MultiBox Detector (SSD)__  \n",
    "This is a single-shot object detection system that uses a deep convolutional neural network to predict object class scores and bounding box offsets. The system is fast and efficient, making it well-suited for real-time object detection tasks.\n",
    "\n",
    "Codebase: https://github.com/weiliu89/caffe/tree/ssd  \n",
    "Research paper: https://arxiv.org/abs/1512.02325    \n",
    "        \n",
    "### __4. Pose Estimation__\n",
    "This involves estimating the pose (position and orientation) of an object in a given image.   \n",
    "It is typically used to determine the orientation of human bodies, faces or objects, and is widely used in applications such as augmented reality, gaming, and human-computer interaction.\n",
    "\n",
    "Codebase: https://github.com/CMU-Perceptual-Computing-Lab/openpose  \n",
    "Research paper: https://arxiv.org/abs/1812.08008  \n",
    "\n",
    "### __5. Face Detection__  \n",
    "This involves identifying and locating human faces in images or videos.   \n",
    "It is a crucial component in many facial recognition systems, and often the first step in processing facial images.\n",
    "\n",
    "Codebase: https://github.com/opencv/opencv/blob/master/samples/dnn/face_detector  \n",
    "Research paper: https://arxiv.org/abs/1512.02325  \n",
    "        \n",
    "### __6. Lane Detection__  \n",
    "This involves identify the lanes and markings on roads in images and videos, typically used in self-driving car applications to assist the navigation. It involves detecting the boundaries of lanes and classifying them as either driving or non-driving lanes to provide real-time lane guidance.\n",
    "\n",
    "Codebase: https://github.com/udacity/CarND-LaneLines-P1  \n",
    "Research paper: https://ieeexplore.org/abstract/document/7960982  \n",
    "\n",
    "### __7. Optical Flow__  \n",
    "\n",
    "This involves calculating the motion of objects and pixels between consecutive frames in a video. It is used in various applications such as video compression, action recognition, and autonomous driving.\n",
    "\n",
    "Codebase: https://github.com/opencv/opencv/blob/master/samples/python/opt_flow.py  \n",
    "Research paper: https://ieeexplore.org/abstract/document/731667   \n",
    "        \n",
    "### __8. DeepLab__\n",
    "This is a series of deep convolutional neural networks for semantic image segmentation.   \n",
    "The systems use atrous convolutions and conditional random fields to generate high-quality segmentation masks.\n",
    "\n",
    "Codebase: https://github.com/tensorflow/  \n",
    "        \n",
    "### __9. Object Tracking SORT (Simple Online and Realtime Tracking)__   \n",
    "This involves locating and tracking objects over time in a video stream.    \n",
    "It is typically accomplished by using computer algorithms to analyze sequential frames of the video and determine the object's position and trajectory in each frame.\n",
    "\n",
    "Explanation: Object Tracking using Kalman Filter and Hungarian Algorithm  \n",
    "Source code: https://github.com/abewley/sort  \n",
    "Research paper: https://arxiv.org/abs/1602.00763    \n",
    "        \n",
    "### __10. Image Classification using Deep Convolutional Neural Networks__    \n",
    "This involves assigning a label to an input image, based on its visual content.   \n",
    "It involves training a machine learning model on a large dataset of labeled images and using it to predict the class of new, unseen images.\n",
    "\n",
    "Source code: https://github.com/pytorch/examples/tree/master/imagenet  \n",
    "Research Paper: https://arxiv.org/pdf/1512.03385.pdf    \n",
    "        \n",
    "### __11. Car License Plate Detection (using Deep Learning based Object Detection algorithms)__\n",
    "This involves locating and extracting the license plate information from a given car image.   \n",
    "The extracted information can then be used for various purposes such as vehicle identification and tracking.\n",
    "\n",
    "Source code: https://github.com/openalpr/openalpr, https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Cpp  \n",
    "Research Paper: \"A Robust Real-time Automatic License Plate Recognition based on the YOLO Detector\" by J. Kim et al., 2019, https://arxiv.org/abs/1901.05079  \n",
    "\"Real-time vehicle license plate detection and recognition using deep learning\" by M. Khan et al., 2018, https://ieeexplore.org/abstract/document/8460597/    \n",
    "    \n",
    "### __12. Viola-Jones Algorithm__  \n",
    "This is a classic computer vision method for detecting faces in images and video.   \n",
    "The system uses a cascade of simple Haar-like features to efficiently detect faces in real-time.  \n",
    "\n",
    "Codebase: https://github.com/opencv/opencv/tree/master/data/haarcascades  \n",
    "Research paper: https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf  \n",
    "        \n",
    "### __13. Multi-task Cascaded Convolutional Networks (MTCNN)__\n",
    "This is a face detection system that uses cascaded convolutional networks to perform face detection, facial landmark localization, and facial attribute analysis.\n",
    "\n",
    "Codebase: https://github.com/kpzhang93/MTCNN_face_detection_alignment  \n",
    "Research paper: https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf  \n",
    "        \n",
    "### __14. Super-Resolution (using Deep Convolutional Neural Networks)__\n",
    "This increases the resolution of an image while preserving its important details.   \n",
    "It can be achieved using deep learning models that learn the mapping between low and high resolution images, generating a high-resolution image from a low-resolution one as output.  \n",
    "\n",
    "Source code:\n",
    "\n",
    "SRResNet: https://github.com/twtygqyy/pytorch-SRResNet  \n",
    "EDSR: https://github.com/thstkdgus35/EDSR-PyTorch  \n",
    "RCAN: https://github.com/yulunzhang/RCAN  \n",
    "Research paper:  \n",
    "\n",
    "SRResNet: https://arxiv.org/abs/1609.04802  \n",
    "EDSR: https://arxiv.org/abs/1707.02921  \n",
    "RCAN: https://arxiv.org/abs/1707.01594  \n",
    "\n",
    "### __15. Image Restoration__  \n",
    "This involves removing degradation such as noise, blur, or over-exposure from an image, to enhance its quality and make it more visually appealing. This is achieved by using techniques such as denoising, deblurring, and inpainting to undo the effects of degradation and recover the original image content.  \n",
    "\n",
    "Explanation: Image restoration involves removing degradation and improving the visual quality of an image, such as removing noise, blurring, or removing defects.  \n",
    "Source code: https://github.com/SaoYan/Image-Restoration  \n",
    "Research Paper: Image Restoration Using Convolutional Neural Networks (2017), https://arxiv.org/abs/1707.06841\n",
    "\n",
    "### __16. Scene Understanding__  \n",
    "This involves recognizing and categorizing different elements in a scene (such as objects, people, and environment) to gain a deeper understanding of the context and meaning of an image. It often involves multiple computer vision tasks, including object detection, image segmentation, and semantic segmentation.  \n",
    "\n",
    "Source code: https://github.com/charlesq34/pointnet  \n",
    "Research paper:  \n",
    "\n",
    "PointNet: https://arxiv.org/abs/1612.00593  \n",
    "SceneNet RGB-D: https://arxiv.org/abs/1707.01302  \n",
    "SPADE: https://arxiv.org/abs/1903.07291 \n",
    "        \n",
    "### __17. Action Recognition__  \n",
    "This involves identifying and classifying human actions within video data.   \n",
    "It is commonly used in surveillance, sports analysis and human-computer interaction applications.  \n",
    "\n",
    "Codebase: https://github.com/axelbarroso/C3D-keras  \n",
    "Research Paper:    \n",
    "\"Two-Stream Convolutional Networks for Action Recognition in Videos\" by Simonyan and Zisserman (2014).    \n",
    "\"Learning Spatiotemporal Features with 3D Convolutional Networks\" by Tran et al. (2015).    \n",
    "\n",
    "### __18. Image Style Transfer__\n",
    "This involves transforming an input image to have the same style as a reference image, while retaining its content. It is often achieved using Convolutional Neural Networks (CNNs) trained on large datasets of style images.    \n",
    "\n",
    "Source code: https://github.com/cysmith/neural-style-tf  \n",
    "Research Paper: A Neural Algorithm of Artistic Style, Leon A. Gatys et al. (2015) https://arxiv.org/abs/1508.06576  \n",
    "        \n",
    "### __19. Gaze Estimation__  \n",
    "This involves predicting the direction a person is looking based on their eye and head movements.   \n",
    "It is used to understand user attention and to provide interactive control in human-computer interaction systems.  \n",
    "\n",
    "Explanation: Gaze Estimation is the process of predicting the direction of a person's gaze based on image or video data. This can be useful for various applications, such as assistive technologies for people with disabilities, human-computer interaction, and user attention analysis.  \n",
    "Codebase:  \n",
    "\n",
    "GazeNet (TensorFlow): https://github.com/NandaMohammed/GazeNet  \n",
    "DeepGaze II (MATLAB): https://github.com/mpatacchiola/deepgaze  \n",
    "MPIIGaze (MATLAB): https://github.com/mpatacchiola/gaze-estimation  \n",
    "Research Papers:  \n",
    "\n",
    "GazeNet: https://arxiv.org/abs/1710.05987  \n",
    "DeepGaze II: https://ieeexplore.org/abstract/document/7888061/  \n",
    "MPIIGaze: https://ieeexplore.org/abstract/document/7645456/  \n",
    "        \n",
    "### __20. Image Generation__  \n",
    "In this, we generate new images from a given input, typically a noise vector or a sample from a prior distribution. It aims to capture the underlying structure and patterns of an image dataset to generate new, diverse images that are similar in style and content to the training data.  \n",
    "\n",
    "\"Practical Deep Image Generation with GANs\" - Code: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples\n",
    "Research Paper: https://arxiv.org/abs/1807.04720  \n",
    "\n",
    "\"DCGAN: Deep Convolutional Generative Adversarial Networks\" - Code: https://github.com/carpedm20/DCGAN-tensorflow  \n",
    "Research Paper: https://arxiv.org/abs/1511.06434  \n",
    "\n",
    "\"Wasserstein GAN\" - Code: https://github.com/martinarjovsky/WassersteinGAN  \n",
    "Research Paper: https://arxiv.org/abs/1701.07875  \n",
    "\n",
    "\"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks\" - Code: https://github.com/eriklindernoren/PyTorch-GAN  \n",
    "Research Paper: https://arxiv.org/abs/1511.06434    \n",
    "  \n",
    "\"Progressive Growing of GANs for Improved Quality, Stability, and Variation\" - Code: https://github.com/tkarras/progressive_growing_of_gans  \n",
    "Research Paper: https://arxiv.org/abs/1710.10196  \n",
    "\n",
    "### __21. Image Captioning__ \n",
    "This is a task in computer vision where a textual description is generated for an input image, aimed at explaining its content to a human reader.   \n",
    "It uses deep learning models to learn the mapping between image and textual representations.\n",
    "Image Captioning involves generating textual description of an image.   \n",
    "It is a task in computer vision and natural language processing, and is typically performed using Encoder-Decoder models or Attention-based models.  \n",
    "\n",
    "Codebase:\n",
    "\n",
    "PyTorch Tutorial on Image Captioning: https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/03-advanced/image_captioning  \n",
    "TensorFlow Tutorial on Image Captioning: https://github.com/tensorflow/models/tree/master/research/im2txt  \n",
    "Research Paper:\n",
    "\n",
    "\"Show, Attend and Tell: Neural Image Caption Generation with Visual Attention\" (2015) by Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel: https://arxiv.org/abs/1502.03044    \n",
    "\"Are Neural Image Captions Better than Humans at Describing Images?\" (2017) by Ting Yao, Yaoxiong Li, Fan Ma, Jianping Shen, Zhiheng Huang, Wei Liu, Ting Liu: https://arxiv.org/abs/1705.04875    \n",
    "\n",
    "### __22. Disease detection CV projects__\n",
    "Here are some disease detection projects in Computer Vision and their codebase/research paper links:\n",
    "\n",
    "Plant Disease Detection:\n",
    "Codebase: https://github.com/hardikvasa/image-classification-keras  \n",
    "Research Paper: https://ieeexplore.org/abstract/document/8926551  \n",
    "\n",
    "Skin Lesion Analysis for Melanoma Detection:  \n",
    "Codebase: https://github.com/parhambarazesh/Skin-Lesion-Analysis  \n",
    "Research Paper: https://arxiv.org/abs/1907.03380  \n",
    "\n",
    "Diabetic Retinopathy Detection:  \n",
    "Codebase: https://github.com/EyePainter/Diabetic-Retinopathy-Detection  \n",
    "Research Paper: https://www.sciencedirect.com/science/article/pii/S2213177917304780  \n",
    "\n",
    "Pneumonia Detection using Chest X-rays:  \n",
    "Codebase: https://github.com/akshaybahadur21/Pneumonia-Detection  \n",
    "Research Paper: https://ieeexplore.ieee.org/document/8869364  \n",
    "\n",
    "Lung Nodule Detection and Classification:\n",
    "Codebase: https://github.com/tanaymukherjee/Lung-Nodule-Detection-Classification  \n",
    "Research Paper: https://ieeexplore.org/abstract/document/8661197  \n",
    "\n"
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   "source": [
    "## Ke-Sen Huang's Home Page  \n",
    "\n",
    "https://www.kesen.realtimerendering.com/"
   ]
  }
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