Computer Vision and Deep learning
Project1 - Facial keypoint detection Use image processing techniques and deep learning techniques to detect faces in an image and find facial keypoints, such as the position of the eyes, nose, and mouth on a face. image processing and feature extraction techniques are used to programmatically represent different facial features. Use knowledge of deep learning techniques to program a convolutional neural network to recognize facial keypoints. Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.
Project2 - Automatic image captioning Combine CNN and RNN knowledge to build a deep learning model that produces captions given an input image. Image captioning requires that creating a complex deep learning model with two components: a CNN that transforms an input image into a set of features, and an RNN that turns those features into rich, descriptive language.
Project3 - Landmark detection and tracking Use feature detection and keypoint descriptors to build a map of the environment with SLAM (simultaneous localization and mapping). Implement a robust method for tracking an object over time, using elements of probability, motion models, and linear algebra. Localization techniques are widely used in autonomous vehicle navigation.