1. Facial KeyPoints Detection:
In this project, knowledge of computer vision techniques and deep learning architectures are applied to build a facial keypoint detection system that takes in any image with faces, and predicts the location of 68 distinguishing keypoints on each face!
Facial keypoints include points around the eyes, nose, and mouth on a face and are used in many applications. These applications include: facial tracking, facial pose recognition, facial filters, and emotion recognition. The implemented code looks at any image, detects faces, and predicts the locations of facial keypoints on each face.
2. Image Captioning :
In this project, a neural network architecture (CNN Encoder- RNN Decoder) is constructed which generates captions from images (text describing the image) automatically.
The model is trained on Microsoft Common Objects in Context (MS COCO) dataset and the network is tested on the novel images.
3. SLAM: Object Tracking and Landmark Detection
In this project, I implemented SLAM (Simultaneous Localization and Mapping) for a two dimensional world and tracked the locations of robot and andmarks in the real time based on the map of the environment created.
To summarize, for simultaneous localization and mapping (SLAM), information is gathered from a robot's sensors and motions over time, and then use that gathered information about measurements and motion to re-construct a map of the world.