Deep-Learning

This article is a continuation of the previous article on Semantic Image Segmentation in the browser using TensorFlow.js pre-trained DeepLab model. Here, we will use the DeepLab model’s output to manipulate specific parts of the image based on user selection. Essentially, we will allow the user to remove or restore a specific segment of an image that corresponds to an object class identified by the model. ...

In Computer Vision, one of the main challenges is to understand an image at a higher level like the way we humans do, i.e., to identify the objects, their location and their surroundings to make sense of the scene captured in the image. Semantic Image Segmentation is the process of identifying the objects present in the image and labelling each pixel by associating it to an identified object class. Semantic Image Segmentation can be applied to a wide variety of use cases such as human body segmentation, separating the background from foreground in an image, and understanding the surroundings for self-driving cars, just to name a few. ...

This article is the continuation of the previous article on MediaPipe Face Mesh model in TensorFlow.js, where we looked at creating the triangle mesh of the face using the model’s output. Here, we will look at detecting and tracking iris within the eyes using the MediaPipe Iris model. 1. Iris Detection MediaPipe Iris model accurately estimates the iris landmarks within the eye and tracks it. Iris detection & tracking can be used in augmented reality to create avatars and also to determine the distance of the camera to the user. ...