Object-Detection

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. ...

I have often wondered if I can build a tool that can accept custom queries to filter photos and images based on the items present in the image. There are some popular apps such as Google Photos, OneDrive, etc. which provide this sort of functionality to various degrees but they don’t necessarily let the user enter custom queries to filter images. In this article, we will look at utilizing TensorFlow.js pre-trained COCO-SSD object detection model to categorize and filter images. ...

Most of us interested in TensorFlow.js would be familiar with the popular demo of disappearing people in real time by Jason Mayes. This is a fantastic example of what is possible using TensorFlow.js in the browser. Jason is gracious to share the code of this application for others to learn and implement their own versions of such applications. In this article, I will attempt to document and walkthrough this code, so that it helps anyone who is trying to understand the code for this app. ...

In this article, we will be building a simple app to detect objects on an image using TensorFlow.js library. To do this we will make use of the TensorFlow.js pre-trained “coco-ssd ” model. In this example, user will browse and select an image file to identify objects present in that image. This selected image file is only handled on the client side within the browser and not uploaded to any server. ...