Hands-on Training on Image Processing

The session was insightful where Mr. Janarthnan M shared the basics of about Computer Vision and Image Processing.

Day- 1:

Talked about the basics of Image Processing and classic image processing algorithms. Analog and digital image processing are the two techniques used in image processing.

The method for processing pictures, prints, and other tangible reproductions of images is known as analogue image processing. In contrast, digital image processing uses

sophisticated algorithms to manipulate digital images in order to generate information.Briefed about them with the help of presentation. And introduced them in Colab.

Day-2:

Talked about the package FiftyOne, The free software for creating superior datasets and computer vision models. Poor quality data is the biggest obstacle to the success of

machine learning systems. Additionally, enhancing a model might be time-consuming and ineffective without the proper tools. By enabling you to quickly and accurately display

datasets and comprehend models, FiftyOne accelerates your machine learning operations. and explained them in python. Also explained how the dataset can be imported into colab.

Day-3:

Started with the explanation of 3D object detection in colab. The majority of object detection research has been on 2D object detection. These are the some of the models,

RCNN, Fast RCNN, SSD, and Masked RCNN. Real-world environments contain 3D items. Because of this, 3D bounding boxes would be preferable to the often utilized 2D detections for binding things seen in the actual world. We need 3D object detection so that we can record the sizes, orientations, and locations of items in the real world. Since robotics see the environment similarly to humans do, we would be able to apply these 3D detection in practical applications like Augmented Reality (AR), self-driving automobiles,

and robotics. Amazingly, Google has developed a model that perceives the environment and can identify three-dimensional things in the actual world. The Objectron is the name

of this design. Explained the implementation of the project and also enlightened with respect to the dataset provided by Xoriant.

Day-4:

Continued with the 3D object detection and solved the bug with respect to the code of the students, The way object detection works in video is quite similar to how it operates in photos. Such a tool would enable the computer to find, recognise, and categorize things visible in the supplied moving pictures. First things first, reference data must be fed into the machine, So with respect to that the examples were provided. The fact that a video is really a collection of individual pictures (or frames) is one obvious factor for the small imbalance. However, because video processing introduces a new dimension to the problem, the time dimension of this description cannot fully capture what video processing is.Proper guidance for proceeding with respect to the project.

Day-5:

The insights on Pixel Level Scene Segmentation, the various types of image segmentation as there were a set of students provided with the pixel level scene segmentation which explained the various types namely instance, semantic and panoptic where we were guided with proceeding with the semantic segmentation. The project required proper explanations that were guided with the help of various examples through the coding part. Also brief explanations with respect to the topic were given and the basics of the topic were explained using codes in colab. Further guided us about the execution part in the project.