Mask R-CNN Image Segmentation

What does mask R-CNN mean?

Mask R-CNN stands for “Mask Region-based Convolutional Neural Network,” a deep learning model for object detection and segmentation.

How do I mask an image for image segmentation?

Image masking for segmentation involves using techniques like Mask R-CNN to create pixel-wise masks, outlining object boundaries accurately.

What is the difference between mask R-CNN and R-CNN?

Mask R-CNN extends R-CNN by adding an additional branch to predict pixel-wise segmentation masks alongside bounding boxes, enhancing object localization.

What are the advantages of mask R-CNN?

Mask R-CNN excels in precise object localization and segmentation, providing detailed masks, making it suitable for tasks requiring accurate spatial understanding.

What is the difference between mask R-CNN and Yolo?

Mask R-CNN focuses on detailed instance segmentation, providing pixel-level accuracy, while YOLO (You Only Look Once) emphasizes real-time object detection with bounding boxes.

Image segmentation plays a crucial role in computer vision tasks, enabling machines to understand and analyze visual content at a pixel level. It involves dividing an image into distinct regions or objects, facilitating object recognition, tracking, and scene understanding. In this article, we explore three popular image segmentation techniques: Mask R-CNN, GrabCut, and OpenCV.

Let’s understand, What Image Segmentation with Mask R-CNN and GrabCut are?

What is R-CNN?

R-CNN stands for Region-based Convolutional Neural Network. It is a ground-breaking object detection system that combines object localization and recognition into an end-to-end deep learning framework. 

RNN can be summarised in the following ways.

• Region Proposal: Initially, a region proposal algorithm (such as selective search) generates a set of potential bounding box regions in an image that are likely to contain objects of interest. These regions serve as candidate object locations.
• Feature Extraction: Each region proposal is then individually cropped and resized to a fixed size and passed through a pre-trained CNN (such as AlexNet or VGGNet). The CNN extracts high-level features from the region, transforming it into a fixed-length feature vector.
• Classification and Localization: The feature vector obtained from the CNN is fed into separate fully connected layers. The classification layer predicts the probability of different object classes present in the region, while the regression layer refines the coordinates of the bounding box, improving localization accuracy.
• Non-Maximum Suppression (NMS): To eliminate redundant detections, non-maximum suppression is applied. It removes overlapping bounding boxes, keeping only the most confident detection for each object instance.