With the advancement of Artificial Intelligence, the demand for accurate data is proliferating. AI companies are constantly hiring data providers capable of delivering highly efficient and situation-specific tailored datasets. With the availability of various annotation types that enable accurate data recognition and labeling, it becomes difficult for data scientists and ML engineers to choose the perfect one. In computer vision, image segmentation is the most preferred choice when differentiating between objects with the highest degree of accuracy. It enables the precise outline of the entire object irrespective of its unique shape and makes itself eligible for innovative technological advancements. The most popular uses-cases for image segmentation are autonomous vehicles, medical imaging, agricultural AI, etc.
What is Image Segmentation?
Image Segmentation is a computer vision task that partitions an image into various fragments and inscribes a label with each segment. The output of image segmentation is a mask that precisely outlines the entire shape of the object in the image with one or more colors. Each annotated pixel in this process belongs to a single class and ensures pixel-level accuracy for real-world applications. As a result, sometimes, it is also called pixel-level classification. Image segmentation is different from object detection, which draws a bounding box around the image to localize objects; image recognition gives many labels to an entire image. Image segmentation is famous for providing more fine-grain information about the contents of an image. Systems are fed with various datasets that can be manually collected or open source to streamline image segmentation in machine learning.
Types of Image Segmentation
Image segmentation is divided into three categories based on the information extracted from an image.
- Semantic Segmentation
- Instance Segmentation
- Panoptic segmentation
Let's deep dive to explore each category.
Semantic Segmentation is the process of classifying or segmenting a broad boundary of objects in an image into various semantic classes. In this segmentation, without considering any context, just the pixels are organized into a separate category. For example, the semantic segmentation of an image of a crowd depicts the crowd region and classifies it to a "pedestrian" class.
Instance Segmentation provides a segment map for each object it views in the image, without any idea of the object's class. It classifies segments depending on the "instances" instead of classes. And unaware of the class a classified region belongs to, instance segmentation creates a segment map by segregating it with very similar object regions depending on boundaries. For example, the instance segmentation of a crowd image depicts each person separately from the crowd region and the surrounding objects; however, unable to define each object.
Panoptic Segmentation is the most informative segmentation as it depicts each instance of an object in the image and predicts the identity of each object. It combines semantic segmentation and instance segmentation, which provide us with the segment maps for all objects of any particular class present in the image.
Why is Image Segmentation Important?
Image Segmentation ensures the segmentation of an image into various meaningful segments and tells the story behind a scene precisely at a more granular level. Computer vision technologies include image classification to depict what generally an image is, object detection to locate the content of that image, and image segmentation describes the shape of an image object by outlining it precisely. The industries enriched from image segmentation include robotics, medical AI, autonomous vehicles, photo editing and creating tools(like Snapchat, photo lab), etc. The only reason for its popularity is granular image understanding. For example, in photo editing software, segmentation enables you to change the background of your image from a small room to the Taj Mahal by segmenting the foreground and background of your picture.
Use cases of Image Segmentation
Image Segmentation successfully secures its advantageous position in diagnostics and treatment pipelines. It enhances medical images like CT scans and MRI by effectively separating the homogenous areas at a pixel-accurate scale. Medical image processing is adapting a vast number of segmentation processes to detect the pixels of human organs. Although they face some challenges, including some imaging ambiguities like low contrast imagery, some of the most common medical usages include:
- CT scan organ segmentation
- X-Ray segmentation
- Surgical video annotation
- Dental instance segmentation
- Digital pathology cell segmentation
Image segmentation is used in almost every field of robotics, from industrial to agricultural and service. Every branch is dependent. Robots need a pixel-level understanding of a scenario to map and interpret the scene where they want to work, and the image segmentation technique is acting as a service provider. It enables robots to identify each area with sufficient information; the most common robotics usages include:
- Recycling object picking
- Instance segmentation for robotic grasping
- Autonomous navigation and SLAM
The technology used for autonomous vehicles comprises deep learning, computer vision, machine learning, and many other technologies. Image segmentation is a vital component of autonomous vehicles in computer vision. While driving a car, the driver needs to focus on the road, sidewalks, traffic signs, pedestrians, other moving and static vehicles, and many others. Similarly, autonomous vehicle manufacturers have to take care of all these things to ensure safety. In addition, a pixel-level mapping of the world is required by autonomous vehicles to see, interpret, and respond to real-time scenarios—image segmentation enables accuracy to self-driving vehicles and few usages of autonomous vehicles.
- Drivable surface semantic segmentation
- Car and pedestrian instance segmentation
- In-vehicle object detection (items left behind by passengers)
- Potholes Detection and Segmentation
Image Segmentation techniques are used for creative augmentation of visual content, including content creation techniques, video, and image editors. It enables the masking of objects and pixel-level map creation that isolate and separate various segments of images. Creativity usages include:
- Creating a green screen effect.
- Focusing the image's foreground by blurring the background.
- Overlaying various live-stickers on images.
- Cutting different regions from images
- Providing try-on experience in e-commerce sites for choosing the most suited products.
Image Segmentation Techniques
Numerous traditional techniques are available to achieve image segmentation, and each of them suggests a unique approach to get desired output of an image or video. Some famous methods are thresholding, edge detection segmentation, region-based segmentation, and clustering.
Thresholding is one of the easiest and optimal image segmentation methods where the background and foreground of images are contrasted and need to be highlighted. In this technique, the class division depends on a threshold value. Binary classification is used to distinguish the pixels with 0 is assigned to the background or anything with pixel value greater than the threshold, 1 depicts the foreground, and image with pixel value lesser than the threshold results in a binary map by changing the original image into a black-and-white. Algorithms like contour detection and identification can only work on binary images, and they depend on thresholding for image binarization.
Region-Based segmentation algorithms work by recognizing the immediate boundaries of the pixels and analyzing the similarities and differences of adjacent pixels. The probability of belonging to the same object is higher nearest Pixels, and that's why region-based segmentation separates them by defining them as similar or dissimilar. It finds the similarities between adjacent pixels to determine the boundaries of the given object and group them under a standard class. One of the challenges this technique faces is that lighting and contrast within the image reduce the accuracy of the definition of the object parameters.
The edge detection algorithm identifies specific pixels as "edge pixels" in the image and accordingly classifies those edge pixels under a particular class. This algorithm is used to resolve the region-based algorithms challenges by emphasizing the object edges to achieve efficient results and less time-consuming, best suited for objects having cleared outlines with visuals. One of the most popular examples is the Canny edge detection algorithm.
How can Indika AI help?
Images are segmented into different components by our team and then annotated. Our Computer Vision experts detect desired objects within images at the pixel level. We work on various AI use-cases like Medical AI. Our Radiologist-led imaging enrichment and annotation teams process tens of thousands of plain films, CTs, MRIs, and ultrasound images to produce ground truth data for advanced diagnostic technology using Image segmentation.
Our dedicated team is ready and capable of handling any project, regardless of size or complexity. Contact us today!