Unlike humans, computers do not have brains that can help them scan, sort, and navigate the environment in real time. Computers must therefore be taught to perceive their surroundings, interpret them and make appropriate decisions mimicking the human brain. Different data annotation techniques can be used to achieve this task, but panoptic segmentation has proven more effective.
With the passing of time, we are edging closer to developing artificial intelligence that can replicate the entire range of human abilities. Panoptic segmentation is one of the techniques that can be used to create data for training machines to separate different objects in an image. Image segmentation is categorized into semantic, instance, and panoptic segmentation, but this article will focus on panoptic segmentation.
Understanding The Concept of Image Segmentation
Understanding panoptic segmentation is easy if you have some background knowledge about image segmentation. However, we will summarize these areas if you have never heard about image segmentation and its different categories.
Image segmentation is assigning a label to identify each pixel in an object. The labels are predefined and represent object classes such as animals, vehicles, etc. There are three image segmentation types: semantic, instance, and panoptic.
What Is Panoptic Segmentation
Panoptic segmentation is rarely talked about as a category of image segmentation. It was introduced in 2018 by Alexander Kirillov. Panoptic segmentation combines the earlier categories of image segmentation to simplify the task of segmenting an image using one simplified method rather than two different approaches.
In panoptic segmentation, each pixel in an image is assigned two labels simultaneously, i.e., each pixel is assigned a label denoting the class of the object and another label to denote the instance number. That means that if there are several cats in an image, the pixels of the first cat would be assigned the class label “cat” and an instance label number “0”. The instance label is then incremented depending on the instance of the object present in the image.
Why Is Panoptic Segmentation Useful?
One of the key reasons why panoptic segmentation is beneficial compared to other image segmentation techniques is its ability to provide an accurate and complete representation of the specific view by including both ‘stuff’ and ‘things’ in the output. In machine learning, “things” in an image are countable objects, including plants, people, cars, animals, etc. “Stuff,” on the other hand, represents all elements that cannot be quantified, including roads, the sky, rivers, etc.
The ability of panoptic segmentation to combine both “stuff” and “things” in the final output while simultaneously assigning a class label and an instance ID provides machines with a better and more comprehensive understanding of images. That improves the accuracy of ML algorithms used in computer vision.
Instance Segmentation Vs. Panoptic Segmentation
Instance segmentation involves assigning each pixel in the image to a particular class and where the objects belonging to the same class are highlighted as separate instances and not lumped together. For instance, in an image with multiple persons, each person is labeled as a separate instance to reflect the difference between each person, i.e., if there are ten people in the image, each person is assigned the label “person” and given an instance identifier starting from number “0” and incremented until the last person is properly annotated.
Panoptic segmentation improves on instance segmentation by combining it with semantic segmentation to form a single, complete and effective image segmentation technique. Instead of simply adding the instances, panoptic segmentation allows each pixel in an object to be labeled with a class and instance ID, simultaneously providing more accurate context to machine learning algorithms.
Semantic Vs. Instance Segmentation
Semantic segmentation involves assigning class labels to every pixel in an image, making it easy for machines to localize different objects in an image. Semantic segmentation puts all similar objects under one class without going into details. For instance, all pixels representing cars are assigned a label car without any further details such as instance.
Instance segmentation is similar to semantic segmentation, although it adds an extra level of detail, making object localization much easier. It involves marking the objects that are similar or belong to the same class as individual instances rather than lumping them together, as in semantic segmentation. For example, in an image with two cars, all pixels in each car are labeled and assigned the class car in addition to a number that denotes the instance of each vehicle, e.g., car1, car2, etc.
Use cases and applications Of Panoptic Segmentation
You can imagine how much panoptic segmentation has skyrocketed accuracy in computer vision applications by providing a comprehensive and detailed view of images and real-time video. Here are a few use cases where panoptic segmentation plays an immense role in taking innovation to the next level.
1. Medical imaging
Combating diseases is one area in the medical field where a clear understanding of the cells is needed. Particularly, it is vital to visualize cells to distinguish between healthy and unhealthy cells properly. Due to the diversity and overlapping nature of cells, it can be a daunting task to detect cells accurately during health examinations, such as when conducting cancer screening.
Previously, semantic models have been the basis of training image AI, but that proved to have problems with overlapping cells. The introduction of panoptic segmentation models combined with deep learning is proving to overcome overlapping cells by providing far more accurate visualization in a manner no previous technology has achieved.
2. Self-driving vehicles
The safety of autonomous vehicles lies in their ability to detect other objects on the road, including vehicles, pedestrians, and cyclists, as well as their general surroundings, including traffic lights, barriers, etc., in real-time and make the appropriate decisions. Panoptic segmentation provides datasets for training autonomous vehicles that are more accurate and provide a clearer picture of “things” and “stuff” in an image. With such datasets, autonomous vehicles, with the help of hardware such as LiDAR sensors and cameras, can perceive, detect and interpret their driving environment more accurately in real time, avoiding accidents.
3. Smart cities
One key characteristic of smart cities is their ability to monitor, control and optimize all aspects of their operations. That includes optimizing their utilities, security, waste management, healthcare, roads, education, etc. Building smart cities is a difficult task requiring high precision and cutting-edge tools that AI and computer vision can provide. Panoptic segmentation can be used to build smart city models that meet design requirements while eliminating chances of design failures.
4. Digital Image Processing
Smartphones have advanced a lot in recent years. Currently, smartphones come with cameras that can capture photos and videos in 4K. However, these cameras need software with pixel-wise comprehension of the people and the photo or video background for appropriate enhancement and amplification. Panoptic segmentation can train such software by leveraging its ability to separate “things” from “stuff”. That is useful in creating effects such as portrait mode, Bokeh mode, auto-focus, and photo manipulation.
In essence, panoptic segmentation combines instance segmentation and semantic segmentation to provide a clear, in-depth output on the full scene of an image or real-time video. The technique does not simply differentiate between “things” or “stuff.” Instead, each pixel in an image is labeled and given an instance ID to provide a more detailed picture. That allows better understanding and interpretation of images by ML and AI models. While it is clear that some other advanced techniques might appear in the future and revolutionize image segmentation, panoptic segmentation remains the best technique currently, which will contribute significantly to object detection in
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