Computer vision (CV) has reached 99% accuracy from 50% inside 10 years. The technology is anticipated to enhance further to an unprecedented level with modern algorithms and image segmentation techniques. Recently, Meta’s FAIR lab has released the Segment Anything Model (SAM) – a game-changer in image segmentation. This advanced model can produce detailed object masks from input prompts, taking computer vision to latest heights. It will probably potentially revolutionize how we interact with digital technology on this era.
Let’s explore image segmentation and briefly uncover how SAM impacts computer vision.
What’s Image Segmentation & What Are its Types?
Image segmentation is a process in computer vision that divides a picture into multiple regions or segments, each representing a unique object or area of the image. This approach allows experts to isolate specific parts of a picture to acquire meaningful insights.
lmage segmentation models are trained to enhance output by recognizing necessary image details and reducing complexity. These algorithms effectively differentiate between different regions of a picture based on features akin to color, texture, contrast, shadows, and edges.
By segmenting a picture, we are able to focus our evaluation on the regions of interest for insightful details. Below are different image segmentation techniques.
- Semantic segmentation involves labeling pixels into semantic classes.
- Instance segmentation goes further by detecting and delineating each object in a picture.
- Panoptic segmentation assigns unique instance IDs to individual object pixels, leading to more comprehensive and contextual labeling of all objects in a picture.
Segmentation is implemented using image-based deep learning models. These models fetch all of the priceless data points and features from the training set. Then, turn this data into vectors and matrices to grasp complex features. Among the widely used deep learning models behind image segmentation are:
How Image Segmentation Works?
In computer vision, most image segmentation models consist of an encoder-decoder network. The encoder encodes a latent space representation of the input data which the decoder decodes to form segment maps, or in other words, maps outlining each object’s location within the image.
Often, the segmentation process consists of three stages:
- A picture encoder that transforms the input image right into a mathematical model (vectors and matrices) for processing.
- The encoder aggregates the vectors at multiple levels.
- A quick mask decoder takes the image embeddings as input and produces a mask that outlines different objects within the image individually.
The State of Image Segmentation
Starting in 2014, a wave of deep learning-based segmentation algorithms emerged, akin to CNN+CRF and FCN, which made significant progress in the sphere. 2015 saw the rise of the U-Net and Deconvolution Network, improving the accuracy of the segmentation results.
Then in 2016, Instance Aware Segmentation, V-Net, and RefineNet further improved the accuracy and speed of segmentation. By 2017, Mark-RCNN and FC-DenseNet introduced object detection and dense prediction to segmentation tasks.
In 2018, Panoptic Segmentation, Mask-Lab, and Context Encoding Networks were at the middle of the stage as these approaches addressed the necessity for instance-level segmentation. By 2019, Panoptic FPN, HRNet, and Criss-Cross Attention introduced latest approaches for instance-level segmentation.
In 2020, the trend continued with the introduction of Detecto RS, Panoptic DeepLab, PolarMask, CenterMask, DC-NAS, and Efficient Net + NAS-FPN. Finally, in 2023, now we have SAM, which we are going to discuss next.
Segment Anything Model (SAM) – General Purpose Image Segmentation
The Segment Anything Model (SAM) is a latest approach that may perform interactive and automatic segmentation tasks in a single model. Previously, interactive segmentation allowed for segmenting any object class but required an individual to guide the tactic by iteratively refining a mask.
Automatic segmentation in SAM allows the segmentation of specific object categories defined ahead of time. Its promotable interface makes it highly flexible. Consequently, SAM can address a wide selection of segmentation tasks using an appropriate prompt, akin to clicks, boxes, text, and more.
SAM is trained on a various and insightful dataset of over 1 billion masks, making it possible to acknowledge latest objects and pictures unavailable within the training set. This contemporary framework will widely revolutionize the CV models in applications like self-driving cars, security, and augmented reality.
SAM can detect and segment objects across the automobile in self-driving cars, akin to other vehicles, pedestrians, and traffic signs. In augmented reality, SAM can segment the real-world environment to put virtual objects in appropriate locations, making a more realistic and interesting UX.
Image Segmentation Challenges in 2023
The increasing research and development in image segmentation also bring significant challenges. Among the foremost image segmentation challenges in 2023 include the next:
- The increasing complexity of datasets, especially for 3D image segmentation
- The event of interpretable deep models
- Using unsupervised learning models that minimize human intervention
- The necessity for real-time and memory-efficient models
- Eliminating the bottlenecks of 3D point-cloud segmentation
The Way forward for Computer Vision
The worldwide computer vision market impacts multiple industries and is projected to achieve over $41 billion by 2030. Modern image segmentation techniques just like the Segment Anything Model coupled with other deep learning algorithms will further strengthen the material of computer vision within the digital landscape. Hence, we’ll see more robust computer vision models and intelligent applications in the longer term.
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