Image segmentation is a technique for breaking up a digital image into smaller groupings called image segments, which reduces the complexity of the image and makes each segment more easily processed or analyzed. Technically, segmentation is the process of giving labels to pixels in a picture in order to distinguish between objects, persons, or other significant aspects.
Object detection is a frequent use of picture segmentation. It is usual practice to initially apply an image segmentation method to discover things of interest in the picture before processing the complete image. The object detector may then work with a bounding box that the segmentation algorithm has previously established. By stopping the detector from analyzing the full picture, accuracy is increased and inference time is decreased.
A crucial component of computer vision technologies and algorithms is image segmentation. It is employed in a variety of real-world contexts, including as face identification and recognition in video surveillance, medical image analysis, computer vision for autonomous cars, and satellite image analysis.
How Does It Work?
Image segmentation is a function that creates an output from input images. In the output, each pixel’s object class or instance is specified by a mask or matrix with numerous components.
For picture segmentation, a number of pertinent heuristics, or high-level image attributes, might be helpful. Standard image segmentation algorithms, which include grouping methods like edges and histograms, are built on these attributes.
Color is an illustration of a common heuristic. In order to provide a uniform color for the picture backdrop, graphic designers may utilize a green screen. This allows for automated background detection and replacement during post-processing.
Contrast is another example of a helpful heuristic—programs for picture segmentation can readily tell apart between a dark object and a bright backdrop (such as the sky). Based on sharply contrasting values, the algorithm determines pixel boundaries.
Even though traditional picture segmentation methods based on these heuristics might be quick and easy, they frequently need a lot of fine-tuning to accommodate particular use cases using manually created heuristics. They are not usually precise enough to be used with complicated imagery. Deep learning and machine learning are used in more recent segmentation methods to improve flexibility and accuracy.