- "Image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects."
A process of dividing an image into multiple segments meaning the pixels that belong to a similar category.
Image Processing: This involves enhancing, filtering, restoring, and manipulating digital images to make them easier to analyze and interpret.
Edge Detection: Edge detection refers to the process of identifying sharp changes in brightness values of an image to determine where objects or boundaries lie.
Thresholding: It is a technique in which an image is converted into a binary image by segmenting the image at a certain threshold value.
Region-based segmentation: This involves grouping adjacent pixels or pixels that share similar characteristics together to form distinct regions.
Contour detection: It is the process of identifying the borders or boundaries of specific regions or objects within an image.
Clustering: Clustering is a process of grouping similar data points into clusters or subsets called segments.
Texture analysis: Texture analysis involves identifying and quantifying patterns of texture and contrast in an image.
Watershed segmentation: It is a method for image segmentation that treats the image as a topographic map, where the water flows to the lowest points and forms boundaries between regions.
Morphological Segmentation: It is a method for segmenting an image based on the shape and size of specific features within the image.
Machine Learning: Machine learning can be used to train image segmentation models that recognize and classify different parts of an image based on labeled datasets.
Object detection: Object detection is the task of detecting and localizing one or more objects in an image or video sequence.
Semantic Segmentation: Semantic segmentation is the process of dividing an image into multiple regions, which are labeled based on the objects or categories within them.
Instance Segmentation: It is a more advanced form of segmentation that not only identifies objects within an image but also separates them into distinct instances.
Deep Learning: It is a subset of machine learning that utilizes neural networks to identify patterns and features within images to perform complex tasks such as image segmentation.
Computer Vision Applications: Segmentation has numerous applications within computer vision, such as object detection, image recognition, medical imaging, and autonomous driving.
Object Segmentation: This type of segmentation focuses on identifying distinct objects in an image and separating them from the background.
Semantic Segmentation: This type of segmentation labels each pixel in an image with a class or category, such as "person", "car", "tree", etc.
Instance Segmentation: This type of segmentation identifies individual objects or instances within a scene and assigns a unique label to each one.
Boundary Segmentation: This type of segmentation identifies the boundaries or edges of objects in an image, usually through the use of edge-detection algorithms.
Depth Segmentation: This type of segmentation estimates the depth or distance of objects in an image, usually through the use of stereo vision or depth sensors.
Time Series Segmentation: This type of segmentation deals with segmenting video sequences into distinct temporal segments.
Region Segmentation: This type of segmentation divides an image into regions based on similarities in color, texture, or other visual features.
Motion Segmentation: This type of segmentation identifies moving objects in a video sequence and separates them from the static background.
Texture Segmentation: This type of segmentation clusters pixels with similar texture properties into separate regions.
Contour Segmentation: This type of segmentation isolates contours or outlines of objects in an image, usually through the use of boundary detection algorithms.
- "The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze."
- "Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images."
- "The process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics."
- "The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image."
- "Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture."
- "Adjacent regions are significantly different color respect to the same characteristic(s)."
- "When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions."
- "Also known as image regions or image objects (sets of pixels)."
- "The representation of an image into something that is more meaningful and easier to analyze."
- "Pixels with the same label share certain characteristics."
- "Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images."
- "The result of image segmentation is a set of segments that collectively cover the entire image.
- "A set of contours extracted from the image."
- "The resulting contours after image segmentation can be used to create 3D reconstructions with the help of geometry reconstruction algorithms like marching cubes."
- "Characteristics or computed property such as color, intensity, or texture."
- "The goal of segmentation is to simplify...the representation of an image into something that is...easier to analyze."
- "When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions."
- "Pixels with the same label share certain characteristics."
- "The resulting contours after image segmentation can be used to create 3D reconstructions with the help of geometry reconstruction algorithms like marching cubes."