Image segmentation
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). 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. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
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- enOriginal image
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- enImage after running k-means with k = 16. Note that a common technique to improve performance for large images is to downsample the image, compute the clusters, and then reassign the values to the larger image if necessary.
- enSource image.
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- enIn digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). 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. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
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- envertical
- Has abstract
- enIn digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). 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. More precisely, image segmentation is 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 (see edge detection). 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 with the help of interpolation algorithms like marching cubes.
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- Image
- enPolarlicht 2 kmeans 16 large.png
- enPolarlicht 2.jpg
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- Image segmentation
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- enImage segmentation
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- web.archive.org/web/20080314011622/http:/instrumentation.hit.bg/Papers/2008-02-02%203D%20Multistage%20Entropy.htm
- www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/2009-icra.pdf
- ipolcore.ipol.im/demo/clientApp/demo.html%3Fid=295
- blogs.mathworks.com/pick/2017/12/07/minimizing-energy-to-segment-images-or-cluster-data/
- web.archive.org/web/20100518124644/http:/csc.fsksm.utm.my/syed/projects/image-processing.html
- www.mathworks.com/discovery/image-segmentation.html
- www.robotics.tu-berlin.de/menue/team/oliver_brock
- rd.springer.com/article/10.1007/s11075-008-9183-x
- disp.ee.ntu.edu.tw/meeting/%E6%98%B1%E7%BF%94/Segmentation%20tutorial.pdf
- www.dubikatz.com
- www.iprg.co.in
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- 3D reconstruction
- Active appearance model
- Active shape model
- Activity recognition
- Algorithm
- Andrew Witkin
- A priori and a posteriori
- Arithmetic mean
- Autoencoder
- Balanced histogram thresholding
- Bayes' theorem
- Biomimetic
- Boundary tracing
- Brightness
- Category:Digital imaging
- Category:Image segmentation
- Chain code
- Clique (graph theory)
- Cluster analysis
- Color
- Color quantization
- Computer vision
- Conditional probability
- Content-based image retrieval
- Contour line
- Convolutional neural network
- CT scan
- Demetri Terzopoulos
- Diffusion MRI
- Digital image
- Digital image processing
- Distance
- Domain knowledge
- Edge detection
- Entropy (information theory)
- Expectation–maximization algorithm
- Face detection
- Face recognition
- Fast marching method
- File:3D CT of thorax.jpg
- File:Model of a segmented femur - journal.pone.0079004.g005.png
- File:MRF neighborhood.png
- File:Sample segmentation HMRF-EM.png
- Fingerprint recognition
- Gestalt psychology
- Global optimum
- Glossary of graph theory
- Graduated optimization
- Graph (data structure)
- Haralick
- Heuristic
- Histogram
- Hue
- Huffman coding
- Image-based meshing
- Image processing
- Image quantization
- Image texture
- Inverse problems
- Iris recognition
- Iterated conditional modes
- Iterative
- Journal of Pattern Recognition Research
- K-means++
- K-means algorithm
- K-means clustering
- Kohonen map
- Lagrangian relaxation
- Lambda-connectedness
- Level-set data structures
- Level-set method
- List of manual image annotation tools
- Livewire Segmentation Technique
- Log-likelihood
- Long short-term memory
- Lossy compression
- Luminița Vese
- Luminous intensity
- Machine vision
- Magnetic resonance imaging
- Marching cubes
- Markov random field
- Maximum a posteriori estimation
- Mean shift
- Medical imaging
- Minimum bounding box
- Minimum description length
- Minimum spanning tree-based segmentation
- Multispectral segmentation
- Multivariate normal distribution
- Mumford–Shah functional
- Neural network
- Object-based image analysis
- Object co-segmentation
- Object detection
- Otsu's method
- Partial differential equation
- Pedestrian detection
- Pixel
- Potts model
- Pulse-coupled networks
- Quadtree
- Random
- Random walker (computer vision)
- Range image segmentation
- Recursion (computer science)
- Region-growing
- Rigid motion segmentation
- Scale space
- Scale-space segmentation
- Segmentation-based object categorization
- Set (mathematics)
- Similarity measure
- Simple Interactive Object Extraction
- Simulated annealing
- Snake (computer vision)
- Split and merge segmentation
- Statistical dispersion
- Texture (computer graphics)
- Thresholding (image processing)
- Undirected graph
- U-Net
- Vector quantization
- Vertex (graph theory)
- Video surveillance
- Video tracking
- Volume rendering
- Watershed (algorithm)
- SameAs
- 4kXCH
- Image segmentation
- Image segmentation
- m.02jj2w
- Phân vùng ảnh
- Q56933
- Segmentação (processamento de imagem)
- Segmentace obrazu
- Segmentación (procesamiento de imágenes)
- Segmentación semántica
- Segmentacja obrazu
- Segmentasi citra
- Segmentatie (digitale beeldverwerking)
- Segmentation d'image
- Segmentazione di immagini
- Segmentierung (Bildverarbeitung)
- Сегментация (обработка изображений)
- Сегментація зображення
- Պատկերի հատվածավորում
- סגמנטציה (מחשוב)
- بخشبندی تصویر
- تقطيع الصورة
- 图像分割
- 영상 분할
- Subject
- Category:Digital imaging
- Category:Image segmentation
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