In and computer vision, image segmentation is the process of partitioning a 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 . 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 geometry reconstruction algorithms like marching cubes.
Some of the practical applications of image segmentation are:
Machine vision
Medical imaging, including volume rendered images from computed tomography and magnetic resonance imaging.
Locate tumors and other pathologies
Measure tissue volumes
Diagnosis, study of anatomical structure
Surgery planning
Virtual surgery simulation
Intra-surgery navigation
Radiotherapy
Object detection
Pedestrian detection
Face detection
Brake light detection
Locate objects in satellite images (roads, forests, crops, etc.)
Recognition Tasks
Face recognition
Fingerprint recognition
Iris recognition
Prohibited Item at Airport security checkpoints
Traffic control systems
Video surveillance
Video object co-segmentation and action localization
Several general-purpose algorithms and techniques have been developed for image segmentation.
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In and computer vision, image segmentation is the process of partitioning a 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.
Image analysis or imagery analysis is the extraction of meaningful information from s; mainly from s by means of techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information.
Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
This course intends to teach image analysis/processing with a strong emphasis
of applications in life sciences. The idea is to enable the participants to solve
image processing questions via workflo
This course intends to teach image analysis/processing with a strong emphasis
of applications in life sciences. The idea is to enable the participants to solve
image processing questions via workflo
Ce cours d'introduction à la microscopie a pour but de donner un apperçu des différentes techniques d'analyse de la microstructure et de la composition des matériaux, en particulier celles liées aux m
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Advances in scanning systems have enabled the digitization of pathology slides into Whole-Slide Images (WSIs), opening up opportunities to develop Computational Pathology (CompPath) methods for comput
Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even thoughthe discriminative power of
EPFL2022
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Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring system. However, the covariate shift between RSI datasets under different capture conditions cannot be allevi