Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods [PDF]
Remote Sensing, 2016 Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data.
Lei Ma +7 more
doaj +3 more sources
Scale-space for empty catheter segmentation in PCI fluoroscopic images [PDF]
International Journal of Computer Assisted Radiology and Surgery, 2017 In this article, we present a method for empty guiding catheter segmentation in fluoroscopic X-ray images. The guiding catheter, being a commonly visible landmark, its segmentation is an important and a difficult brick for Percutaneous Coronary Intervention (PCI) procedure modeling.In number of clinical situations, the catheter is empty and appears as ...
Ketan Bacchuwar +3 more
semanticscholar +7 more sources
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020 Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI have shown promising performance, but face difficulties in modeling distributions with high fidelity, which is crucial for ...
Christoph Baur +3 more
semanticscholar +6 more sources
Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations [PDF]
IEEE Transactions on Image Processing, 2004 In this paper, we introduce a framework that merges classical ideas borrowed from scale-space and multiresolution segmentation with nonlinear partial differential equations. A non-linear scale-space stack is constructed by means of an appropriate diffusion equation.
Ana Petrović +2 more
semanticscholar +5 more sources
Colour Morphological Scale-Spaces for Image Segmentation [PDF]
Procedings of the British Machine Vision Conference 2005, 2005 Morphological scale-spaces have become an important tool for analysing greyscale images. However, their extension to colour images has proven elusive until recently. In this paper an original evaluation of two recently proposed colour sieves is presented, both algorithmically and in terms of their computational and segmentation performance.
David Gimenez, A.N. Evans
openalex +3 more sources
FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution [PDF]
IEEE International Conference on Robotics and Automation, 2020 Real-time semantic segmentation is desirable in many robotic applications with limited computation resources. One challenge of semantic segmentation is to deal with the object scale variations and leverage the context.
Zhanpeng Zhang, Kaipeng Zhang
semanticscholar +5 more sources
Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
Sensors, 2022 In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields.
Yuan Liu +5 more
doaj +2 more sources
Scale-Equivariant UNet for Histopathology Image Segmentation [PDF]
GEOmedia, 2023 Digital histopathology slides are scanned and viewed under different magnifications and stored as images at different resolutions. Convolutional Neural Networks (CNNs) trained on such images at a given scale fail to generalise to those at different ...
Yi-Lun Yang +2 more
semanticscholar +3 more sources
Image segmentation through the scale-space random walker [PDF]
, 2021 This thesis proposes an extension to the Random Walks assisted segmentation algorithm that allows it to operate on a scale-space. Scale-space is a multi-resolution signal analysis method that retains all of the structures in an image through progressive blurring with a Gaussian kernel.
Richard Rzeszutek
+7 more sources
A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation [PDF]
Int. J. Wavelets Multiresolution Inf. Process., 2014 In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation.
Jérôme Gilles, K. M. Heal
semanticscholar +5 more sources