Results 51 to 60 of about 471,167 (221)

Superpixel Classification Based Optic Cup Segmentation [PDF]

, 2013
In this paper, we propose a superpixel classification based optic cup segmentation for glaucoma detection. In the proposed method, each optic disc image is first over-segmented into superpixels. Then mean intensities, center surround statistics and the location features are extracted from each superpixel to classify it as cup or non-cup.
Jun, Cheng, Jiang, Liu, Dacheng, Tao, Fengshou, Yin, Damon Wing Kee, Wong, Yanwu, Xu, Tien Yin, Wong   +6 more
openaire   +2 more sources

A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC

Applied Sciences, 2018
An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed.
Wei Zhao, Haodi Zhang, Yujin Yan, Yi Fu, Hai Wang   +4 more
doaj   +1 more source

AINet: Association Implantation for Superpixel Segmentation

IEEE International Conference on Computer Vision, 2021
Recently, some approaches are proposed to harness deep convolutional networks to facilitate superpixel segmentation. The common practice is to first evenly divide the image into a pre-defined number of grids and then learn to associate each pixel with ...
Yaxiong Wang, Yunchao Wei, Xueming Qian, Li Zhu, Yi Yang   +4 more
semanticscholar   +1 more source

Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data

, 2016
This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data.
AS Brahmachari, DG Lowe, E Serradell, H Isack, H Wang, J Shen, MA Fischler, PE Hart, Pushmeet Kohli, QH Tran, R Achanta, R Tennakoon, S Mittal   +12 more
core   +1 more source

BACA: Superpixel Segmentation with Boundary Awareness and Content Adaptation

Remote Sensing, 2022
Superpixels could aggregate pixels with similar properties, thus reducing the number of image primitives for subsequent advanced computer vision tasks.
N. Liao, Baolong Guo, Cheng Li, Hui Liu, Chaoyan Zhang   +4 more
semanticscholar   +1 more source

A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification

Remote Sensing, 2022
Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel.
Jili Sun, Lingdong Geng, Yize Wang
doaj   +1 more source

Semi-Supervised Hierarchical Semantic Object Parsing

, 2017
Models based on Convolutional Neural Networks (CNNs) have been proven very successful for semantic segmentation and object parsing that yield hierarchies of features.
Amindavar, Hamidreza, Mirakhorli, Jalal
core   +1 more source

Adaptive Superpixel Segmentation With Non-Uniform Seed Initialization

IEEE Transactions on Big Data
Superpixel segmentation is a powerful image pre-processing tool in computer vision applications. However, fewer superpixel segmentation methods consider automatically determining the number of initial superpixels.
Xinlin Xie, Jing Fan, Xinying Xu, Gang Xie   +3 more
semanticscholar   +1 more source

A deep learning‐enabled toolkit for the 3D segmentation of ventricular cardiomyocytes

The Journal of Physiology, EarlyView.
Abstract figure legend 3D cardiomyocyte segmentation enables comprehensive analyses of myocardial microstructure in health and disease; however, it is technically demanding. We present an open‐source toolkit for this task, which reduces challenges associated with sample preparation, image restoration, segmentation and proofreading.
Joachim Greiner, Fabio Frangiamore, Frédéric Sonak, Josef Madl, Thomas Seidel, Peter Kohl, Eva A. Rog‐Zielinska   +6 more
wiley   +1 more source

Robust Active Contour Model for Image Segmentation Using a Probability Density Function Approach

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper proposes an active contour model‐based image segmentation algorithm using the probability density function. Initially, the probability density function is defined by the local mean and variance. Next, a length penalty term and a distance regularization term are incorporated.
XinChao Meng, Si Si, Pei Zhang
wiley   +1 more source