Efficient Graph-Based Image Segmentation
TLDR
An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.Abstract:
This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.read more
Citations
More filters
Proceedings ArticleDOI
Learning object relationships via graph-based context model
TL;DR: A context link view of contextual knowledge, where the relationship between a pair of annotated regions is represented as a context link on a similarity graph of regions, for modeling image-dependent contextual relationships using graph-based context model.
Journal ArticleDOI
Assessing Passive and Active Solar Energy Resources in Cities Using 3D City Models
TL;DR: In this paper, the photovoltaic potential has been calculated and compared with the electricity demand to establish the PV fraction, and the passive solar gains were simulated for each building in the city quarter to analyse the solar contribution for heating demand reduction.
Proceedings ArticleDOI
Graph cut with ordering constraints on labels and its applications
TL;DR: It is observed that the commonly used graph-cut based alpha-expansion is more likely to get stuck in a local minimum when ordering constraints are used, so order-preserving moves are developed, which are developed and used for certain simple shape priors in graphcut segmentation.
Proceedings ArticleDOI
Accurate 3D ground plane estimation from a single image
TL;DR: This work proposes algorithms to accurately estimate the 3D location of the landmarks from the robot only from a single image taken from its on board camera, which differs from previous efforts in this domain.
Journal ArticleDOI
Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces
TL;DR: It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure.
References
More filters
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Proceedings ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
TL;DR: A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting several kinds of cluster structure in arbitrary point sets; description of the detected clusters is possible in some cases by extensions of the method.