1,498 research outputs found
Automatic Image Segmentation by Dynamic Region Merging
This paper addresses the automatic image segmentation problem in a region
merging style. With an initially over-segmented image, in which the many
regions (or super-pixels) with homogeneous color are detected, image
segmentation is performed by iteratively merging the regions according to a
statistical test. There are two essential issues in a region merging algorithm:
order of merging and the stopping criterion. In the proposed algorithm, these
two issues are solved by a novel predicate, which is defined by the sequential
probability ratio test (SPRT) and the maximum likelihood criterion. Starting
from an over-segmented image, neighboring regions are progressively merged if
there is an evidence for merging according to this predicate. We show that the
merging order follows the principle of dynamic programming. This formulates
image segmentation as an inference problem, where the final segmentation is
established based on the observed image. We also prove that the produced
segmentation satisfies certain global properties. In addition, a faster
algorithm is developed to accelerate the region merging process, which
maintains a nearest neighbor graph in each iteration. Experiments on real
natural images are conducted to demonstrate the performance of the proposed
dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI
Image segmentation evaluation using an integrated framework
In this paper we present a general framework we have developed for running and evaluating automatic image and video segmentation algorithms. This framework was designed to allow effortless integration of existing and forthcoming image segmentation algorithms, and allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding and visualization. We then utilize this framework to automatically evaluate four distinct segmentation algorithms, and present and discuss the results and statistical findings of the experiment
On morphological hierarchical representations for image processing and spatial data clustering
Hierarchical data representations in the context of classi cation and data
clustering were put forward during the fties. Recently, hierarchical image
representations have gained renewed interest for segmentation purposes. In this
paper, we briefly survey fundamental results on hierarchical clustering and
then detail recent paradigms developed for the hierarchical representation of
images in the framework of mathematical morphology: constrained connectivity
and ultrametric watersheds. Constrained connectivity can be viewed as a way to
constrain an initial hierarchy in such a way that a set of desired constraints
are satis ed. The framework of ultrametric watersheds provides a generic scheme
for computing any hierarchical connected clustering, in particular when such a
hierarchy is constrained. The suitability of this framework for solving
practical problems is illustrated with applications in remote sensing
Merging toward natural clusters
To findout how many clusters exist in a sample set is an old yet unsolved problem in unsupervised clustering. This problem inevitably occurs in region merging/growing, a well studied and popular technique in image segmentation. Region merging usually needs a stop criterion. The stop criterion is not automatically determined and often has to be set manually to arrive at a sensible segmentation, which is rather difficult for natural images. To address this problem, we present a robust stop criterion that is based on a novel distinctness predicate for adjacent regions. The predicate discerns distinct regions by examining the evidence of the boundary between neighboring regions. Requiring that every region should be distinct from each other, the proposed method is able to choose a stop point where a natural partition is most likely. Under a region merging framework, we demonstrate the effectiveness of the stop criterion using two merging criterion: one based on optimizing a global functional, and another based on a local criterion. Experimental results and comparison are given at the end. © 2009 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio
Hierarchical image segmentation relying on a likelihood ratio test
International audienceHierarchical image segmentation provides a set of image seg-mentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on three well known image databases show efficiency
Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation
International audienceRecent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner
Image Segmentation Using Dynamic Region Merging
In region merging the there are two essential issues first is order of merging and second one is stopping criterion. This work addresses two issues which are solved by Dynamic region merging algorithm which is defined by SPRT and the minimal cost criterion. The process is start from an oversegmented image, then neighboring regions are progressively merged if there is an evidence for merging. The final result is based on the observed image. This algorithm also satisfies the certain global properties of segmentation. In this algorithm region merging process become faster due to nearest neighbor graph in each iteration. The performance of dynamic region merging algorithm is shown on natural images
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