Boundary Extraction in Images Using Hierarchical Clustering-based Segmentation

Hierarchical organization is one of the main characteristics of human segmentation. A human subject segments a natural image by identifying physical objects and marking their boundaries up to a certain level of detail [1]. Hierarchical clustering based segmentation (HCS) process mimics this capability of the human vision. The HCS process automatically generates a hierarchy of segmented images. The hierarchy represents the continuous merging of similar, spatially adjacent or disjoint, regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased. HCS process is unsupervised and is completely data driven. This ensures that the segmentation process can be applied to any image, without any prior information about the image data and without any need for prior training of the segmentation process with the relevant image data. The implementation details of HCS process have been described elsewhere in the author's work [2]. The purpose of the current study is to demonstrate the performance of the HCS process in outlining boundaries in images and its possible application in processing medical images. [1] P. Arbelaez. Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Proceedings 5th IEEE Workshop on Perceptual Organization in Computer Vision (POCV'06). June 2006. New York, USA. [2] A. N. Selvan. Highlighting Dissimilarity in Medical Images Using Hierarchical Clustering Based Segmentation (HCS). M. Phil. dissertation, Faculty of Arts Computing Engineering and Sciences Sheffield Hallam Univ., Sheffield, UK, 2007.</p

Integration of perceptal grouping and depth

International Conference on Pattern Recognition (ICPR), 2000, Barcelona (España)Different data acquisition methods are tailored at extracting particular characteristics from a scene and by combining their results a more robust scene description can be created. A method to fuse perceptual groupings extracted from color-based segmentation and depth information from stereo using supervised classification is presented. The merging of data from these two acquisition modules allows for a spatially coherent blend of smooth regions and detail in an image. Depth cues are used to limit the area of interest in the scene and to improve perceptual grouping solving subsegmentation and oversegmentation of the original images. The complexity of the algorithm does not exceed that of the individual acquisition modules. The resulting scene description can then be fed to an object recognition modules for scene interpretation.This work was supported by the project 'Active vision systems based in automatic learning for industrial applications' ().Peer Reviewe

An improved image segmentation algorithm for salient object detection

Semantic object detection is one of the most important and challenging problems in image analysis. Segmentation is an optimal approach to detect salient objects, but often fails to generate meaningful regions due to over-segmentation. This paper presents an improved semantic segmentation approach which is based on JSEG algorithm and utilizes multiple region merging criteria. The experimental results demonstrate that the proposed algorithm is encouraging and effective in salient object detection

Segmentation of Structured Objects in Image 1

Abstract Detection of foreground structured objects in the images is an essential task in many image processing applications. This paper presents a region merging and region growing approach for automatic detection of the foreground objects in the image. The proposed approach identifies objects in the given image based on general properties of the objects without depending on the prior knowledge about specific objects. The region contrast information is used to separate the regions of the structured objects from the background regions. The perceptual organization laws are used in the region merging process to group the various regions i.e. parts of the object. The system is adaptive to the image content. The results of the experiments show that the proposed scheme can efficiently extract object boundary from the background

Algorithmic Perception of Vertices in Sketched Drawings of Polyhedral Shapes

In this article, visual perception principles were used to build an artificial perception model aimed at developing an algorithm for detecting junctions in line drawings of polyhedral objects that are vectorized from hand-drawn sketches. The detection is performed in two dimensions (2D), before any 3D model is available and minimal information about the shape depicted by the sketch is used. The goal of this approach is to not only detect junctions in careful sketches created by skilled engineers and designers but also detect junctions when skilled people draw casually to quickly convey rough ideas. Current approaches for extracting junctions from digital images are mostly incomplete, as they simply merge endpoints that are near each other, thus ignoring the fact that different vertices may be represented by different (but close) junctions and that the endpoints of lines that depict edges that share a common vertex may not necessarily be close to each other, particularly in quickly sketched drawings. We describe and validate a new algorithm that uses these perceptual findings to merge tips of line segments into 2D junctions that are assumed to depict 3D vertices

Automatic Feature Set Selection for Merging Image Segmentation Results Using Fuzzy Clustering

The image segmentation performance of clustering algorithms is highly dependent on the features used and the type of objects contained in the image, which limits the generalization ability of such algorithms. As a consequence, a fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm was proposed that merged the initially segmented regions produced by a fuzzy clustering algorithm, using two different feature sets each comprising two features from pixel location, pixel intensity and a combination of both, which considered objects with similar surface variations (SSV), the arbitrariness of fuzzy c-means (FCM) algorithm using pixel location and the connectedness property of objects. The feature set selection for the initial segmentation in the merging technique was however, inaccurate because it did not consider all possible feature set combinations and also manually defined the threshold used to identify objects having SSV. To overcome these limitations, a new automatic feature set selection for merging image segmentation results using fuzzy clustering (AFMSF) algorithm is proposed, which considers the best feature set selection and also calculates the threshold based upon human visual perception. Both qualitative and quantitative analysis prove the superiority of AFMSF algorithm compared with other clustering techniques including FSSC, FCM, possibilistic c-means (PCM) and SFCM, for different image types

Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect. Currently computer-aided detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions". The goal of this paper is to explore the possibility of using hierarchical clustering based segmentation (HSC), as a perceptual aid, to improve the performance of the reader

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

Region-based segmentation of images using syntactic visual features

This paper presents a robust and efficient method for segmentation of images into large regions that reflect the real world objects present in the scene. We propose an extension to the well known Recursive Shortest Spanning Tree (RSST) algorithm based on a new color model and so-called syntactic features [1]. We introduce practical solutions, integrated within the RSST framework, to structure analysis based on the shape and spatial configuration of image regions. We demonstrate that syntactic features provide a reliable basis for region merging criteria which prevent formation of regions spanning more than one semantic object, thereby significantly improving the perceptual quality of the output segmentation. Experiments indicate that the proposed features are generic in nature and allow satisfactory segmentation of real world images from various sources without adjustment to algorithm parameters

A Fuzzy Approach to Text Segmentation in Web Images Based on Human Colour Perception

This chapter describes a new approach for the segmentation of text in images on Web pages. In the same spirit as the authors’ previous work on this subject, this approach attempts to model the ability of humans to differentiate between colours. In this case, pixels of similar colour are first grouped using a colour distance defined in a perceptually uniform colour space (as opposed to the commonly used RGB). The resulting colour connected components are then grouped to form larger (character-like) regions with the aid of a propinquity measure, which is the output of a fuzzy inference system. This measure expresses the likelihood for merging two components based on two features. The first feature is the colour distance between the components, in the L*a*b* colour space. The second feature expresses the topological relationship of two components. The results of the method indicate a better performance than previous methods devised by the authors and possibly better (a direct comparison is not really possible due to the differences in application domain characteristics between this and previous methods) performance to other existing methods