A review on image segmentation techniques

Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.

Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective

A new segmentation algorithm that can be used for robot applications is presented. The input images are dense range data of industrial parts. The image is segmented into a number of surfaces. The segmentation algorithm uses residual analysis to detect edges, then a region-growing technique is used to obtain the final segmented image. The use of the segmentation output for determining the best holdsite position and orientation of objects is studied. As compared to techniques based on intensity images, the use of range images simplifies the holdsite determination. This information can then be used to instruct the robot to grip the object and move it to the required position. The performance of the algorithm on a number of range images is presented. >

Range image segmentation combining edge-detection and region-growing techniques with applications sto robot bin-picking using vacuum gripper

A new approach to range image segmentation is presented. The proposed approach involves two phases in which the region and edge information detected using a set of orthogonal Zernike moment-based operators are combined to provide robust segmentation of range images. In the first phase, each range image point is characterized by the surface normal vector and the depth value at that point. A surface feature-based clustering of range image points yields its initial region-based segmentation. This initial segmentation phase often produces oversegmented images. In the second phase of the proposed technique, the oversegmented image is resegmented by appropriately merging adjacent regions using the edge information to produce final segmentation. One attractive characteristic of the proposed technique is that the same set of three moment-based operators is used to extract both surface and edge features. Thus only three convolution operations are needed at an image point to compute all the desired surface and edge features associated with that point. The performances of the proposed Zernike moment-based operators in surface and edge feature detection are theoretically analyzed. >

Segmentation of range images: an orthogonal moment-based integrated approach

We present an object detection technique that uses local edgels and their geometry to locate multiple objects in a range image in the presence of partial occlusion, background clutter, and depth changes. The fragmented local edgels (key-edgels) are efficiently extracted from a 3D edge map by separating them at their corner points. Each key-edgel is described using our scale invariant descriptor that encodes local geometric configuration by joining the edgel at their start and end points adjacent edgels. Using key-edgels and their descriptors, our model generates promising hypothetical locations in the image. These hypotheses are then verified using more discriminative features. The approach is evaluated on ten diverse object categories in a real-world environment.

Object Detection and Localization in Clutter Range Images Using Edge Features

An algorithm for describing range images based on Mean curvature (H) and Gaussian curvature (K) is presented. Range images are unique in that they directly approximate the physical surfaces of a real world 3-D scene. The curvature parameters are derived from the fundamental theorems of differential geometry and provides visible invariant pixel labels that can be used to characterize the scene. The sign of H and K can be used to classify each pixel into one of eight possible surface types. Due to the sensitivity of these parameters to noise the resulting HK-sing map does not directly identify surfaces in the range images and must be further processed. A region growing algorithm based on modeling the scene points with a Markov Random Field (MRF) of variable neighborhood size and edge models is suggested. This approach allows the integration of information from multiple characteristics in an efficient way. The performance of the proposed algorithm on a number of synthetic and real range images is discussed.

Range data description based on multiple characteristics

The first step in a general 3-D vision system is the segmentation of a digital image into a number of regions that correspond to physical scene surfaces. To compensate for the difficulties associated with edge and region based segmentation methods, we present an integrated approach for range image segmentation. In the first stage we detect jump in the range image. The second stage, involves detecting fold edges using the absolute value of the residual and surface normals. We have used a Bayesian approach for the region growing part of our algorithm. Markov Random Filed is used to model the apriori knowledge information in the Bayesian formulation. A number of experimental results show that this approach is very effective in segmenting a wide variety of range images.

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An integrated approach to segmentation of range images of industrial parts

An algorithm for segmenting range images of industrial parts is presented in this paper Range images, are unique in that they directly approximate the physical surfaces of a real world 3-D scene The segmentation of images ( range or intensity ) is based on edge detection or region growing techniques The algorithm presented in this paper segments the range images by detecting discontinuities There are three types of discontinuities in range images: jump, crease and smooth edges The detection of a jump edge is relatively easy and can be obtained using edge detection techniques used for intensity images The crease and smooth edges are difficult to detect especially in the presence of noise Our approach is based on the analysis of the difference between the input and the filtered images We show that, at an edge, the difference after Gaussian smoothing has a maxima in a direction perpendicular to the edge The close connected regions are then obtained by eroding the image once and an iterative region growing least square fit is used to obtain the final segmented image The performance of the proposed algorithm on a number of range images is presented

Segmentation Of Range Images

Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks such as 3-D object recognition a representation of the object(s) present in the image is derived and then matched with stored models to determine the object(s) identity. Surface based representation is the most widely used representation in range image analysis. To generate surface based representation of an object a segmentation of the object into a number of surfaces is needed. In this paper we present an approach for the segmentation of range images into a number of surfaces that in turn can be used to generate surface based representation. The approach integrates both edge detection and region growing techniques to achieve the segmentation. We start by detecting jump edges. Jump edge map is processed and regions surrounded by jump edges are isolated. Next fold edges are detected iteratively using normals and residual. Fold edge map is processed to obtain the final segmented image. Jump and fold edge maps are processed using a Bayesian approach. The apriori knowledge is modeled using Markov Random Field. For jump edges we have used a coupled line and depth process. For fold edges we have combined line residuals and normals to process the fold edge map. The performance of the algorithm on a number of range images is presented.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Integration of edge- and region-based techniques for range image segmentation

This paper presents an algorithm for the segmentation and classification of dense range images of industrial parts. Range images, are unique in that they directly approximate the physical surfaces of a real world 3-D scene. The segmentation of images (range or intensity) is based on edge detection or region growing techniques. The approach presented in this paper segments range images by combining edge detection and region growing techniques. Jump and roof edges are detected using residual analysis. The residual is defined as the absolute value of the difference between the original image and a filtered version. We show that, at an edge, the difference after smoothing has a maxima in the direction perpendicular to the edge for jump and roof edges. The segmented surfaces is then classified into planar, convex, or concave. The classification is done in two steps. The first step utilizes a variation of the Wald-Wolfowitz runs test to classify the surfaces into planar or curved. The second step further classifies each curved surface into convex or concave using a multi-scale residual computation. The performance of the algorithm on a number of industrial parts range images is presented.

Ezzet H. Al-Hujazi

Papers

Range data description based on multiple characteristics

An integrated approach to segmentation of range images of industrial parts

Segmentation Of Range Images

Integration of edge- and region-based techniques for range image segmentation

Residual Analysis for Range Image Segmentation and Classification