Xavier Cufí

Yet Another Survey on Image Segmentation: Region and Boundary Information Integration

Image segmentation has been, and still is, a relevant research area in Computer Vision, and hundreds of segmentation algorithms have been proposed in the last 30 years. However, it is well known that elemental segmentation techniques based on boundary or region information often fail to produce accurate segmentation results. Hence, in the last few years, there has been a tendency towards algorithms which take advantage of the complementary nature of such information. This paper reviews different segmentation proposals which integrate edge and region information and highlights 7 different strategies and methods to fuse such information. In contrast with other surveys which only describe and compare qualitatively different approaches, this survey deals with a real quantitative comparison. In this sense, key methods have been programmed and their accuracy analyzed and compared using synthetic and real images. A discussion justified with experimental results is given and the code is available on Internet.

Strategies for image segmentation combining region and boundary information

Image segmentation has been, and still is, an important research area in Computer Vision, and hundreds of segmentation algorithms have been proposed in the last 30 years. However, elementary segmentation techniques based on either boundary or region information often fail to produce accurate segmentation results on their own. In the last few years, there has therefore been a trend towards algorithms that take advantage of their complementary nature. This paper reviews various segmentation proposals that integrate edge and region information and highlights different strategies and methods for fusing such information. The key objective is to point out the advantages and disadvantages of the various approaches, as well as to comment upon new and interesting ideas that perhaps have not been properly exploited.

A review of image segmentation techniques integrating region and boundary information

Publisher Summary This chapter reviews the most current and most relevant segmentation techniques that integrate region and boundary information. It focuses on the strategy performed to carry out the integration process. In this sense, a classification of cooperative segmentation techniques is proposed and several algorithms are described to point out their strengths and weaknesses. Concerning the strategies for the integration of edge information and region information, it is obvious that there are still methods to be explored. In this sense, a hybrid strategy between embedded and postprocessing may be a solution for some of the weak points. A basic scheme of such an idea is presented in the chapter where an algorithm based on an embedded strategy produces an initial result that is a posteriori refined by a postprocessing fusion with boundary information.

An approach to vision-based station keeping for an unmanned underwater vehicle

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented.

Fourier-based Registration for Robust Forward-looking Sonar Mosaicing in Low-visibility Underwater Environments

Vehicle operations in underwater environments are often compromised by poor visibility conditions. For instance, the perception range of optical devices is heavily constrained in turbid waters, thus complicating navigation and mapping tasks in environments such as harbors, bays, or rivers. A new generation of high-definition forward-looking sonars providing acoustic imagery at high frame rates has recently emerged as a promising alternative for working under these challenging conditions. However, the characteristics of the sonar data introduce difficulties in image registration, a key step in mosaicing and motion estimation applications. In this work, we propose the use of a Fourier-based registration technique capable of handling the low resolution, noise, and artifacts associated with sonar image formation. When compared to a state-of-the art region-based technique, our approach shows superior performance in the alignment of both consecutive and nonconsecutive views as well as higher robustness in featureless environments. The method is used to compute pose constraints between sonar frames that, integrated inside a global alignment framework, enable the rendering of consistent acoustic mosaics with high detail and increased resolution. An extensive experimental section is reported showing results in relevant field applications, such as ship hull inspection and harbor mapping.

Shape-based feature selection for microcalcification evaluation

This work focuses on the selection of a set of shape-based features in order to assist radiologists in differentiating between malignant and benignant clustered microcalcifications in mammograms. The results obtained allow the creation of a model for the evaluation of the benignant or malignant character of the microcalcifications in a mammogram, based exclusively on the following parameters: number of clusters, number of holes, area, Feret elongation, roughness and elongation. The performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in this field. Additionally, the work is based on an unpublished database formed by patients of the Regional Health Area of Girona, which in the future may contribute to increase the digital mammogram databases.

Long-term mapping and localization using feature stability histograms

This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term runs.

GARBI: a low-cost underwater vehicle

Abstract The progress on robotics, computer technology, sensors and perception systems has produced great advances in the field of underwater robotics, in the research and development of both autonomous ROVs and AUVs. Unfortunately, this progress has driven to the use of very expensive devices and, consequently, the development of high-cost underwater vehicles. As an alternative approach, the GARBI underwater robot has been designed as a low-cost ROV developed for underwater applications in shallow water environments. This paper describes the underwater vehicle and the endowed systems, the robots goals and its main applications.

Recovering camera motion in a sequence of underwater images through mosaicking

A procedure for automatic mosaic construction over long image sequences is presented. This mosaic is used by an underwater vehicle to estimate its motion with respect to the ocean floor. The system exploits texture cues to solve the correspondence problem. The dynamic selection of a reference image extracted from the mosaic improves motion estimation, bounding accumulated error. Experiments with real images are reported.

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the regions characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach.