Maria Frucci

OVERSEGMENTATION REDUCTION BY FLOODING REGIONS AND DIGGING WATERSHED LINES

The watershed transformation is a primary tool for segmenting a grey-tone image into subsets that are of interest to a visual observer. The resulting image, however, may often appear oversegmented into a large number of tiny regions (basins), most of which are not significant to the problem of domain. In this paper, a method for removing these nonsignificant basins is presented. The notions of relative significance and intrinsic significance are introduced, which lead to the definition of three types of significance for a basin: strong, weak and partial. The merging of a basin with other basins only occurs when the significance of the basin is not strong, and is restricted to suitably selected adjacent basins. The merging is performed by using an iterated process consisting of two phases. The first involves the removal of certain regional minima, and is accomplished by following either a flooding or a digging scheme. The second identifies the basins corresponding to the regional minima remaining in the image and utilizes the watershed transformation. An appropriate selection of the basins to be merged produces a segmented image perceptually close to the original image. The performance of the proposed method is for the case of astronomic images.

Using resolution pyramids for watershed image segmentation

In this paper we build a shape preserving resolution pyramid and use it in the framework of image segmentation via watershed transformation. Our method is based on the assumption that the most significant image components perceived at high resolution will also be perceived at lower resolution. Thus, we detect the seeds for the watershed transformation at a low resolution, and use them to distinguish significant and non-significant seeds at any selected higher resolution. In this way, the watershed partition obtained at the selected pyramid level will include only the most significant components, and over-segmentation will be considerably reduced. Segmentations of the image at different scales will be available. Moreover, since the seeds can be detected at different pyramid levels, alternative segmentations of the image at a given resolution can be obtained, each characterized by a different level of detail.

Case-Based Reasoning for Image Segmentation by Watershed Transformation

This chapter introduces a novel image-segmentation scheme based on case-based reasoning. Image segmentation is aimed at dividing an image into a number of different regions in such a way that each region is homogeneous with respect to a given property, but the union of any two adjacent regions is not. To reach this goal, a number of different approaches have been suggested in the literature, among which we consider here watershed-based segmentation. The basic idea of this segmentation scheme is to identify in the gray-level image a suitable set of seeds from which to perform a growing process. The growing process groups to each seed all pixels that are closer to that seed more than to any other seed, provided that a certain homogeneity condition is satisfied. Since any segmentation method includes some parameters, whose values depend on the image characteristics, CBR can be profitably used to improve the performance of the adopted segmentation method and to ensure that good segmentation results are obtained even if the segmentation method is applied to images with different characteristics. In practice, CBR will select from a case-base the cases having image characteristics similar to those of the current input image, and will apply to the current image the segmentation parameters associated to the most similar case. Image characteristics will be computed in terms of mean features on the whole image, and a proper similarity measure will be used to select in the case-base the most similar case.

From Segmentation to Binarization of Gray-level Images

For some gray-level images, the boundary between the foreground and the background is perceived in correspondence with the locally maximal changes in gray-level through the image. In this framework, this paper proposes a method to extract the objects of interest from an image and, hence, to distinguish the foreground from the background, starting from a partition of the image obtained by means of watershed transformation. The regions that are assigned to the foreground are also hierarchically ranked, depending on their perceptual relevance, so that different representations of the image are possible.

BIRD: Watershed Based IRis Detection for mobile devices ✩

Communications with a central iris database system using common wireless technologies, such as tablets and smartphones, and iris acquisition out of the field are important functionalities and capabilities of a mobile iris identification device. However, when images are acquired by means of mobile devices under uncontrolled acquisition conditions, noisy images are produced and the effectiveness of the iris recognition system is significantly conditioned. This paper proposes a technique based on watershed transform for iris detection in noisy images captured by mobile devices. The method exploits the information related to limbus to segment the periocular region and merges its score with the iris’ one to achieve greater accuracy in the recognition phase.

Touchless Target Selection Techniques for Wearable Augmented Reality Systems

The paper deals with target selection techniques for wearable augmented reality systems. In particular, we focus on the three techniques most commonly used in distant freehand pointing and clicking on large displays: wait to click, air tap and thumb trigger. The paper details the design of the techniques for a touchless augmented reality interface and provides the results of a preliminary usability evaluation carried out in out-of-lab settings.

WIRE: Watershed based iris recognition

Abstract A Watershed transform based Iris REcognition system (WIRE) for noisy images acquired in visible wavelength is presented. Key points of the system are: the color/illumination correction pre-processing step, which is crucial for darkly pigmented irises whose albedo would be dominated by corneal specular reflections; the criteria used for the binarization of the watershed transform, leading to a preliminary segmentation which is refined by taking into account the watershed regions at least partially included in the best iris fitting circle; the introduction of a new cost function to score the circles detected as potentially delimiting limbus and pupil. The advantage offered by the high precision of WIRE in iris segmentation has a positive impact as regards the iris code, which results to be more accurately computed, so that the performance of iris recognition is also improved. To assess the performance of WIRE and to compare it with the performance of other available methods, two well known databases have been used, specifically UBIRIS version 1 session 2 and the subset of UBIRIS version 2 that has been used as training set for the international challenge NICE II.

CASE-BASED-REASONING FOR IMAGE SEGMENTATION

This paper proposes to use case-based-reasoning for grey-level image segmentation. Different approaches to image segmentation have been proposed in the literature. The selection of the segmentation approach and the assignment of the values to the parameters involved in the selected algorithm depend on image domain and on the specific application. Case-based-reasoning seems a promising way to make the above selection automatic. In this paper, we describe the results of a preliminary study done in this respect. In particular, we refer to the automatic selection of the values of the parameters for a new watershed image segmentation algorithm.

Reversible skeletonization by (5,7,11)-erosion

The skeleton is a useful tool for analyzing the shape of a non trivial planar figure, namely a connected set of pixels which has variable width and protrusions of different size. The skeleton is a connected subset of the figure, centrally placed in it and with the same connectivity order. Its pixels are labeled with their distance from the complement of the figure, and its branches are located in correspondence with the protrusions regarded as significant in the problem domain. To facilitate both figure interpretation and further processing, the skeleton is generally required to be a linear set, i.e., to be the union of simple digital arcs and curves where no pixel can be removed without creating disconnection or shortening.

Experiencing touchless interaction with augmented content on wearable head-mounted displays in cultural heritage applications

The cultural heritage could benefit significantly from the integration of wearable augmented reality (AR). This technology has the potential to guide the user and provide her with both in-depth information, without distracting her from the context, and a natural interaction, which can further allow her to explore and navigate her way through a huge amount of cultural information. The integration of touchless interaction and augmented reality is particularly challenging. On the technical side, the human–machine interface has to be reliable so as to guide users across the real world, which is composed of cluttered backgrounds and severe changes in illumination conditions. On the user experience side, the interface has to provide precise interaction tools while minimizing the perceived task difficulty. In this study, an interactive wearable AR system to augment the environment with cultural information is described. To confer robustness to the interface, a strategy that takes advantage of both depth and color data to find the most reliable information on each single frame is introduced. Moreover, the results of an ISO 9241-9 user study performed in both indoor and outdoor conditions are presented and discussed. The experimental results show that, by using both depth and color data, the interface can behave consistently in different indoor and outdoor scenarios. Furthermore, the results show that the presence of a virtual pointer in the augmented visualization significantly reduces the users error rate in selection tasks.