Object segmentation needs to be driven by top-down knowledge to produce semantically meaningful results. In this paper, we propose a supervised segmentation approach that tightly integrates object-level top down information with low-level image cues. The information from the two levels is fused under a kernelized structural SVM learning framework. We defined a novel nonlinear kernel for comparing two image-segmentation masks. This kernel combines four different kernels: the object similarity kernel, the object shape kernel, the per-image color distribution kernel, and the global color distribution kernel. Our experiments show that the structured SVM algorithm finds bad segmentations of the training examples given the current scoring function and punishes these bad segmentations to lower scores than the example (good) segmentations. The result is a segmentation algorithm that not only knows what good segmentations are, but also learns potential segmentation mistakes and tries to avoid them. Our proposed approach can obtain comparable performance to other state-of-the-art top-down driven segmentation approaches yet is flexible enough to be applied to widely different domains.

In this paper we introduce novel regularization techniques for level set segmentation that target specifically the problem of multiphase segmentation. When the multiphase model is used to obtain a partitioning of the image in more than two regions, a new set of issues arise with respect to the single phase case in terms of regularization strategies. For example, if smoothing or shrinking each contour individually could be a good model in the single phase case, this is not necessarily true in the multiphase scenario. In this paper, we address these issues designing enhanced length and area regularization terms, whose minimization yields evolution equations in which each level set function involved in the multiphase segmentation can “sense” the presence of the other level set functions and evolve accordingly. In other words, the coupling of the level set function, which before was limited to the data term (i.e. the proper segmentation driving force), is extended in a mathematically principled way to the regularization terms as well. The resulting regularization technique is more suitable to eliminate spurious regions and other kind of artifacts. An extensive experimental evaluation supports the model we introduce in this paper, showing improved segmentation performance with respect to traditional regularization techniques.

We introduce a robust image segmentation method based on a variational formulation using edge flow vectors. We demonstrate the nonconservative nature of this flow field, a feature that helps in a better segmentation of objects with concavities. A multiscale version of this method is developed and is shown to improve the localization of the object boundaries. We compare and contrast the proposed method with well known state-of-the-art methods. Detailed experimental results are provided on both synthetic and natural images that demonstrate that the proposed approach is quite competitive.

In this paper, we present an algorithm for occlusion boundary detection. The main contribution is a probabilistic detection framework defined on spatio-temporal lattices, which enables joint analysis of image frames. For this purpose, we introduce two complementary cost functions for creating the spatio-temporal lattice and for performing global inference of the occlusion boundaries, respectively. In addition, a novel combination of low-level occlusion features is discriminatively learnt in the detection framework. Simulations on the CMU Motion Dataset provide ample evidence that proposed algorithm outperforms the leading existing methods.

In this paper we demonstrate the effectiveness of reference (or atlas)-based non-rigid registration for the segmentation of medical and biological imagery. In particular we introduce a segmentation functional exploiting feature information about the reference image and we minimize it with respect to the parameters of the non-rigid transformation, akin to a region-based maximum likelihood estimation process. The warping transformation is modeled using thin plate splines, which incorporate information about the global rigid motion and the non-rigid local displacements. Extensive experimental evaluations and comparisons with other segmentation techniques on a complex biological dataset are presented. The proposed algorithm outperforms the others in both classification rate and, in particular, localization accuracy.

The first major contribution of this paper is a robust method to learn the photometric mapping between the overlapping portions of two registered images acquired either under different lighting conditions or different sensor modalities. Then, once such mapping is learnt, we demonstrate how it generalizes so that the photometric appearance can be transferred from one image to the other out of their overlapping area. This task is fundamental in several different contexts, such as image colorization, seamless mosaicking or change detection. After introducing the theory and discussing the algorithms, we will present several examples that confirm the efficacy of the proposed method in dealing with different types of images.

Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley Segmentation Data Set by comparing its performance with other segmentation techniques.

A variational approach for the background/foreground segmentation of multiple views of the same scene is presented. The main novelty is the introduction of cost functions based on pairwise similarity between pixels across different images. These cost functions are minimized within a level set framework. In addition, a warping model (rigid or non-rigid) between the emerging foregrounds in the different views is imposed, thus avoiding the introduction of a specific shape term in the cost function to handle occlusions. The thin plate spline (TPS) warping is for the first time employed within the level set framework to model non- rigid deformations. The minimization of these cost functions leads to simultaneous segmentation and registration of the different views. Examples of segmentations of a variety of objects are shown and possible applications are proposed.

One of the main challenges in image segmentation is to adapt prior knowledge about the objects/regions that are likely to be present in an image, in order to obtain more precise detection and recognition. Typical applications of such knowledge-based segmentation include partitioning satellite images and microscopy images, where the context is generally well defined. In particular, we present an approach that exploits the knowledge about foreground and background information given in a reference image, in segmenting images containing similar objects or regions. This problem is presented within a variational framework, where cost functions based on pair-wise pixel similarities are minimized. This is perhaps one of the first attempts in using non-shape based prior information within a segmentation framework. We validate the proposed method to segment the outer nuclear layer (ONL) in retinal images. This approach successfully segments the ONL within an image and enables further quantitative analysis.

We introduce a framework for image enhancement, which smooths images while preserving edge information. Domain (spatial) and range (feature) information are combined in one single measure in a principled way. This measure turns out to be the geodesic distance between pixels, calculated on weighted orthogonal domains. The weight function is computed to capture the underlying structure of the image manifold, but allowing at the same time to efficiently solve, using the Fast Marching algorithm on orthogonal domains, the eikonal equation to obtain the geodesic distances. We show promising results in edge-preserving denoising of gray scale, color and texture images.

In this paper, we analyze the redundancy in calculating all pairs of geodesic distances on a rectangular grid. Fast marching method is an efficient way to estimate the geodesic distances from a point. But when calculated for all the points on the grid, this introduces certain redundancy. Our analysis shows that over 90% of the distances are actually recalculated. We propose a novel solution which exploits this redundancy to reduce the number of distances evaluated using the fast marching method and enforces the symmetry of the distance matrix. Experimental results show the improved accuracy obtained with our implementation.

MOTIVATION: Many aging genes have been found from unbiased screens in model organisms. Genetic interventions promoting longevity are usually quantitative, while in many other biological fields (e.g. development) null mutations alone have been very informative. Therefore, in the case of aging the task is larger and the need for a more efficient genetic search strategy is especially strong. RESULTS: The topology of genetic and metabolic networks is organized according to a scale-free distribution, in which hubs with large numbers of links are present. We have developed a computational model of aging genes as the hubs of biological networks. The computational model shows that, after generalized damage, the function of a network with scale-free topology can be significantly restored by a limited intervention on the hubs. Analyses of data on aging genes and biological networks support the applicability of the model to biological aging. The model also might explain several of the properties of aging genes, including the high degree of conservation across different species. The model suggests that aging genes tend to have a higher number of connections and therefore supports a strategy, based on connectivity, for prioritizing what might otherwise be a random search for aging genes.