Catálogo de publicaciones - libros

A novel multiresolution approach is presented to segment Brain MRI images using fuzzy clustering. This approach is based on the fact that the image segmentation results should be optimized simultaneously in different scales. A new fuzzy inter-scale constraint based on antistrophic diffusion linkage model is introduced, which builds an efficient linkage relationship between the high resolution images and low resolution ones. Meanwhile, this paper develops two new fuzzy distances and then embeds them into the fuzzy clustering algorithm. The distances describe the fuzzy similarity in adjacent scales effectively. Moreover, a new multiresolution framework combining the inter- and intra-scale constraints is presented. The proposed framework is robust to noise images and low contrast ones, such as medical images. Segmentation of a number of images is illustrated. The experiments show that the proposed approach can extract the objects accurately.

This work takes is part of a medical research project which intends to induce and study cardiac hibernation in rats. The underlying goal is to understand the physiology of heart disease. We present here a novel method to compute the 2D-deformation field of the heart (rat or human) from tagged MRI. Previous work is not suitable for wide clinical use for different reasons, including important computing time and lack of robustness. We propose an original description of tags as local minima of 1D signals. This leads us to a new formulation of the tag tracking problem as an Extrema Temporal Chaining (ETC) and a 2D-rendering. 2D-displacements are then interpolated on a dense field. The developed method is fast and robust. Its performances are compared to those of HARP, a leading method in this field.

Symptoms of Parkinson’s disease can be relieved through Deep Brain Stimulation. This neurosurgical technique relies on high precision positioning of electrodes in specific areas of the basal ganglia and the thalamus. In order to identify these anatomical targets, which are located deep within the brain, we developed a semi-automated method of image analysis, based on data fusion. Information provided by both anatomical magnetic resonance images and expert knowledge is managed in a common possibilistic frame, using a fuzzy logic approach. More specifically, a graph-based modeling theoretical anatomical knowledge is matched to the image data from each patient, through a research algorithm (or ) which simultaneously computes an estimation of the location of every structures, thus assisting the neurosurgeon in defining the optimal target. The method was tested on 10 images, with promising results. Location and segmentation results were statistically assessed, opening perspectives for enhancements.

Understanding behavior of migrating cells is becoming an emerging research area with many important applications. Segmentation and tracking constitute vital steps of this research. In this paper, we present an automated cell segmentation and tracking system designed to study migration of cells imaged with a phase contrast microscope. For segmentation the system uses active contour level set methods with a novel extension that efficiently prevents false-merge problem. Tracking is done by resolving frame to frame correspondences between multiple cells using a multi-distance, multi-hypothesis algorithm. Cells that move into the field-of-view, arise from cell division or disappear due to apoptosis are reliably segmented and tracked by the system. Robust tracking of cells, imaged with a phase contrast microscope is a challenging problem due to difficulties in segmenting dense clusters of cells. As cells being imaged have vague borders, close neighboring cells may appear to merge. These false-merges lead to incorrect trajectories being generated during the tracking process. Current level-set based approaches to solve the false-merge problem require a unique level set per object (the N-level set paradigm). The proposed approach uses evidence from previous frames and graph coloring principles and solves the same problem with only four level sets for any arbitrary number of similar objects, like cells.

The cropping of image is one of most popular functions in current image editing software for general digital camera users. And image-based identifier system is needed for wide distribution of digital image products. In this paper, we propose new concept of visual identifier for digital photos and visual identifier system structure robust against especially cropped natural photos. Visual identifier is new concept with different ground truth rather than retrieval. In the proposed identifier system, local corner is used as main co-location position and local gradient histogram is applied to describe feature in each position. And for robust matching we use simple random sample consensus method. Since image cropping can be considered as a kind of translation, linear model is sufficient as geometric transform model. For experiment we make 11 kinds of modifications of original images and evaluate performance of the proposed algorithm. From experiment results, our proposed algorithm shows better performance relative to previous MPEG-7 visual descriptors.

Most approaches to camera motion estimation from image sequences require matching the projections of at least 4 non-coplanar points in the scene. The case of points lying on a plane has only recently been addressed, using mainly projective cameras. We here study what can be recovered from two uncalibrated views of a under viewing conditions. We prove that the affine epipolar direction can be recovered provided camera motion is free of cyclorotation. The proposed method consists of two steps: 1) computing the affinity between two views by tracking a planar contour, and 2) recovering the epipolar direction by solving a second-order equation on the affinity parameters. Two sets of experiments were performed to evaluate the accuracy of the method. First, synthetic image streams were used to assess the sensitivity of the method to controlled changes in viewing conditions and to image noise. Then, the method was tested under more realistic conditions by using a robot arm to obtain calibrated image streams, which permit comparing our results to ground truth.

The analysis of the events taking place in a crossroads offers the opportunity to avoid harmful situations and the potential to increase traffic efficiency in modern urban areas. This paper presents an automatic visual system that reasons about the moving vehicles being observed and extracts high-level information, useful for traffic monitoring and detection of unusual activity. Initially, moving objects are detected using an adaptive background image model. Then, the vehicles are tracked down by an iterative method where the features being tracked are updated frame by frame. Next, paths are packed into routes using a similarity measure and a sequential clustering algorithm. Finally, the crossroads activity is organized into states representing the underlying mechanism that causes the type of motion being detected. We present the experimental evidence that suggests that the framework may prove to be useful as a tool to monitor traffic-light-controlled crossroads.

This paper proposes a method for detecting frontal pedestrian crossings and estimating its length from image data obtained with a single camera as a travel aid for the blind. It is important for the blind to know whether or not a frontal area is a crossing. The existence of a crossing is detected in two steps. In the first step, feature points for a crossing is extracted using Fisher criterion. In the second step, the existence of a crossing is detected by checking on the peoriodicity of white stripes on the road using projective invariants. Next, we propose a method for estimaing crossing length using extracted feature points. From the experimental results for evaluation, it is found that the existence of a crossing is successfully detected for all 173 real images which include 100 images with crossings and 73 images without crossing. The rms error of crossing length estimation for the 100 images is found 2.29m.

In this paper, we propose a novel stochastic attributed relational graph (SARG) matching algorithm in order to cope with possible distortions due to noise and occlusion. The support flow and the correspondence measure between nodes are defined and estimated by analyzing the distribution of the attribute vectors in the relation vector space. And then the candidate subgraphs are extracted and ordered according to the correspondence measure. Missing nodes for each candidates are identified by the iterative voting scheme through an error analysis, and then the final subgraph matching is carried out effectively by excluding them. Experimental results on the synthetic ARGs demonstrate that the proposed SARG matching algorithm is quite robust and efficient even in the noisy environment. Comparative evaluation results also show that it gives superior performance compared to other conventional graph matching approaches.

In most content-based image retrieval systems, the low level visual features such as color, texture and region play an important role. Variety of dissimilarity measures were introduced for an uniform quantization of visual features, or a . However, a cluster-based representation, or a , has proven to be more compact and theoretically sound for the accuracy and robustness than a histogram. Despite of these advantages, so far, only a few dissimilarity measures have been proposed. In this paper, we present a novel dissimilarity measure for a random signature, Perceptually Modified Hausdorff Distance (PMHD), based on Hausdorff distance. In order to demonstrate the performance of the PMHD, we retrieve relevant images for some queries on real image database by using only color information. The precision vs. recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.