Catálogo de publicaciones - libros

We present in this paper a multi-agent approach for range image segmentation. The approach consists in using autonomous agents for the segmentation of a range image in its different planar regions. Agents move on the image and perform local actions on the pixels, allowing robust region extraction and accurate edge detection. In order to improve the segmentation quality, a Bayesian edge regularization is applied to the resulting edges. A new Markov Random Field (MRF) model is introduced to model the edge smoothness, used as a prior in the edge regularization. The experimental results obtained with real images from the ABW database show a good potential of the proposed approach for range image analysis, regarding both segmentation efficiency, and detection accuracy.

This paper presents a novel non-iterative method to restore the out-of-focus part of an image. The proposed method first applies a robust local blur estimation to obtain a blur map of the image. The estimation uses the maximum of difference ratio between the original image and its two digitally re-blurred versions to estimate the local blur radius. Then adaptive least mean square filters based on the local blur radius and the image structure are applied to restore the image and to eliminate the sensor noise. Experimental results have shown that despite its low complexity the proposed method has a good performance at reducing spatially varying blur.

This paper introduces a Bayesian restoration method for low-resolution images combined with a geometry-driven smoothness prior and a new global multimodal prior. The multimodal prior is proposed for images that normally just have a few dominant colours. In spite of this, most images contain much more colours due to noise and edge pixels that are part of two or more connected smooth regions. The Maximum A Posteriori estimator is worked out to solve the problem. Experimental results confirm the effectiveness of the proposed global multimodal prior for images with a strong multimodal colour distribution such as cartoons. We also show the visual superiority of our reconstruction scheme to other traditional interpolation and reconstruction methods: noise and compression artifacts are removed very well and our method produces less blur and other annoying artifacts.

A novel filtering operator based on type-2 fuzzy logic is proposed for detail preserving restoration of images corrupted by impulse noise. The performance of the proposed operator is evaluated for different test images corrupted at various noise densities and also compared with representative impulse noise removal operators from the literature. Results of the filtering experiments show that the presented operator offers superior performance over the competing operators by efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.

A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against two other widely used contrast enhancement methods.

A spatiotemporal method is presented for detection and concealment of local defects such as blotches in old color films. Initially, non-local means (NL-means) method which does not require motion estimation is used for noise removal in image sequences. Later, the motion vectors that are incorrectly estimated within defect regions are repaired by taking account of the temporal continuity of motion trajectory. The defects in films are detected by spike detection index (SDI) method, which are easily adapted to color image sequences. Finally, the proposed inpainting algorithm fills in detected defect regions, which is not required to estimate true motion like other approaches. The method is presented on synthetic and real image sequences, and efficient concealment results are obtained.

This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into , , and classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.

In this work, we present two active shape models for the segmentation of tubular objects. The first model is built using cylindrical parameterization and minimum description length to achieve correct correspondences. The other model is a multidimensional point distribution model built from the centre line and related information of the training shapes. The models are used to segment the human trachea in low-dose CT scans of the thorax and are compared in terms of compactness of representation and segmentation effectiveness and efficiency. Leave-one-out tests were carried out on real CT data.

I-ImaS (Intelligent Imaging Sensors) is a European project which has designed and developed a new adaptive X-ray imaging system using on-line exposure control, to create locally optimized images. The I-ImaS system allows for real-time image analysis during acquisition, thus enabling real-time exposure adjustment. This adaptive imaging system has the potential of creating images with optimal information within a given dose constraint and to acquire optimally exposed images of objects with variable density during one scan. In this paper we present the control system and results from initial tests on mammographic and encephalographic images. Furthermore, algorithms for visualization of the resulting images, consisting of unevenly exposed image regions, are developed and tested. The preliminary results show that the same image quality can be achieved at 30-70% lower dose using the I-ImaS system compared to conventional mammography systems.

In this paper, we introduce a novel evolution-based segmentation algorithm by using the heat flow analogy, to gain practical advantage. The proposed algorithm consists of two parts. In the first part, we represent a particular heat conduction problem in the image domain to roughly segment the region of interest. Then we use geometric heat flow to complete the segmentation, by smoothing extracted boundaries and removing possible noise inside the prior segmented region. The proposed algorithm is compared with active contour models and is tested on synthetic and medical images. Experimental results indicate that our approach works well in noisy conditions without pre-processing. It can detect multiple objects simultaneously. It is also computationally more efficient and easier to control and implement in comparison to active contour models.