Catálogo de publicaciones - libros

We propose a new fusion method to evaluate fingerprint quality by combining both spatial and frequency features of a fingerprint image. In frequency domain, a ring structure of DFT magnitude and directional Gabor features are applied. In spatial domain, black pixel ratio of central area is taken into account. These three features are the most efficient indexes for fingerprint quality assessment. Though additional features could be introduced, their slight improvement in performance will be traded off with complexity and computational load to some extent. Thus in this paper, each of the three features are first employed to assess fingerprint quality, their evaluation performance are also discussed. Then the suggested fusion approach of the three features is presented to obtain the final quality scores. We test the fusion method in our public security fingerprint database. Experimental results demonstrate that the proposed scheme can estimate the quality of fingerprint images accurately. It provides a feasible rejection of poor fingerprint images before they are presented to the fingerprint recognition system for feature extraction and matching.

This paper presents a new approach to automatic 3D face recognition using a model-based approach. This work uses real 3D dense point cloud data acquired with a scanner using a stereo photogrammetry technique. Since the point clouds are in varied orientations, by applying a non-iterative registration method, we automatically transform each point cloud to a canonical position. Unlike the iterative ICP algorithm, our non-iterative registration process is scale invariant. An efficient B-spline surface-fitting technique is developed to represent 3D faces in a way that allows efficient surface comparison. This is based on a novel knot vector standardisation algorithm which allow a single BSpline surface to be fitted onto a complex object represented as a unstructured points cloud. Consequently, dense correspondences across objects are established. Several experiments have been conducted and 91% recognition rate can be achieved.

In this paper, we propose a denoising method based on PCA reconstruction for removing complex color noise components on human faces, which is not easy to remove by using vectorial color filters. The proposed method is composed of the following six steps: training of canonical eigenface space using PCA, automatic extraction of facial features using active appearance model and alignment of the input face to mean shape, reconstruction of an initial noise free face, relighting of reconstructed face using a bilateral filter, extraction of noise regions using the variances of skin color of training data, and reconstruction using partial information of input images (except the noise regions) and blending of the reconstructed image with the original image. Experimental results show that the proposed denoising method maintains the structural characteristics of input faces, while efficiently removing noise components with complex colors.

We developed an algorithmic scheme to extract the semantical description of the face and the face motion from an image sequence, and to re-play this action in a 3-dimensional (3D) virtual world. The presented combines new methods for detection and tracking of the face and facial features, for estimating the 3D face movements and the nonrigid facial expressions, and for extracting the MPEG4 facial animation parameters. In the scheme, the face is treated either as a 2D object that has specific color, shape and motion characteristics, either as a 3D model that is calibrated and moved using a natural displacement-based deformation model. A dynamic MPEG4 displacement table takes care of the semantical controls of the animations of the face model. As a result, this virtual face model mimics well the gestures of the person in the video.

This paper presents a practical combination of image processing and pattern recognition techniques in order to identify pathological and atypical cells in phase contrast cytological images. The algorithms involved in the processing cover: oriented edge detection, ridge following, contour grouping and ellipse fitting. The Hough Transform and other techniques are discussed for comparison. Various pattern recognition techniques are tested and compared. All the exploited algorithms were customized to reflect specificity of phase contrast images and apriori–knowledge of cytological smear. Possible applications of this algorithm for automated screening systems are enumerated.

A new general framework for shape extraction is presented, based on the paradigm of water flow. The mechanism embodies the fluidity of water and hence can detect complex shapes. A new snake-like force functional combining edge-based and region-based forces produces capability for both range and accuracy. Properties analogous to surface tension and adhesion are also applied so that the smoothness of the evolving contour and the ability to flow into narrow branches can be controlled. The method has been assessed on synthetic and natural images, and shows encouraging detection performance and ability to handle noise, consistent with properties included in its formulation.

Human interaction is a crucial restriction of active contour model, or snakes. In this paper we propose a fully automatic algorithm based on gradient vector flow (GVF) field and watershed-based region merging. Firstly a scalar force field is constructed by minimizing an energy function from the GVF force field. From the scalar field we extract a set of seed points facilely, and get an initial segmentation without doing curve evolution. Then a Region Adjacency Graph (RAG) based region merging algorithm is applied to get the final result. Several experimental results demonstrate that this method is efficient to multiple objects segmentation, and insensitive to noises.

The paper describes a system for reading embossed Braille patterns on used aluminum beverage container lids. The intent of the system is to check whether the used containers are entitled to a refund. The lids have strong specular reflections. The reflections are avoided by a novel method that illuminates the lid alternating from two angles, and acquires two separate images. This illumination method is more compact than existing methods. We use the extended maxima algorithm to detect the Braille dots, and a cluster-based pattern point matching algorithm to recognize a pre-defined Braille pattern. The algorithms are customized to increase speed using a priori information. The system was evaluated on a test set containing 225 images. The median time used for analyzing one beverage can was 1 second, and the recognition rate was 94 percent.

In this paper, we propose a segmentation method for an automated differential counter using image analysis. The segmentation here is to extract leukocytes (white blood cells) and separate its constituents, nucleus and cytoplasm, in blood smear images. For this purpose, a region-based active contour model is used where region information is estimated using a statistical analysis. The role of the regional statistics is mainly to attract evolving contours toward the boundaries of leukocytes, avoiding problems with initialization. And contour deformation near to the boundaries is constrained by an additional regularizer. The active contour model is implemented using a level set method and validated with a leukocyte image database.

This paper proposes an object-based, highly scalable, lossy-to-lossless coding approach for magnetic resonance (MR) images. The proposed approach, called OBHS-SPIHT, is based on the well known set partitioning in hierarchical trees (SPIHT) algorithm and supports both quality and resolution scalability. It progressively encodes each slice of the MR data set separately in a multiresolution fashion from low resolution to full resolution and in each resolution from low quality to lossless quality. To achieve more compression efficiency, the algorithm only encodes the main object of interest in the input data set, and ignores the unnecessary background. The experimental results show the efficiency of the proposed algorithm for multiresolution lossy-to-lossless MRI data coding. OBHS-SPIHT, is a very attractive coding approach for medical image information archiving and transmission applications especially over heterogeneous networks.