Catálogo de publicaciones - libros

Current systems for automatic processing of salmon are not able to remove all bones from freshly slaughtered salmon. This is because some of the bones are attached to the flesh by tendons, and the fillet is damaged or the bones broken if the bones are pulled out. This paper describes a camera based system for determining the tendon positions in the tissue, so that the tendon can be cut with a knife and the bones removed. The location of the tendons deep in the tissue is estimated based on the position of a texture pattern on the fillet surface. Algorithms for locating this line-looking pattern, in the presence of several other similar-looking lines and significant other texture are described. The algorithm uses a model of the pattern’s location to achieve precision and speed, followed by a RANSAC/MLESAC inspired line fitting procedure. Close to the neck the pattern is barely visible; this is handled through a greedy search algorithm. We achieve a precision better than 3 mm for 78% of the fish using maximum 2 seconds processing time.

In this article, we are interested in the X-rays images of the spinal column in various positions. The purpose of this work is to extract some parameters determining the vertebral mobility and its variation during flexion-extension movements. A modified Discrete Dynamic Contour Model (DDCM) using the Canny edge detector was the starting point for our segmentation algorithm. To address the lack of convergence due to open contour, we have elaborated a heuristic method appropriate to the area of our application. The results in real images cooresponding to the cervical spinal column and their comparison with manual measures are presented to demonstrate and to validate the proposed technique.

The slit lamp microscope is the most popular opthalmologic instrument comprising a microscope with an light source attached to it. The coupling of microscope and light source distinguishes it from other optical devices. In this paper an Auto Focus system is proposed that considers this mechanical coupling and compensates for movements of the patient. It tracks the patient’s eye during the focusing process and applies a robust contrast-measurement algorithm to an area relative to it. The proposed method proved to be very accurate, reliable and stable, even starting from very defocused positions.

This paper proposes a method, that integrates image analysis and probabilistic techniques, for counting olive trees in high-resolution satellite images. Counting trees becomes significant for surveying and inventorying forests, and in certain cases relevant for assessing estimates of the production of plantations, as it is the case of the olive trees fields. The method presented in this paper exploits the particular characteristics of parcels, i.e. a certain reticular layout and a similar appearance of trees, to yield a probabilistic measure that captures the confident of each spot in the image to be an olive tree. Some promising experimental results have been obtained in satellite images taken from QuickBird.

Estimating the ego-motion of a mobile robot has been traditionally achieved by means of encoder-based odometry. However, this method presents several drawbacks, such as the existence of accumulative drifts, its sensibility to slippage, and its limitation to planar environments. In this work we present an alternative method for estimating the incremental change in the robot pose from images taken by a stereo camera. In contrast to most previous approaches for 6D visual odometry, based on iterative, approximate methods, we propose here to employ an optimal closed-form formulation which is more accurate, efficient, and does not exhibit convergence problems. We also derive the expression for the covariance associated to this estimation, which enables the integration of our approach into vision-based SLAM frameworks. Additionally, our proposal combines highly-distinctive SIFT descriptors with the fast KLT feature tracker, thus achieving robust and efficient execution in real-time. To validate our research we provide experimental results for a real robot.

This paper focuses on application of fuzzy sets of type 2 (FS2) in color images segmentation. The proposed approach is based on FS2 entropy application and region merging. Both local and global information of the image are employed and FS2 makes it possible to take into account the total uncertainty inherent to the segmentation operation. Fuzzy entropy is utilized as a tool to perform histogram analysis to find all major homogeneous regions at the first stage. Then a basic and fast region merging process, based on color similarity and reduction of small clusters, is carried out to avoid oversegmentation. The experimental results demonstrate that this method is suitable to find homogeneous regions for natural images, even for noisy images.

In multiple-object tracking, the lack in prior information limits the association performance. Furthermore, to improve tracking, dynamic models are needed in order to determine the settings of the estimation algorithm. In case of complex motions, the dynamic cannot be learned and the task of tracking becomes difficult. That is why online spatio-temporal motion estimation is of crucial importance. In this paper, we propose a new model for multiple target online tracking: the Energetic Normalized Mutual Information Model (ENMIM). ENMIM combines two algorithms: (i) Quadtree Normalized Mutual Information, QNMI, a recursive partitioning methodology involving a region motion extraction; (ii) an energy minimization approach for data association adapted to the constraint of lack in prior information about motion and based on geometric properties. ENMIM is able to handle typical problems such as large inter-frame displacements, unlearned motions and noisy images with low contrast. The main advantage of ENMIM is its parameterless and its capacity to handle noisy multi-modal images without exploiting any pre-processing step.

Deriving the geometrical features of an observed scene is pivotal for better understanding and detection of events in recorded videos. In the paper methods are presented for the estimation of various geometrical scene characteristics. The estimated characteristics are: point correspondences in stereo views, mirror pole, light source and horizon line. The estimation is based on the analysis of dynamical scene properties by using co-motion statistics. Various experiments prove the feasibility of our approach.

Being aware of other vehicles on the road ahead is a key information to help driver assistance systems to increase driver’s safety. This paper addresses this problem, proposing a system to detect vehicles from the images provided by a single camera mounted in a mobile platform. A classifier–based approach is presented, based on the evaluation of a cascade of classifiers (COC) at different scanned image regions. The Adaboost algorithm is used to determine the COC from training sets. Two proposals are done to reduce the computation needed for the detection scheme used: a lazy evaluation of the COC, and the customization of the COC by a wrapping process. The benefits of these two proposals are quantified in terms of the average number of image features required to classify an image region, achieving a reduction of the 58% on this concept, while scarcely penalizing the detection accuracy of the system.

An efficient automatic detection strategy for aerial moving targets in airborne forward-looking infrared (FLIR) imagery is presented in this paper. Airborne cameras induce a global motion over all objects in the image, that invalidates motion-based segmentation techniques for static cameras. To overcome this drawback, previous works compensate the camera ego-motion. However, this approach is too much dependent on the quality of the ego-motion compensation, tending towards an over-detection. In this work, the proposed strategy estimates a robust motion vector field, free of erroneous vectors. Motion vectors are classified into different independent moving objects, corresponding to background objects and aerial targets. The aerial targets are directly segmented using their associated motion vectors. This detection strategy has a low computational cost, since no compensation process or motion-based technique needs to be applied. Excellent results have been obtained over real FLIR sequences.