International Journal of Pattern Recognition and Artificial Intelligence

A robust and efficient approach to estimate the fundamental matrix is proposed. The main goal is to reduce the computational cost involved in the estimation when robust schemas are applied. The backbone of the proposed technique is the conventional Least Median of Squares (LMedS) technique. It is well known that the LMedS is one of the most robust regressors for highly contaminated data and unstable models. Unfortunately, its computational complexity renders it useless for practical applications. To overcome this problem, a small number of low-dimensionality least-square problems are solved using well-selected subsets from the input data. The results of this initial approach are fed into the LMedS schema, which is applied to recover the final estimation of the Fundamental matrix. The complexity is substantially reduced by applying a selection process based on an effective statistical analysis of the inherent correlation of the input data. This analysis is used to define a suitable clustering of the data and to drive the subset selection aiming at the reduction of the search space in the LMedS schema. It is shown that avoiding redundancies better estimates can be obtained while keeping the computational cost low. Selected results of computer experiments were conducted to assess the performance of the proposed technique.

This study obtains projective reconstructions of an object or a scene from its image pair and measures relative consistency of these projective reconstructions. 3D points are estimated from an image pair using projective and epipolar geometry. Two measures are presented for verification of projective reconstructions with each other. These measures are based on the equality of ratios between the x-, y- and z-coordinates of 3D reconstructed points which are obtained from the same corresponding points. This information is used for measuring the relative consistency of projective reconstructions obtained from the same image pair.

In this paper, a new approach named focal region-based volume rendering for visualizing internal structures of volumetric data is presented. This approach presents volumetric information through integrating context information as the structure analysis of the data set with a lens-like focal region rendering to show more detailed information. This feature-based approach contains three main components: (i) A feature extraction model using 3D image processing techniques to explore the structure of objects to provide contextual information; (ii) An efficient ray-bounded volume ray casting rendering to provide the detailed information of the volume of interest in the focal region; (iii) The tools used to manipulate focal regions to make this approach more flexible. The approach provides a powerful framework for producing detailed information from volumetric data. Providing contextual information and focal region renditions at the same time has the advantages of easy to understand and comprehend volume information for the scientist. The interaction techniques provided in this approach make the focal region-based volume rendering more flexible and easy to use.

This paper describes a surface flattening technique, which has been developed in particular to obtain a complete view of the cortical surface of the brain. However, the method is able to produce an overall planar view of any anatomical or real-life object, provided it is topologically compatible with the sphere (i.e. genus 0). It computes the shading of the original surface for rays casted from a nearby surrounding surface and unfolds this surface in a 2D plane, without introducing major distortions. The flat image consisting of the mapped shading results has the advantage that the sulci (i.e. the grooves characterizing the superficial brain geometry) of the cortical surface of the brain can be followed in their entirety, which facilitates the study and the recognition of their patterns.

The new visualization method is integrated into a versatile medical image analysis environment. A first study to assess its usefulness has been accomplished and is also reported in this paper.

This paper proposes a new method for the exact reconstruction of gray-scale images from projections. The image projections construct an accumulator array, which is used afterwards to reconstruct the original grayscale image by applying the proposed decomposition algorithm. The proposed method determines the number of projections and the number of rays in each projection that are required in order to achieve the reconstruction. These two parameters also define the dimensions of the accumulator array. Using an accumulator array with proper dimensions ensures that there is always a unique characteristic sample for each pixel, which is used during the reconstruction process to extract the pixel's grayscale value. During the reconstruction phase, the sinusoidal contribution of each pixel is removed from the accumulator array. At the end of the decomposition process the accumulator array becomes empty and the original image is exactly reconstructed. The experimental results confirm the robustness and efficiency of the proposed method.

We aim at recognizing a set of dance gestures from contemporary ballet. Our input data are motion trajectories followed by the joints of a dancing body provided by a motion-capture system. It is obvious that direct use of the original signals is unreliable and expensive. Therefore, we propose a suitable tool for nonuniform sub-sampling of spatio-temporal signals. The key to our approach is the use of polygonal approximation to provide a compact and efficient representation of motion trajectories. Our dance gesture recognition method involves a set of Hidden Markov Models (HMMs), each of them being related to a motion trajectory followed by the joints. The recognition of such movements is then achieved by matching the resulting gesture models with the input data via HMMs. We have validated our recognition system on 12 fundamental movements from contemporary ballet performed by four dancers.

This paper focuses on impulsive noise filtering and outliers rejection in gray-scale images. The proposed method combines neural networks, lower-upper-middle (LUM) smoothers and adaptive switching operations to produce a high-quality enhanced image. Extensive experimentation reported in this paper indicates that the proposed method is sufficiently robust, achieves an excellent balance between noise suppression and signal-detail preservation, and outperforms some well-known filters both subjectively and objectively.

A runtime system for implementation of image processing operations is presented. It is designed for working in a flexible and distributed environment related to the software architecture of a newly developed UAV system. The software architecture can be characterized at a coarse scale as a layered system, with a deliberative layer at the top, a reactive layer in the middle, and a processing layer at the bottom. At a finer scale each of the three levels is decomposed into sets of modules which communicate using CORBA, allowing system development and deployment on the UAV to be made in a highly flexible way. Image processing takes place in a dedicated module located in the process layer, and is the main focus of the paper. This module has been designed as a runtime system for data flow graphs, allowing various processing operations to be created online and on demand by the higher levels of the system. The runtime system is implemented in Java, which allows development and deployment to be made on a wide range of hardware/software configurations. Optimizations for particular hardware platforms have been made using Java's native interface.