Manuel J. Lucena

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This paper presents a scheme of image retrieval from a database using queries prompted by the colour and the shape of the objects present in di€erent scenes. Of the whole scheme of image retrieval, we will focus attention on the modules that allow feature extraction of the component objects from the scenes and the matching of the objects among the di€erent(More)
In this paper, we study the use of optical flow as a characteristic for tracking. We analyze the behavior of three flow-based observation models for particle filter algorithms, and compare the results with the ones obtained by a well-known, gradient based, observation model. In theory, optical flow could be used directly to displace an object model, but in(More)
In this paper, we present an observation model to track objects using particle filter algorithms based on matching techniques for computing optical flow. Although optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation techniques lack the(More)
This paper addresses the human action recognition task from optical flow. This task is in itself an interesting problem, given the lack of accuracy and noisy characteristics of the optical flow estimation. Optical flow is one of the most popular descriptors characterizing motion, but due to its instability is usually used in combination with parametric(More)
This paper presents a technique to enable deformable objects to be matched throughout video sequences based on the information provided by the multi-scale local histograms of the images. We shall show that this technique is robust enough for viewpoint changes, lighting changes, large motions of the matched object and small changes in rotation and scale.(More)
This paper evaluates different Restricted Boltzmann Machines models in unsupervised, semi-supervised and supervised frameworks using information from human actions. After feeding these multilayer models with low level features, we infer high-level discriminating features that highly improve the classification performance. This approach eliminates the(More)
In this paper, we present an observation model based on the Lucas and Kanade algorithm for computing optical flow, to track objects using particle filter algorithms. Although optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation techniques(More)
We present two observation models based on optical flow information to track objects using particle filter algorithms. Although, in principle, the optical flow information enables us to know the displacement of the objects present in a scene, it cannot be used directly to displace a model since flow estimation techniques lack the necessary precision. We(More)