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The paper presents a method for groundplane estimation from image-pairs even if unstructured environment and motion. In a typical outdoor multi-camera system the observed objects might be very different due to the noise coming from lighting conditions, camera positions. Static features such as color, shape, and contours cannot be used for image matching in these cases. In the paper a method is proposed...
A new method is presented for identifying rigid motion of free-form curves based on “related-points” extracted from the decomposition of implicit polynomials of these curves. Polynomial decomposition expresses the curve as a unique sum of products of (possibly) complex lines. We show that each real intersection point of these lines, i.e. related-points, undergoes the same motion with the curve, and...
Multiple classifiers have shown capability to improve performance in pattern recognition. This process can improve the overall accuracy of the system by using an optimal decision criteria. In this paper we propose an approach using a weighted benevolent fusion strategy to combine two state of the art pixel based motion classifiers. Tests on outdoor and indoor sequences confirm the efficacy of this...
Optical flow algorithms generally demand for high computational power and huge storage capacities. This paper is a contribution for real-time implementation of an optical flow algorithm on a pipeline machine. This overall optical flow computation methodology is presented and evaluated on a set of synthetic and real image sequences. Results are compared to other implementations using as measures the...
A novel de-interlacing algorithm based on motion objects is presented in this paper. In this algorithm, natural motion objects, not contrived blocks, are considered as the processing cells, which are accurately detected by a new scheme, and whose matching objects are quickly searched by the immune clonal selection algorithm. This novel algorithm integrates many other de-interlacing methods, so it...
The present work presents and evaluates a method to automatically select training samples of medium resolution satellite images within a supervised object oriented classification procedure. The method first takes a pair of images of the same area acquired in different dates and segments them in homogeneous regions on both images. Then a change detection algorithm takes stable segments as training...
This paper describes a new parallel algorithm to compute the optical flow of a video sequence. A previous sequential algorithm has been distributed over a cluster. It has been implemented in a cluster with 8 nodes connected by means of a Gigabit Ethernet. On this architecture, the algorithm, that computes the optical flow of every image on the sequence, is able of processing 10 images of 720 × 576...
We present lipreading using recurrent neural prediction model. Lipreading copes with time-series data like speech recognition. Therefore, many traditional methods use Hidden Markov Model (HMM) as the classifier for lipreading. However, in recent years, a speech recognition method using Recurrent Neural Prediction Model (RNPM) is proposed, and good result is reported. It is expected that RNPM also...
This paper presents a multiple model parallel processing technique to adaptively estimate the nonuniformity parameters of infrared image sequences. The approach is based on both an optimal recursive estimation based on a fast form of the Kalman filter, and a solution for the uncertainties on the system model by running a bank of those estimators in parallel. The residual errors of these estimators...
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