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ÐWe describe an algorithm for reconstructing three-dimensional structure and motion causally, in real time from monocular sequences of images. We prove that the algorithm is minimal and stable, in the sense that the estimation error remains bounded with probability one throughout a sequence of arbitrary length. We discuss a scheme for handling occlusions(More)
We analyze the observability of the continuous and discrete states of a class of continuous-time linear hybrid systems. We derive necessary and sufficient conditions that the structural parameters of the model must satisfy in order for filtering and smoothing algorithms to operate correctly. Our conditions are simple rank tests that exploit the geometry of(More)
Subspace identification for closed loop systems has been recently studied by several authors. A class of new and consistent closed-loop subspace algorithms is based on identification of a predictor model, in a way similar as prediction error methods (PEM) do. Experimental evidence suggests that these methods have a behavior which is very close to PEM in(More)
— There is experimental evidence that a recently proposed subspace algorithm based on predictor identification has a behavior which is very close to prediction error methods in certain simple examples; this observation raises a question concerning its optimality. It is known that time series identification using the Canonical Correlation Analysis (CCA)(More)
We study a simple linear regression problem for grouped variables; we are interested in methods which jointly perform estimation and variable selection, that is, that automatically set to zero groups of variables in the regression vector. The Group Lasso (GLasso), a well known approach used to tackle this problem which is also a special case of Multiple(More)