Measuring Robustness of Classifiers to Geometric Transformations
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Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks. Nevertheless, it is unclear to which extent state-of-the-art classifiers are invariant to basic transformations such as rotatio ...
Process monitoring and fault diagnosis are broadly used to control quality and enforce safety compliance in industrial processes. Processes are commonly monitored by online spectroscopy, with either PCA or calibration techniques such as PLS being used to p ...
In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute densely. We also present an EM based algorithm to compute dense depth and occlusion maps from wide baseline image pairs using this descriptor. This yields much ...
Institute of Electrical and Electronics Engineers2010
The construction of low-dimensional models explaining high-dimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern transformation manifold models generated by in-plane geometric transformation ...
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This paper addresses the problem of distributed coding of light fields in camera networks. A novel distributed coding scheme with side information is presented, based on spherical image expansion over an overcomplete dictionary of geometric atoms. We propo ...