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We study and evaluate invariance properties of local descriptors.We introduce a new Local Histogram Equalization (LHE) descriptor.We show the robustness of LHE for various color and geometric distortions.LHE is recommended for feature matching in Camera-Projector systems. Spatial Augmented Reality applications generally use projector-camera systems to control the visual projection appearance by comparing the initial projected and the acquired images. To obtain an accurate geometric compensation, a non-intrusive feature-point matching approach can be exploited which must handle complex photometric distortions due to the spectral devices responses, complex illumination and the mixing of the projected image with the projection surface.This paper first discusses the invariance properties of existing color descriptors in that application for non-intrusive geometric compensation. Their performance is evaluated using the framework of Setkov et al. (2013) extended by adding the several new test cases: modeled synthetic projections, real-world projections under various illuminants on one and two planar surfaces. Our experimental results show two main conclusions: (1) classical color vision models are hardly suitable to model the distortions in a projector-camera system, and (2) the LHE-based descriptor (Local Histogram Equalization) is the most reliable to compensate real-projections.

This article presents the human evaluation of ILIAD, a program for machine-aided indexing (MAI). It consists of two language engineering modules and is designed to assist expert librarians in computer-aided indexing and document analysis. Our aim is the expert evaluation of automatic multi-word term indexing. Evaluation is performed by documentary engineers. Cataloging and indexing are their principal tasks. They also have a good scientific knowledge of the domain to which the indexed documents belong.We first present the ILIAD program and the two systems submitted to this evaluation, the methodology (protocol) adopted, the differences between the protocol and the implementation, and the results of these evaluations. Human evaluation is divided into three parts: firstly the evaluation of controlled indexing, then free indexing and finally term variant extraction performed during controlled indexing. Finally, we analyze the relevance of this evaluation by calculating the agreement frequency and the Kappa coefficient and propose some future developments.

Windowing techniques play a key role in information retrieval. Previous works have suggested that the quality of access to information relies heavily on the characteristics of the windows. This study provides a linguistic approach to text windowing through an extraction of term variants with the help of a partial parser. The syntactic grounding of the method ensures that words observed within restricted spans are lexically related and that spurious word cooccurrences are ruled out with a good level of confidence. The system, is computationally tractable on large corpora and large lists of terms. Illustrative examples of term variations form a large medical corpus are given. An experimental evaluation of the method shows that only a small proportion of co-occurring words are lexically related and motivates the call for natural language parsing techniques in text windowing.