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International audience; No-reference image quality assessment (NR-IQA) aims at computing an image quality score that best correlateswith either human perceived image quality or an objective quality measure, without any prior knowledge ofreference images. Although learning-based NR-IQA methods have achieved the best state-of-the-art results sofar, those methods perform well only on the datasets on which they were trained. The datasets usually containhomogeneous documents, whereas in reality, document images come from different sources. It is unrealistic tocollect training samples of images from every possible capturing device and every document type. Hence, weargue that a metric-based IQA method is more suitable for heterogeneous documents. We propose a NR-IQAmethod with the objective quality measure of OCR accuracy. The method combines distortion-specific qualitymetrics. The final quality score is calculated taking into account the proportions of, and the dependency amongdifferent distortions. Experimental results show that the method achieves competitive results with learning-basedNR-IQA methods on standard datasets, and performs better on heterogeneous documents.

International audience; This paper reports on the first phase of an attempt to create a full retro-engineering pipeline that aims to construct a complete set of coherent typographic parameters defining the typefaces used in a printed homogenous text. It should be stressed that this process cannot reasonably be expected to be fully automatic and that it is designed to include human interaction. Although font design is governed by a set of quite robust and formal geometric rulesets, it still heavily relies on subjective human interpretation. Furthermore, different parameters, applied to the generic rulesets may actually result in quite similar and visually difficult to distinguish typefaces, making the retro-engineering an inverse problem that is ill conditioned once shape distortions (related to the printing and/or scanning process) come into play. This work is the first phase of a long iterative process, in which we will progressively study and assess the techniques from the state-of-the-art that are most suited to our problem and investigate new directions when they prove to not quite adequate. As a first step, this is more of a feasibility proof-of-concept, that will allow us to clearly pinpoint the items that will require more in-depth research over the next iterations.

International audience; A novel action recognition strategy in a video-surveillance context is herein presented. The method starts by computing a multiscale dense optical flow, from which spatial apparent movement regions are clustered as Regions of Interest (RoIs). Each ROI is summarized at each time by an orientation histogram. Then, a multilayer structure dynamically stores the orientation histograms associated to any of the found RoI in the scene and a set of cumulated temporal statistics is used to label that RoI using a previously trained support vector machine model. The method is evaluated using classic human action and public surveillance datasets, with two different tasks: (1) classification of short sequences containing individual actions, and (2) Frame-level recognition of human action in long sequences containing simultaneous actions. The accuracy measurements are: 96.7% (sequence rate) for the classification task, and 95.3% (frame rate) for recognition in surveillance scenes.