Jérôme Pasquet

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Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and(More)
This paper deals with digital archiving of cemetery heritage. A built cemetery is a tangible evidence of historical and cultural periods through the style and the shape of tombs. It gives quantitative information on the local population, about its history (by reading birth and death dates), its culture (by analysing name typology) and its temporal evolution(More)
Many different hypotheses may be chosen for modeling a steganography/steganalysis problem. In this paper, we look closer into the case in which Eve, the steganalyst, has partial or erroneous knowledge of the cover distribution. More precisely we suppose that Eve knows the algorithms and the payload size that has been used by Alice, the steganographer, but(More)
In this paper, we deal with the problem of object detection in aerial images. A lot of efficient approaches uses a cascade of classifiers which process vectors of descriptive features such as HOG. In order to take into account the variability in object dimension, features at different resolutions are often concatenated in a large descriptor vector. This(More)
Today, with the higher computing power of CPUs and GPUs, many different neural network architectures have been proposed for object detection in images. However, these networks are often not optimized to process color information. In this paper, we propose a new method based on an SVM network, that efficiently extracts this color information. We describe(More)
Deep neural networks yield positive object detection results in aerial imaging. To deal with the massive computational time required, we propose to connect an SVM Network to the different feature maps of a CNN. After the training of this SVM Network, we use an activation path to cross the network in a predefined order. We stop the crossing as quickly as(More)
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