Scale Normalization for the Distance Maps AAM

International audienceThe Active Apearence Models (AAM) are often used in Man-Machine Interaction for their ability to align the faces. We propose a new normalization method for AAM based on distance map in order to strengthen their robustness to differences in illumination. Our normalization do not use the photometric normalization protocol classically used in AAM and is much more simpler to implement. Compared to Distance Map AAM performances of {Leg06} and other AAM implementation which use CLAHE {Zuiderveld94} normalization or gradient information, our proposition is at the same time much robust to illumination and AAM initialization. The tests have been drive in the context of generalization: 10 persons with frontal illumination from M2VTS database {m2vts} were considered to build the AAM, and 17 persons under 21 different illuminations from CMU database {Sim02} were used for the testing base

Assisting pre-big bang phenomenology through short-lived axions

We present the results of a detailed study of how isocurvature axion fluctuations are converted into adiabatic metric perturbations through axion decay, and discuss the constraints on the parameters of pre-big bang cosmology needed for consistency with present CMB-anisotropy data. The large-scale normalization of temperature fluctuations has a non-trivial dependence both on the mass and on the initial value of the axion. In the simplest, minimal models of pre-big bang inflation, consistency with the COBE normalization requires a slightly tilted (blue) spectrum, while a strictly scale-invariant spectrum requires mild modifications of the minimal backgrounds at large curvature and/or string coupling.Comment: 14 pages, latex, 1 figure included using epsfig. A few typos corrected, two references added, the figure slightly improved. To appear in Phys. Lett.

Multi-view 3D retrieval using silhouette intersection and multi-scale contour representation

We describe in this paper two methods for 3D shape indexing and retrieval that we apply on two data collections of the SHREC - SHape Retrieval Contest 2007: Watertight models and 3D CAD models. Both methods are based on a set of 2D multi-views after a pose and scale normalization of the models using PCA and the enclosing sphere. In all views we extract the models silhouettes and compare them pairwise. In the first method the similitude measure is obtained by integrating on the pairs of views the difference between the areas of the silhouettes union and the silhouettes intersection. In the second method we consider the external contour of the silhouettes, extract their convexities and concavities at different scale levels and build a multiscale representation. The pairs of contours are then compared by elastic matching achieved by using dynamic programming. Comparisons of the two methods are shown with their respective strengths and weaknesses

An Analysis of Scale Invariance in Object Detection - SNIP

An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data. By evaluating the performance of different network architectures for classifying small objects on ImageNet, we show that CNNs are not robust to changes in scale. Based on this analysis, we propose to train and test detectors on the same scales of an image-pyramid. Since small and large objects are difficult to recognize at smaller and larger scales respectively, we present a novel training scheme called Scale Normalization for Image Pyramids (SNIP) which selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at \url{http://bit.ly/2yXVg4c}.Comment: CVPR 2018, camera ready versio