Camera motion estimation through planar deformation determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion, that allows to separate the image deformation in a similarity and a ``purely'' projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediatly deduce camera motion parameters.Comment: 21 pages, version modifi\'ee accept\'e le 20 mars 200

Persistence modules, shape description, and completeness

Persistence modules are algebraic constructs that can be used to describe the shape of an object starting from a geometric representation of it. As shape descriptors, persistence modules are not complete, that is they may not distinguish non-equivalent shapes. In this paper we show that one reason for this is that homomorphisms between persistence modules forget the geometric nature of the problem. Therefore we introduce geometric homomorphisms between persistence modules, and show that in some cases they perform better. A combinatorial structure, the $H_0$-tree, is shown to be an invariant for geometric isomorphism classes in the case of persistence modules obtained through the 0th persistent homology functor

Anomalous Purcell decay of strongly driven inhomogeneous emitters coupled to a cavity

We perform resonant fluorescence lifetime measurements on a nanocavity-coupled erbium ensemble as a function of cavity-laser detuning and pump power. Our measurements reveal an anomalous suppression of the ensemble decay lifetime at zero cavity detuning and high pump fluence. We capture qualitative aspects of this decay rate suppression using a Tavis-Cummings model of non-interacting spins coupled to a common cavity.Comment: 4 figure

A differentiable forward model for the concurrent, multi-peak Bragg coherent x-ray diffraction imaging problem

We present a general analytic approach to spatially resolve the nano-scale lattice distortion field of strained and defected compact crystals with Bragg coherent x-ray diffraction imaging (BCDI). Our approach relies on fitting a differentiable forward model simultaneously to multiple BCDI datasets corresponding to independent Bragg reflections from the same single crystal. It is designed to be faithful to heterogeneities that potentially manifest as phase discontinuities in the coherently diffracted wave, such as lattice dislocations in an imperfect crystal. We retain fidelity to such small features in the reconstruction process through a Fourier transform -based resampling algorithm designed to largely avoid the point spread tendencies of commonly employed interpolation methods. The reconstruction model defined in this manner brings BCDI reconstruction into the scope of explicit optimization driven by automatic differentiation. With results from simulations and experimental diffraction data, we demonstrate significant improvement in the final image quality compared to conventional phase retrieval, enabled by explicitly coupling multiple BCDI datasets into the reconstruction loss function.Comment: 30 pages, 23 figure

A Root-to-Leaf Algorithm Computing the Tree of Shapes of an Image

International audienceWe propose an algorithm computing the tree of shapes of an image, a unified variation of the component trees, proceeding from the root to the leaf shapes in a recursive fashion. It proceeds differently from existing algorithms that start from leaves, which are regional extrema of intensity, and build the intermediate shapes up to the root, which is the whole image. The advantage of the proposed method is a simpler, clearer, and more concise implementation, together with a more favorable running time on natural images. For integer-valued images, the complexity is proportional to the total variation, which is the memory size of the output tree, which makes the algorithm optimal

Clustering of IRE1α depends on sensing ER stress but not on its RNase activity

The sensors of the unfolded protein response react to endoplasmic reticulum (ER) stress by transient activation of their enzymatic activities, which initiate various signaling cascades. In addition, the sensor IRE1α exhibits stress-induced clustering in a transient time frame similar to activation of its endoRNase activity. Previous work had suggested that the clustering response and RNase activity of IRE1α are functionally linked, but here we show that they are independent of each other and have different behaviors and modes of activation. Although both clustering and the RNase activity are responsive to luminal stress conditions and to depletion of the ER chaperone binding protein, RNase-inactive IRE1α still clusters and, conversely, full RNase activity can be accomplished without clustering. The clusters formed by RNase-inactive IRE1α are much larger and persist longer than those induced by ER stress. Clustering requires autophosphorylation, and an IRE1α mutant whose RNase domain is responsive to ligands that bind the kinase domain forms yet a third type of stress-independent cluster, with distinct physical properties and half-lives. These data suggest that IRE1α clustering can follow distinct pathways upon activation of the sensor.—Ricci, D., Marrocco, I., Blumenthal, D., Dibos, M., Eletto, D., Vargas, J., Boyle, S., Iwamoto, Y., Chomistek, S., Paton, J. C., Paton, A. W., Argon, Y. Clustering of IRE1α depends on sensing ER stress but not on its RNase activity. FASEB J. 33, 9811–9827 (2019). www.fasebj.org

Clustering of IRE1α depends on sensing ER stress but not on its RNase activity

The sensors of the unfolded protein response react to endoplasmic reticulum (ER) stress by transient activation of their enzymatic activities, which initiate various signaling cascades. In addition, the sensor IRE1α exhibits stress-induced clustering in a transient time frame similar to activation of its endoRNase activity. Previous work had suggested that the clustering response and RNase activity of IRE1α are functionally linked, but here we show that they are independent of each other and have different behaviors and modes of activation. Although both clustering and the RNase activity are responsive to luminal stress conditions and to depletion of the ER chaperone binding protein, RNase-inactive IRE1α still clusters and, conversely, full RNase activity can be accomplished without clustering. The clusters formed by RNase-inactive IRE1α are much larger and persist longer than those induced by ER stress. Clustering requires autophosphorylation, and an IRE1α mutant whose RNase domain is responsive to ligands that bind the kinase domain forms yet a third type of stress-independent cluster, with distinct physical properties and half-lives. These data suggest that IRE1α clustering can follow distinct pathways upon activation of the sensor.—Ricci, D., Marrocco, I., Blumenthal, D., Dibos, M., Eletto, D., Vargas, J., Boyle, S., Iwamoto, Y., Chomistek, S., Paton, J. C., Paton, A. W., Argon, Y. Clustering of IRE1α depends on sensing ER stress but not on its RNase activity. FASEB J. 33, 9811–9827 (2019). www.fasebj.org