In-Flight CCD Distortion Calibration for Pushbroom Satellites Based on Subpixel Correlation

We describe a method that allows for accurate inflight calibration of the interior orientation of any pushbroom camera and that in particular solves the problem of modeling the distortions induced by charge coupled device (CCD) misalignments. The distortion induced on the ground by each CCD is measured using subpixel correlation between the orthorectified image to be calibrated and an orthorectified reference image that is assumed distortion free. Distortions are modeled as camera defects, which are assumed constant over time. Our results show that in-flight interior orientation calibration reduces internal camera biases by one order of magnitude. In particular, we fully characterize and model the Satellite Pour l'Observation de la Terre (SPOT) 4-HRV1 sensor, and we conjecture that distortions mostly result from the mechanical strain produced when the satellite was launched rather than from effects of on-orbit thermal variations or aging. The derived calibration models have been integrated to the software package Coregistration of Optically Sensed Images and Correlation (COSI-Corr), freely available from the Caltech Tectonics Observatory website. Such calibration models are particularly useful in reducing biases in digital elevation models (DEMs) generated from stereo matching and in improving the accuracy of change detection algorithms

OL\'E: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning

Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin of the learned deep representations. To simultaneously achieve these two goals, different solutions have been proposed in the literature, such as the pairwise or triplet losses. However, such solutions carry the extra task of selecting pairs or triplets, and the extra computational burden of computing and learning for many combinations of them. In this paper, we propose a plug-and-play loss term for deep networks that explicitly reduces intra-class variance and enforces inter-class margin simultaneously, in a simple and elegant geometric manner. For each class, the deep features are collapsed into a learned linear subspace, or union of them, and inter-class subspaces are pushed to be as orthogonal as possible. Our proposed Orthogonal Low-rank Embedding (OL\'E) does not require carefully crafting pairs or triplets of samples for training, and works standalone as a classification loss, being the first reported deep metric learning framework of its kind. Because of the improved margin between features of different classes, the resulting deep networks generalize better, are more discriminative, and more robust. We demonstrate improved classification performance in general object recognition, plugging the proposed loss term into existing off-the-shelf architectures. In particular, we show the advantage of the proposed loss in the small data/model scenario, and we significantly advance the state-of-the-art on the Stanford STL-10 benchmark

Image forgery detection via forensic similarity graphs

In the article 'Exposing Fake Images with Forensic Similarity Graphs', O. Mayer and M. C. Stamm introduce a novel image forgery detection method. The proposed method is built on a graph-based representation of images, where image patches are represented as the vertices of the graph, and the edge weights are assigned in order to reflect the forensic similarity between the connected patches. In this representation, forged regions form highly connected subgraphs. Therefore, forgery detection and localization can be cast as a cluster analysis problem on the similarity graph. The authors present two graph clustering methods to detect and localize image forgeries. In this paper, we present briefly the method and offer an online executable version allowing everyone to test it on their own suspicious images.Projecto ANR-16-DEFA-0004Proyecto vera.ai (101070093

Forensic similarity for source camera model comparison

In the article 'Forensic Similarity for Digital Images', O. Mayer and M. C. Stamm introduce the forensic similarity approach, which aims at determining whether two image patches contain the same forensic traces or not. The proposed method is based on a feed-forward neural network which consists of two modules : a feature extraction module using a pair of CNNs in a siamese configuration, and a three-layer neural network that maps the extracted features into a similarity score. In this article, we explore the use of the forensic similarity score for source camera model comparison, as one of the possible applications of such an approach suggested by Mayer and Stamm.Proyecto ANR-DGA (ANR-16-DEFA-0004)Proyecto vera.ai (101070093

A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging

Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints. Full size images are available as HAL technical report hal-01107519v5, IEEE Transactions on Computational Imaging, 201

U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold

In this work we propose a non-contrastive method for anomaly detection and segmentation in images, that benefits both from a modern machine learning approach and a more classic statistical detection theory. The method consists of three phases. First, features are extracted by making use of a multi-scale image Transformer architecture. Then, these features are fed into a U-shaped Normalizing Flow that lays the theoretical foundations for the last phase, which computes a pixel-level anomaly map, and performs a segmentation based on the a contrario framework. This multiple hypothesis testing strategy permits to derive a robust automatic detection threshold, which is key in many real-world applications, where an operational point is needed. The segmentation results are evaluated using the Intersection over Union (IoU) metric, and for assessing the generated anomaly maps we report the area under the Receiver Operating Characteristic curve (ROC-AUC) at both image and pixel level. For both metrics, the proposed approach produces state-of-the-art results, ranking first in most MvTec-AD categories, with a mean pixel-level ROC- AUC of 98.74%. Code and trained models are available at https://github.com/mtailanian/uflow.Comment: 18 page