Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo

To date, the comparison of Statistical Shape Models (SSMs) is often solely performance-based, carried out by means of simplistic metrics such as compactness, generalization, or specificity. Any similarities or differences between the actual shape spaces can neither be visualized nor quantified. In this paper, we present a new method to qualitatively compare two linear SSMs in dense correspondence by computing approximate intersection spaces and set-theoretic differences between the (hyper-ellipsoidal) allowable shape domains spanned by the models. To this end, we approximate the distribution of shapes lying in the intersection space using Markov chain Monte Carlo and subsequently apply Principal Component Analysis (PCA) to the posterior samples, eventually yielding a new SSM of the intersection space. We estimate differences between linear SSMs in a similar manner; here, however, the resulting spaces are no longer convex and we do not apply PCA but instead use the posterior samples for visualization. We showcase the proposed algorithm qualitatively by computing and analyzing intersection spaces and differences between publicly available face models, focusing on gender-specific male and female as well as identity and expression models. Our quantitative evaluation based on SSMs built from synthetic and real-world data sets provides detailed evidence that the introduced method is able to recover ground-truth intersection spaces and differences accurately.Comment: Accepted to WACV'2

MapFormer: Boosting Change Detection by Using Pre-change Information

Change detection in remote sensing imagery is essential for a variety of applications such as urban planning, disaster management, and climate research. However, existing methods for identifying semantically changed areas overlook the availability of semantic information in the form of existing maps describing features of the earth's surface. In this paper, we leverage this information for change detection in bi-temporal images. We show that the simple integration of the additional information via concatenation of latent representations suffices to significantly outperform state-of-the-art change detection methods. Motivated by this observation, we propose the new task of Conditional Change Detection, where pre-change semantic information is used as input next to bi-temporal images. To fully exploit the extra information, we propose MapFormer, a novel architecture based on a multi-modal feature fusion module that allows for feature processing conditioned on the available semantic information. We further employ a supervised, cross-modal contrastive loss to guide the learning of visual representations. Our approach outperforms existing change detection methods by an absolute 11.7% and 18.4% in terms of binary change IoU on DynamicEarthNet and HRSCD, respectively. Furthermore, we demonstrate the robustness of our approach to the quality of the pre-change semantic information and the absence pre-change imagery. The code will be made publicly available

Dependence of aptamer activity on opposed terminal extensions : improvement of light-regulation efficiency

Aptamers that can be regulated with light allow precise control of protein activity in space and time and hence of biological function in general. In a previous study, we showed that the activity of the thrombin-binding aptamer HD1 can be turned off by irradiation using a light activatable "caged" intramolecular antisense-domain. However, the activity of the presented aptamer in its ON state was only mediocre. Here we studied the nature of this loss in activity in detail and found that switching from 5'- to 3'-extensions affords aptamers that are even more potent than the unmodified HD1. In particular we arrived at derivatives that are now more active than the aptamer NU172 that is currently in phase 2 clinical trials as an anticoagulant. As a result, we present light-regulatable aptamers with a superior activity in their ON state and an almost digital ON/OFF behavior upon irradiation

Honeycomb Plots: Visual Enhancements for Hexagonal Maps

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.publishedVersio