Capturing Appearance Variation in Active Appearance Models

The paper presents an extension of active appearance models (AAMs) that is better capable of dealing with the large variation in face appearance that is encountered in large multi-person face data sets. Instead of the traditional PCA-based texture model, our extended AAM employs a mixture of probabilistic PCA to describe texture variation,...

Texton-based analysis of paintings

The visual examination of paintings is traditionally performed by skilled art historians using their eyes. Recent advances in intelligent systems may support art historians in determining the authenticity or date of creation of paintings. In this paper, we propose a technique for the examination of brushstroke structure that views the wildly...

Identification of Myosotis seeds by means of digital image analysis

Despite their name, the identification of seeds of Myosotis species (forget-me-not) has hitherto received little attention from archaeobotanists. In an attempt to assemble a collection of reliable identification criteria, digital image analysis was applied to photographs of Myosotis seeds by means of Fovea Pro 4.0. This program computes 23...

Action unit classification using active appearance models and conditional random fields

In this paper, we investigate to what extent modern computer vision and machine learning techniques can assist social psychology research by automatically recognizing facial expressions. To this end, we develop a system that automatically recognizes the action units defined in the facial action coding system (FACS). The system uses a...

AU Classification using AAMs and CRFs (extended abstract)

Automatic facial expression recognition is an important problem in social signal processing that has applications ranging from treatment of autistic children to monitoring of conflict situations [6]. In psychology, facial expressions are generally described using the Facial Action Coding System (FACS; [2]), in which each facial muscle is...

Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings

The Allen Brain Atlases enable the study of spatially resolved, genome-wide gene expression patterns across the mammalian brain. Several explorative studies have applied linear dimensionality reduction methods such as Principal Component Analysis (PCA) and classical Multi-Dimensional Scaling (cMDS) to gain insight into the spatial organization...

An Automated Method for Semantic Classification of Regions in Coastal Images

Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness. Therefore manual quality control and site- and time...

Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic...

Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model

We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The hidden units are connected in a chain structure that models temporal dependencies in the data. Compared with the prior models for time-series...

Persistent self-supervised learning: From stereo to monocular vision for obstacle avoidance

Self-supervised learning is a reliable learning mechanism in which a robot uses an original, trusted sensor cue for training to recognize an additional, complementary sensor cue. We study for the first time in self-supervised learning how a robot’s learning behavior should be organized, so that the robot can keep performing its task in the...