Search Results for author: Erik Rodner

Found 28 papers, 5 papers with code

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

1 code implementation19 Apr 2018 Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler

Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e. g., fraud detection, climate analysis, or healthcare monitoring.

Anomaly Detection Fraud Detection +2

Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

no code implementations23 May 2017 Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian Ellis, Mohammad Ilyas, Nasir Rajpoot

In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.

whole slide images

Generalized orderless pooling performs implicit salient matching

2 code implementations ICCV 2017 Marcel Simon, Yang Gao, Trevor Darrell, Joachim Denzler, Erik Rodner

In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training.

Fast Learning and Prediction for Object Detection using Whitened CNN Features

no code implementations10 Apr 2017 Björn Barz, Erik Rodner, Christoph Käding, Joachim Denzler

We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection.

Feature Engineering object-detection +1

Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes

no code implementations19 Dec 2016 Christoph Käding, Erik Rodner, Alexander Freytag, Joachim Denzler

The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet.

Active Learning

ImageNet pre-trained models with batch normalization

3 code implementations5 Dec 2016 Marcel Simon, Erik Rodner, Joachim Denzler

Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches.

Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches

no code implementations21 Oct 2016 Erik Rodner, Marcel Simon, Robert B. Fisher, Joachim Denzler

In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition.

Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets

no code implementations10 Oct 2016 Manuel Amthor, Erik Rodner, Joachim Denzler

We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application.

Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks

no code implementations14 Jun 2016 Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler

Neural networks and especially convolutional neural networks are of great interest in current computer vision research.

Semantic Segmentation

Fine-grained Recognition Datasets for Biodiversity Analysis

no code implementations3 Jul 2015 Erik Rodner, Marcel Simon, Gunnar Brehm, Stephanie Pietsch, J. Wolfgang Wägele, Joachim Denzler

In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis.

Classification Fine-Grained Image Classification +1

Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances

no code implementations CVPR 2015 Christoph Kading, Alexander Freytag, Erik Rodner, Paul Bodesheim, Joachim Denzler

In active learning, all categories occurring in collected data are usually assumed to be known in advance and experts should be able to label every requested instance.

Active Learning

Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks

no code implementations ICCV 2015 Marcel Simon, Erik Rodner

Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object.

Data Augmentation Model Discovery

Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding

no code implementations23 Feb 2015 Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler

Classifying single image patches is important in many different applications, such as road detection or scene understanding.

Scene Understanding

Part Detector Discovery in Deep Convolutional Neural Networks

1 code implementation12 Nov 2014 Marcel Simon, Erik Rodner, Joachim Denzler

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination.

General Classification

ARTOS -- Adaptive Real-Time Object Detection System

no code implementations10 Jul 2014 Björn Barz, Erik Rodner, Joachim Denzler

ARTOS is all about creating, tuning, and applying object detection models with just a few clicks.

object-detection Real-Time Object Detection

Instance-weighted Transfer Learning of Active Appearance Models

no code implementations CVPR 2014 Daniel Haase, Erik Rodner, Joachim Denzler

Therefore, we present a transfer learning method that is able to learn from related training data using an instance-weighted transfer technique.

Transfer Learning

Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012

no code implementations17 Oct 2013 Christoph Göring, Alexander Freytag, Erik Rodner, Joachim Denzler

In this paper, we tackle the problem of visual categorization of dog breeds, which is a surprisingly challenging task due to simultaneously present low interclass distances and high intra-class variances.

Kernel Null Space Methods for Novelty Detection

no code implementations CVPR 2013 Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, Joachim Denzler

In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance.

Density Estimation Object Recognition

Efficient Learning of Domain-invariant Image Representations

no code implementations15 Jan 2013 Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, Kate Saenko

We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers.

Representation Learning

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