63 citations · 153 across the 14 of their papers we have counts for
14 papers
Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset
Mathias Zinnen, Prathmesh Madhu, Inger Leemans +6
Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-g…
ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings
Alexander Schmidt, Prathmesh Madhu, Andreas Maier +2
Image enhancement algorithms are very useful for real world computer vision tasks where image resolution is often physically limited by the sensor size. While state-of-the-art deep…
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification
Adarsh Bhandary Panambur, Hui Yu, Sheethal Bhat +3
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introd…
SniffyArt: The Dataset of Smelling Persons
Mathias Zinnen, Azhar Hussian, Hang Tran +3
Smell gestures play a crucial role in the investigation of past smells in the visual arts yet their automated recognition poses significant challenges. This paper introduces the Sn…
Transfer Learning for Olfactory Object Detection
Mathias Zinnen, Prathmesh Madhu, Peter Bell +2
We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining. We find that including an additional stage of object-detectio…
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition
Mathias Zinnen, Prathmesh Madhu, Ronak Kosti +3
The Odeuropa Challenge on Olfactory Object Recognition aims to foster the development of object detection in the visual arts and to promote an olfactory perspective on digital heri…
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning
Adarsh Bhandary Panambur, Prathmesh Madhu, Andreas Maier
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy plann…
An unobtrusive quality supervision approach for medical image annotation
Sonja Kunzmann, Mathias Öttl, Prathmesh Madhu +2
Image annotation is one essential prior step to enable data-driven algorithms. In medical imaging, having large and reliably annotated data sets is crucial to recognize various dis…
ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas
Prathmesh Madhu, Tilman Marquart, Ronak Kosti +4
Image compositions are helpful in the study of image structures and assist in discovering the semantics of the underlying scene portrayed across art forms and styles. With the digi…
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning
Adarsh Bhandary Panambur, Prathmesh Madhu, Andreas Maier
Early detection and analysis of calcifications in mammogram images is crucial in a breast cancer diagnosis workflow. Management of calcifications that require immediate follow-up a…
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer Learning
Prathmesh Madhu, Angel Villar-Corrales, Ronak Kosti +5
Human pose estimation (HPE) is a central part of understanding the visual narration and body movements of characters depicted in artwork collections, such as Greek vase paintings.…
Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors
Prathmesh Madhu, Tilman Marquart, Ronak Kosti +3
Image compositions as a tool for analysis of artworks is of extreme significance for art historians. These compositions are useful in analyzing the interactions in an image to stud…
Recognizing Characters in Art History Using Deep Learning
Prathmesh Madhu, Ronak Kosti, Lara Mührenberg +3
In the field of Art History, images of artworks and their contexts are core to understanding the underlying semantic information. However, the highly complex and sophisticated repr…
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
Christian Marzahl, Marc Aubreville, Christof A. Bertram +13
Purpose: Exercise-induced pulmonary hemorrhage (EIPH) is a common syndrome in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a…