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20182025
most citedLocalizing Small Apples in Complex Apple Orchard Environments

2 citations · 4 across the 12 of their papers we have counts for

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13 papers · 1 filter

cs.CV2025

SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition

Sven Schreiber, Noha Sarhan, Simone Frintrop +1

Isolated Sign Language Recognition (ISLR) approaches primarily rely on RGB data or signer pose information. However, combining these modalities often results in the loss of crucial…

cs.CV2025

TRUDI and TITUS: A Multi-Perspective Dataset and A Three-Stage Recognition System for Transportation Unit Identification

Emre Gülsoylu, André Kelm, Lennart Bengtson +5

Identifying transportation units (TUs) is essential for improving the efficiency of port logistics. However, progress in this field has been hindered by the lack of publicly availa…

cs.CV2025

ContextLoss: Context Information for Topology-Preserving Segmentation

Benedict Schacht, Imke Greving, Simone Frintrop +2

In image segmentation, preserving the topology of segmented structures like vessels, membranes, or roads is crucial. For instance, topological errors on road networks can significa…

cs.CV2024

SOS: Segment Object System for Open-World Instance Segmentation With Object Priors

Christian Wilms, Tim Rolff, Maris Hillemann +2

We propose an approach for Open-World Instance Segmentation (OWIS), a task that aims to segment arbitrary unknown objects in images by generalizing from a limited set of annotated…

cs.CV2024

AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection

Mykhailo Koshil, Tilman Wegener, Detlef Mentrup +2

Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. The task is to identify the presence of an anomaly given an im…

cs.CV2023★ 1 cited

SAD: Semi-supervised Small Apple Detection in Orchard Environments

Robert Johanson, Christian Wilms, Ole Johannsen +1

Crop detection is integral for precision agriculture applications such as automated yield estimation or fruit picking. However, crop detection, e.g., apple detection in orchard env…

cs.CV2023

High-Level Parallelism and Nested Features for Dynamic Inference Cost and Top-Down Attention

André Peter Kelm, Niels Hannemann, Bruno Heberle +5

This paper introduces a novel network topology that seamlessly integrates dynamic inference cost with a top-down attention mechanism, addressing two significant gaps in traditional…

cs.CV2023

Small, but important: Traffic light proposals for detecting small traffic lights and beyond

Tom Sanitz, Christian Wilms, Simone Frintrop

Traffic light detection is a challenging problem in the context of self-driving cars and driver assistance systems. While most existing systems produce good results on large traffi…

cs.CV2022★ 1 cited

Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask

Christian Wilms, Alexander Michael Gerlach, Rüdiger Schmitz +1

Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in me…

cs.CV2022★ 2 cited

Localizing Small Apples in Complex Apple Orchard Environments

Christian Wilms, Robert Johanson, Simone Frintrop

The localization of fruits is an essential first step in automated agricultural pipelines for yield estimation or fruit picking. One example of this is the localization of apples i…

cs.CV2021

DeepFH Segmentations for Superpixel-based Object Proposal Refinement

Christian Wilms, Simone Frintrop

Class-agnostic object proposal generation is an important first step in many object detection pipelines. However, object proposals of modern systems are rather inaccurate in terms…

cs.CV2021

Superpixel-based Refinement for Object Proposal Generation

Christian Wilms, Simone Frintrop

Precise segmentation of objects is an important problem in tasks like class-agnostic object proposal generation or instance segmentation. Deep learning-based systems usually genera…

cs.CV2018

AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects

Christian Wilms, Simone Frintrop

We propose a novel approach for class-agnostic object proposal generation, which is efficient and especially well-suited to detect small objects. Efficiency is achieved by scale-sp…