Anselm Haselhoff

458 total citations
15 papers, 256 citations indexed

About

Anselm Haselhoff is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Artificial Intelligence. According to data from OpenAlex, Anselm Haselhoff has authored 15 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 4 papers in Automotive Engineering and 4 papers in Artificial Intelligence. Recurrent topics in Anselm Haselhoff's work include Video Surveillance and Tracking Methods (8 papers), Autonomous Vehicle Technology and Safety (4 papers) and Advanced Vision and Imaging (3 papers). Anselm Haselhoff is often cited by papers focused on Video Surveillance and Tracking Methods (8 papers), Autonomous Vehicle Technology and Safety (4 papers) and Advanced Vision and Imaging (3 papers). Anselm Haselhoff collaborates with scholars based in Germany. Anselm Haselhoff's co-authors include Anton Kummert, Jonas Albers, Karsten Berns, Mirko Meuter, Jonas Schneider and Stefan Müller-Schneiders and has published in prestigious journals such as Multidimensional Systems and Signal Processing, arXiv (Cornell University) and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

Anselm Haselhoff

15 papers receiving 241 citations

Peers

Anselm Haselhoff

CVPR

AE

AI

AE

MT

Chunmian Lin China

Todd N. Schoepflin United States

Christophe Blanc France

J. Giebel Germany

Daniel Goehring Germany

Manwen Liao Hong Kong

Ratnesh Kumar India

Antonio Prioletti Italy

Mirko Felisa Italy

Michele Zanin Italy

Chunmian Lin China

Anselm Haselhoff

201 ×1.2

CVPR

65 ×0.7

AE

45 ×0.7

AI

55 ×1.8

AE

33 ×1.4

MT

Citations per year, relative to Anselm Haselhoff Anselm Haselhoff (= 1×) peers Chunmian Lin

Countries citing papers authored by Anselm Haselhoff

Since Specialization

Citations

This map shows the geographic impact of Anselm Haselhoff's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anselm Haselhoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anselm Haselhoff more than expected).

Fields of papers citing papers by Anselm Haselhoff

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anselm Haselhoff. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anselm Haselhoff. The network helps show where Anselm Haselhoff may publish in the future.

Co-authorship network of co-authors of Anselm Haselhoff

This figure shows the co-authorship network connecting the top 25 collaborators of Anselm Haselhoff. A scholar is included among the top collaborators of Anselm Haselhoff based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anselm Haselhoff. Anselm Haselhoff is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

1.

Haselhoff, Anselm, et al.. (2021). Towards Black-Box Explainability with Gaussian Discriminant Knowledge Distillation. 21–28. 2 indexed citations

2.

Haselhoff, Anselm, et al.. (2020). Multivariate Confidence Calibration for Object Detection. arXiv (Cornell University). 1322–1330. 54 indexed citations

3.

Albers, Jonas, et al.. (2019). Random Forest on an Embedded Device for Real-time Machine State Classification. 1–5. 16 indexed citations

4.

Haselhoff, Anselm, et al.. (2017). Markov random field for image synthesis with an application to traffic sign recognition. 8. 1407–1412. 2 indexed citations

5.

Haselhoff, Anselm, et al.. (2011). MULTI-CAMERA PEDESTRIAN DETECTION BY MEANS OF TRACK-TO-TRACK FUSION AND CAR2CAR COMMUNICATION. 307–312. 2 indexed citations

6.

Haselhoff, Anselm, et al.. (2011). Multisensor data fusion for advanced driver assistance systems - the Active Safety Car project. 1. 1–5. 7 indexed citations

7.

Haselhoff, Anselm, et al.. (2010). Pedestrian crossing detecting as a part of an urban pedestrian safety system. 840–844. 19 indexed citations

8.

Haselhoff, Anselm & Anton Kummert. (2010). On filtering by means of generalized integral images: a review and applications. Multidimensional Systems and Signal Processing. 23(1-2). 291–312. 2 indexed citations

9.

Haselhoff, Anselm & Anton Kummert. (2010). On visual crosswalk detection for driver assistance systems. 883–888. 14 indexed citations

10.

Haselhoff, Anselm & Anton Kummert. (2009). An evolutionary optimized vehicle tracker in collaboration with a detection system. 2. 1–6. 18 indexed citations

11.

Haselhoff, Anselm & Anton Kummert. (2009). A vehicle detection system based on Haar and Triangle features. 261–266. 56 indexed citations

12.

Haselhoff, Anselm & Anton Kummert. (2009). 2D line filters for vision-based lane detection and tracking. 8 indexed citations

13.

Haselhoff, Anselm, et al.. (2008). A signal theoretic approach to measure the influence of image resolution for appearance-based vehicle detection. 11. 822–827. 16 indexed citations

14.

Haselhoff, Anselm, et al.. (2007). Radar-Vision Fusion with an Application to Car-Following using an Improved AdaBoost Detection Algorithm. 854–858. 18 indexed citations

15.

Haselhoff, Anselm, et al.. (2007). Radar-Vision Fusion For Vehicle Detection By Means Of Improved Haar-Like Feature And Adaboost Approach. Zenodo (CERN European Organization for Nuclear Research). 2070–2074. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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