What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Horst Bischof mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Robustness and Machine learning.
His is doing research in Boosting, Video tracking, Object detection, Image segmentation and Classifier, both of which are found in Artificial intelligence.
His Computer vision research incorporates elements of Detector and Benchmark.
His work focuses on many connections between Pattern recognition and other disciplines, such as Facial recognition system, that overlap with his field of interest in Pose.
His studies deal with areas such as Outlier, Stereopsis, Histogram, Optical flow and Mobile computing as well as Robustness.
His Machine learning research is multidisciplinary, incorporating perspectives in Optimization problem, Task and Metric.
His most cited work include:
- The Photodetector Array Camera and Spectrometer (PACS) on the Herschel Space Observatory (2260 citations)
- Computer Vision -- Eccv 2006 (1427 citations)
- Large scale metric learning from equivalence constraints (1359 citations)
What are the main themes of his work throughout his whole career to date?
Horst Bischof mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Segmentation.
His study involves Robustness, Object detection, Image, Boosting and Object, a branch of Artificial intelligence.
Tracking, 3D reconstruction, Image segmentation, Pose and Cognitive neuroscience of visual object recognition are among the areas of Computer vision where he concentrates his study.
Horst Bischof interconnects Contextual image classification and Outlier in the investigation of issues within Pattern recognition.
Horst Bischof works in the field of Machine learning, namely Random forest.
His Segmentation research focuses on Scale-space segmentation in particular.
He most often published in these fields:
- Artificial intelligence (75.15%)
- Computer vision (45.31%)
- Pattern recognition (28.26%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (75.15%)
- Computer vision (45.31%)
- Pattern recognition (28.26%)
In recent papers he was focusing on the following fields of study:
Horst Bischof spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, 3D reconstruction and Machine learning.
His Segmentation, Pose, Convolutional neural network, Image and Object study are his primary interests in Artificial intelligence.
His Computer vision study combines topics in areas such as Computer graphics and Benchmark.
The study incorporates disciplines such as Embedding, Landmark, Outlier, Deep learning and Robustness in addition to Pattern recognition.
The concepts of his 3D reconstruction study are interwoven with issues in Point cloud, Photogrammetry, Pipeline, Line and Ground truth.
His Machine learning study incorporates themes from Training set, Inference and Synthetic data.
Between 2013 and 2021, his most popular works were:
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
- Fast and accurate image upscaling with super-resolution forests (444 citations)
- The Visual Object Tracking VOT2014 challenge results (329 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Horst Bischof mainly investigates Artificial intelligence, Computer vision, Pattern recognition, 3D reconstruction and Tracking.
His studies link Machine learning with Artificial intelligence.
Horst Bischof has researched Machine learning in several fields, including State, Task and Metric.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Benchmark.
His work deals with themes such as Kernel, Deep learning, Outlier and Robustness, which intersect with Pattern recognition.
The 3D reconstruction study combines topics in areas such as Point cloud, Computer graphics, Image processing, Pipeline and Visualization.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
