What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Fatih Porikli focuses on Artificial intelligence, Pattern recognition, Computer vision, Object and Benchmark.
Fatih Porikli focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Range.
His Pattern recognition research incorporates elements of Contextual image classification, Similarity and Feature.
His studies in Computer vision integrate themes in fields like Lie group and Robustness.
He interconnects Sequence and Trajectory in the investigation of issues within Object.
His Benchmark research incorporates themes from Data mining and Measure.
His most cited work include:
- Region Covariance : A Fast Descriptor for Detection and Classification (965 citations)
- Pedestrian Detection via Classification on Riemannian Manifolds (764 citations)
- Integral histogram: a fast way to extract histograms in Cartesian spaces (663 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Object and Pixel.
His study looks at the intersection of Artificial intelligence and topics like Machine learning with Contextual image classification.
Computer vision is closely attributed to Frame in his research.
His study in Classifier, Feature extraction, Convolutional neural network, Training set and Support vector machine is carried out as part of his studies in Pattern recognition.
His Object research includes themes of Sequence and Benchmark.
The Discriminative model study combines topics in areas such as Feature learning and Face.
He most often published in these fields:
- Artificial intelligence (90.68%)
- Computer vision (44.07%)
- Pattern recognition (45.97%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (90.68%)
- Pattern recognition (45.97%)
- Computer vision (44.07%)
In recent papers he was focusing on the following fields of study:
Fatih Porikli spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Machine learning.
All of his Artificial intelligence and Feature, Deep learning, Feature extraction, Convolutional neural network and Segmentation investigations are sub-components of the entire Artificial intelligence study.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Pooling, Pixel, Face, Visualization and Robustness.
His Image, Deblurring, Image restoration and Motion study in the realm of Computer vision connects with subjects such as Stylized fact.
His biological study spans a wide range of topics, including Video tracking, Representation, Autoencoder and Feature learning.
His Machine learning research focuses on Object detection and how it connects with Adversarial system.
Between 2016 and 2021, his most popular works were:
- Going deeper into action recognition (328 citations)
- Saliency-Aware Video Object Segmentation (248 citations)
- See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks (169 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Deep learning.
His study in Artificial intelligence focuses on Discriminative model, Feature extraction, Image, Convolutional neural network and Object.
The concepts of his Pattern recognition study are interwoven with issues in Pixel, Feature and Robustness.
His work on Image restoration as part of general Computer vision research is often related to Wearable computer, thus linking different fields of science.
His Machine learning study combines topics in areas such as Range and Heuristic.
His Deep learning research is multidisciplinary, relying on both Segmentation, Image segmentation, Cognitive neuroscience of visual object recognition, Artificial neural network and Feature vector.
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