What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Pattern recognition, Computer vision, Optical character recognition and Speech recognition are his primary areas of study.
Faisal Shafait regularly ties together related areas like Data mining in his Artificial intelligence studies.
His work on Scale-space segmentation, Segmentation and Image as part of general Computer vision study is frequently linked to Wearable computer, bridging the gap between disciplines.
His studies deal with areas such as Image processing and Algorithm as well as Segmentation.
His Optical character recognition study combines topics from a wide range of disciplines, such as Document processing and Pattern recognition.
His research integrates issues of Feature extraction and Natural language processing in his study of Speech recognition.
His most cited work include:
- ICDAR 2013 Robust Reading Competition (661 citations)
- ICDAR 2015 competition on Robust Reading (541 citations)
- ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images (338 citations)
What are the main themes of his work throughout his whole career to date?
Faisal Shafait mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Optical character recognition.
His Artificial intelligence research incorporates elements of Machine learning and Natural language processing.
His Pattern recognition research integrates issues from Pixel and Image.
His Computer vision research is multidisciplinary, incorporating elements of Scale and Word error rate.
His Deep learning research is multidisciplinary, relying on both Artificial neural network, Identification and Image processing.
Faisal Shafait has included themes like Document layout analysis, Speech recognition and Pattern recognition in his Optical character recognition study.
He most often published in these fields:
- Artificial intelligence (79.62%)
- Pattern recognition (37.91%)
- Computer vision (35.55%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (79.62%)
- Deep learning (14.69%)
- Pattern recognition (37.91%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Deep learning, Pattern recognition, Artificial neural network and Machine learning.
Faisal Shafait interconnects Computer vision and Natural language processing in the investigation of issues within Artificial intelligence.
His research in Deep learning intersects with topics in Object detection, Convolutional neural network and Identification.
His work deals with themes such as Feature, Pixel, Representation, Handwriting and Extreme learning machine, which intersect with Pattern recognition.
Faisal Shafait combines subjects such as Data mining, Embedding, Semantic similarity and Leverage with his study of Artificial neural network.
His biological study spans a wide range of topics, including Document processing and Feature extraction.
Between 2017 and 2021, his most popular works were:
- Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data (73 citations)
- Dense 3D Face Correspondence (40 citations)
- Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Faisal Shafait mainly investigates Artificial intelligence, Deep learning, Pattern recognition, Artificial neural network and Machine learning.
He integrates many fields in his works, including Artificial intelligence and Underwater.
His Deep learning research incorporates themes from Convolutional neural network, Table and Heuristic.
His Pattern recognition study combines topics in areas such as Margin, Pixel, Noise and Multispectral image.
His studies deal with areas such as Classifier, Incremental learning and Forgetting as well as Artificial neural network.
The Machine learning study combines topics in areas such as Document processing and Feature extraction.
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