2016 - IAPR P. Zamperoni Award Content Selection Using Frontalness of Multiple Frames
2014 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to the field of document image analysis and in recognition of service to the IAPR
2014 - IEEE Fellow For contributions to research and development of automatic analysis and processing of document page imaging
David Doermann mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Search engine indexing. His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning. David Doermann has researched Computer vision in several fields, including Tree and Perspective.
David Doermann has included themes like Image quality and Inference in his Pattern recognition study. His research in Image quality intersects with topics in Codebook, Distortion and Feature learning. His research in Image processing tackles topics such as Text processing which are related to areas like Motion estimation and Digital video.
David Doermann focuses on Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Information retrieval. His Artificial intelligence research includes themes of Machine learning and Natural language processing. His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification and Handwriting.
The various areas that he examines in his Feature extraction study include Image quality, Codebook and Feature. His Information retrieval study integrates concerns from other disciplines, such as Document clustering and Image retrieval. He interconnects Backpropagation and Deep learning in the investigation of issues within Convolutional neural network.
His primary scientific interests are in Artificial intelligence, Convolutional neural network, Pattern recognition, Artificial neural network and Deep learning. His study in the field of Feature and Feature extraction is also linked to topics like Differentiable function. His research integrates issues of Object, Object detection, Backpropagation and Filter in his study of Convolutional neural network.
His Pattern recognition study focuses on Segmentation in particular. The various areas that he examines in his Artificial neural network study include Complex system and Generalizability theory. His study explores the link between Layer and topics such as Event recognition that cross with problems in Computer vision.
His primary areas of study are Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Feature. Artificial intelligence is closely attributed to Task analysis in his work. In his research on the topic of Pattern recognition, Variation is strongly related with Image.
His Convolutional neural network research is multidisciplinary, relying on both Backpropagation and Filter. His work carried out in the field of Deep learning brings together such families of science as Algorithm and Entropy. His research in Feature intersects with topics in Image quality, Codebook, Data mining, Metric and Density estimation.
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Automatic text detection and tracking in digital video
Huiping Li;D. Doermann;O. Kia.
IEEE Transactions on Image Processing (2000)
Convolutional Neural Networks for No-Reference Image Quality Assessment
Le Kang;Peng Ye;Yi Li;David Doermann.
computer vision and pattern recognition (2014)
Text Detection and Recognition in Imagery: A Survey
Qixiang Ye;David Doermann.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)
Camera-based analysis of text and documents: a survey
Jian Liang;David Doermann;Huiping Li.
International Journal on Document Analysis and Recognition (2005)
Unsupervised feature learning framework for no-reference image quality assessment
Peng Ye;Jayant Kumar;Le Kang;David Doermann.
computer vision and pattern recognition (2012)
The Indexing and Retrieval of Document Images
Computer Vision and Image Understanding (1998)
Video summarization by curve simplification
Daniel DeMenthon;Vikrant Kobla;David Doermann.
acm multimedia (1998)
Progress in camera-based document image analysis
D. Doermann;Jian Liang;Huiping Li.
international conference on document analysis and recognition (2003)
Robust point matching for nonrigid shapes by preserving local neighborhood structures
Yefeng Zheng;D. Doermann.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)
No-Reference Image Quality Assessment Using Visual Codebooks
Peng Ye;D. Doermann.
IEEE Transactions on Image Processing (2012)
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