Anastasios N. Venetsanopoulos

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Statistics
• Computer vision

His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Image processing and Algorithm. His Artificial intelligence study incorporates themes from Machine learning and Noise. His Pattern recognition research is multidisciplinary, relying on both Regularization and Multilinear map.

Anastasios N. Venetsanopoulos interconnects Pixel, Adaptive filter and Euclidean distance in the investigation of issues within Image processing. His Adaptive filter research includes themes of Brightness and Signal processing. His biological study spans a wide range of topics, including Electronic engineering, Filter, Detector and Robustness.

His most cited work include:

• Nonlinear Digital Filters (977 citations)
• Nonlinear Digital Filters: Principles and Applications (908 citations)
• Color Image Processing and Applications (818 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Image processing. Artificial intelligence connects with themes related to Filter in his study. Anastasios N. Venetsanopoulos has researched Filter in several fields, including Adaptive filter and Signal processing.

His work carried out in the field of Algorithm brings together such families of science as Digital filter, Mathematical optimization and Control theory. His Digital filter research includes elements of Electronic engineering, Finite impulse response and Realization. His Pattern recognition research incorporates elements of Facial recognition system and Machine learning.

He most often published in these fields:

• Artificial intelligence (54.08%)
• Computer vision (36.88%)
• Algorithm (23.58%)

What were the highlights of his more recent work (between 2005-2018)?

• Artificial intelligence (54.08%)
• Pattern recognition (23.94%)
• Computer vision (36.88%)

In recent papers he was focusing on the following fields of study:

Anastasios N. Venetsanopoulos mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Principal component analysis. His work in Artificial intelligence addresses subjects such as Multilinear map, which are connected to disciplines such as Projection. Anastasios N. Venetsanopoulos combines topics linked to Subspace topology with his work on Pattern recognition.

The study incorporates disciplines such as Mammography and Gait analysis in addition to Computer vision. His studies in Feature extraction integrate themes in fields like Radial basis function kernel, Support vector machine, Contextual image classification, Wavelet and Pattern recognition. While the research belongs to areas of Filter, Anastasios N. Venetsanopoulos spends his time largely on the problem of Noise, intersecting his research to questions surrounding Image processing.

Between 2005 and 2018, his most popular works were:

• MPCA: Multilinear Principal Component Analysis of Tensor Objects (658 citations)
• Kernel-Based Positioning in Wireless Local Area Networks (355 citations)
• A survey of multilinear subspace learning for tensor data (284 citations)

In his most recent research, the most cited papers focused on:

• Artificial intelligence
• Statistics
• Computer vision

Anastasios N. Venetsanopoulos spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Facial recognition system. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Gait and Multilinear map. His research investigates the connection between Multilinear map and topics such as Principal component analysis that intersect with problems in Projection.

In general Pattern recognition study, his work on Multilinear principal component analysis, Radial basis function kernel, Kernel embedding of distributions and Kernel method often relates to the realm of Context, thereby connecting several areas of interest. His studies deal with areas such as Gait analysis and Matched filter as well as Computer vision. His Edge detection study combines topics in areas such as Image noise, Noise reduction, Color histogram and Robustness.