What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Djemel Ziou spends much of his time researching Artificial intelligence, Pattern recognition, Mixture model, Image retrieval and Unsupervised learning.
His studies in Artificial intelligence integrate themes in fields like Data mining, Aerial survey and Computer vision.
His study in the field of Image segmentation is also linked to topics like Generalized Dirichlet distribution.
His Mixture model study incorporates themes from Data modeling, Algorithm, Segmentation and Bayesian probability.
His Image retrieval research is multidisciplinary, incorporating elements of Multinomial distribution, Probabilistic logic and Information retrieval.
The study incorporates disciplines such as Automatic summarization and Expectation–maximization algorithm in addition to Unsupervised learning.
His most cited work include:
- Image Quality Metrics: PSNR vs. SSIM (954 citations)
- A comparative analysis of image fusion methods (669 citations)
- Edge Detection Techniques-An Overview (610 citations)
What are the main themes of his work throughout his whole career to date?
Djemel Ziou mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Mixture model and Algorithm.
His biological study deals with issues like Machine learning, which deal with fields such as Data mining.
His Pattern recognition research incorporates elements of Latent Dirichlet allocation and Cluster analysis.
His work is connected to Pixel, Image quality, Image processing, Edge detection and Histogram, as a part of Computer vision.
His Mixture model research incorporates themes from Automatic summarization, Statistical model, Model selection and Expectation–maximization algorithm.
His Algorithm study combines topics from a wide range of disciplines, such as Image and Curvature.
He most often published in these fields:
- Artificial intelligence (70.24%)
- Pattern recognition (40.08%)
- Computer vision (36.90%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (70.24%)
- Pattern recognition (40.08%)
- Computer vision (36.90%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Mixture model and Cluster analysis.
Much of his study explores Artificial intelligence relationship to Machine learning.
The Pattern recognition study combines topics in areas such as Bag-of-words model in computer vision, Invariant and Robustness.
As a part of the same scientific family, Djemel Ziou mostly works in the field of Computer vision, focusing on Feature vector and, on occasion, Digital image, JPEG, Embedding, Steganography and Multinomial logistic regression.
His Mixture model study integrates concerns from other disciplines, such as Compositional data, Mixing, Bounded function, Constant and Discriminative model.
He combines subjects such as Roundness and Image retrieval with his study of Image quality.
Between 2013 and 2021, his most popular works were:
- Segmentation of Terahertz imaging using k-means clustering based on ranked set sampling (28 citations)
- Variational Bayesian inference for infinite generalized inverted Dirichlet mixtures with feature selection and its application to clustering (23 citations)
- Variational learning of finite Dirichlet mixture models using component splitting (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Computer vision, Cluster analysis, Pattern recognition and Mixture model.
Many of his studies involve connections with topics such as Function and Artificial intelligence.
His work deals with themes such as Head and Feature vector, which intersect with Computer vision.
His study explores the link between Cluster analysis and topics such as Simple random sample that cross with problems in Segmentation, Sampling design and Feature learning.
His Pattern recognition research includes themes of Generalized normal distribution, JPEG and Steganalysis.
His work carried out in the field of Mixture model brings together such families of science as Machine learning and Model selection.
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