What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Alexander C. Loui mostly deals with Artificial intelligence, Computer vision, Information retrieval, Image and Digital image.
Alexander C. Loui studies Artificial intelligence, namely Cluster analysis.
His Histogram, Object and Image quality study in the realm of Computer vision interacts with subjects such as Color histogram.
His research integrates issues of Annotation, Classifier and Feature sharing in his study of Information retrieval.
His Digital image research integrates issues from Page layout and Computer graphics.
His research in Pattern recognition intersects with topics in Pixel and Automatic indexing.
His most cited work include:
- Consumer video understanding: a benchmark database and an evaluation of human and machine performance (253 citations)
- Using digital objects organized according to a histogram timeline (239 citations)
- Albuming method with automatic page layout (236 citations)
What are the main themes of his work throughout his whole career to date?
Alexander C. Loui spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Image and Digital image.
His studies deal with areas such as Event and Machine learning as well as Artificial intelligence.
His work in the fields of Pattern recognition, such as Feature vector, overlaps with other areas such as Group.
His Image study combines topics from a wide range of disciplines, such as Data mining and Information retrieval.
His study in Information retrieval is interdisciplinary in nature, drawing from both Annotation, Multimedia, Image retrieval and Feature extraction.
His Digital image research is multidisciplinary, relying on both Computer hardware, Digital image processing, Computer graphics and Encoding.
He most often published in these fields:
- Artificial intelligence (63.59%)
- Computer vision (43.08%)
- Pattern recognition (28.72%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (63.59%)
- Computer vision (43.08%)
- Image (23.59%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Image, Pattern recognition and Segmentation.
He works mostly in the field of Artificial intelligence, limiting it down to topics relating to Frame and, in certain cases, Key.
His studies link Salient with Computer vision.
The concepts of his Image study are interwoven with issues in Algorithm and Normalization.
His Similarity study in the realm of Pattern recognition connects with subjects such as Weighting, Code word and Digital video.
His study looks at the relationship between Segmentation and topics such as Salient objects, which overlap with Tracking.
Between 2012 and 2020, his most popular works were:
- Kinship classification by modeling facial feature heredity (75 citations)
- Identifying scene boundaries using group sparsity analysis (23 citations)
- Imaging workflow using facial and non-facial features (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Feature and Image.
Face, Deep learning, Block-matching algorithm, Similarity and Region of interest are the core of his Artificial intelligence study.
His Face research is multidisciplinary, incorporating elements of Contextual image classification and Machine learning, Feature.
His study in the fields of Artifact, Face detection and Color correction under the domain of Computer vision overlaps with other disciplines such as Pupil and Eye color.
His work deals with themes such as Tree, Text mining, Convolutional neural network and Image retrieval, which intersect with Feature.
Many of his research projects under Image are closely connected to Weighting and Impact score with Weighting and Impact score, tying the diverse disciplines of science together.
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