What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Pixel and Feature extraction.
His study in Sobel operator, Object detection, Edge detection, Image processing and Cluster analysis falls within the category of Artificial intelligence.
In his research, Connected component and Connected-component labeling is intimately related to k-means clustering, which falls under the overarching field of Object detection.
His Pattern recognition research is multidisciplinary, incorporating elements of Video tracking, Facial recognition system, Face and Projection.
The concepts of his Computer vision study are interwoven with issues in Search engine indexing and Graphics.
His research investigates the connection with Pixel and areas like Wavelet which intersect with concerns in Font, Contrast and Image resolution.
His most cited work include:
- A Laplacian Approach to Multi-Oriented Text Detection in Video (242 citations)
- Recognizing Text with Perspective Distortion in Natural Scenes (183 citations)
- (2D)2LDA: An efficient approach for face recognition (134 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Pixel and Feature extraction are his primary areas of study.
Artificial intelligence is closely attributed to Character in his study.
Palaiahnakote Shivakumara works mostly in the field of Pattern recognition, limiting it down to topics relating to Benchmark and, in certain cases, Convolutional neural network.
His Orientation, Segmentation, Image processing and Optical character recognition study, which is part of a larger body of work in Computer vision, is frequently linked to Block, bridging the gap between disciplines.
Palaiahnakote Shivakumara usually deals with Pixel and limits it to topics linked to Line and Point.
His Feature extraction research focuses on Identification and how it connects with Handwriting.
He most often published in these fields:
- Artificial intelligence (94.79%)
- Pattern recognition (72.92%)
- Computer vision (48.44%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (94.79%)
- Pattern recognition (72.92%)
- Pixel (37.50%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Pixel, Benchmark and Computer vision.
In his research, Palaiahnakote Shivakumara performs multidisciplinary study on Artificial intelligence and Process.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Contextual image classification, Canny edge detector, Deep learning and Invariant.
His Pixel research incorporates themes from Histogram, Line and Discrete cosine transform.
His research in Benchmark intersects with topics in Discriminative model, Feature and Character.
His work on Orientation as part of general Computer vision study is frequently linked to License, Wireless sensor network and Quality, bridging the gap between disciplines.
Between 2018 and 2021, his most popular works were:
- Graphology based handwritten character analysis for human behaviour identification (25 citations)
- Exact String Matching Algorithms: Survey, Issues, and Future Research Directions (20 citations)
- Multi-Script-Oriented Text Detection and Recognition in Video/Scene/Born Digital Images (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Palaiahnakote Shivakumara spends much of his time researching Artificial intelligence, Pattern recognition, Cluster analysis, k-nearest neighbors algorithm and Benchmark.
Artificial intelligence is closely attributed to Computer vision in his research.
The various areas that he examines in his Pattern recognition study include Character and Text recognition.
His work deals with themes such as Facial recognition system, Classification rate, Entropy and Biometrics, which intersect with Cluster analysis.
His Benchmark research is multidisciplinary, relying on both Canny edge detector, Segmentation and Keyword spotting.
His Pixel study integrates concerns from other disciplines, such as Bhattacharyya distance, k-means clustering, Deblurring and Minimum bounding box.
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