What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Block Truncation Coding and Feature extraction.
His study in the field of Information hiding and Embedding also crosses realms of Scaling.
Jing-Ming Guo works mostly in the field of Information hiding, limiting it down to topics relating to Digital watermarking and, in certain cases, Halftone and Watermark, as a part of the same area of interest.
His work in the fields of Computer vision, such as Image, Image processing, Pixel and Gaussian noise, intersects with other areas such as Payload.
His work deals with themes such as Image quality, Vector quantization and Block code, which intersect with Block Truncation Coding.
The Image quality study combines topics in areas such as Algorithm and Data compression.
His most cited work include:
- License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques (130 citations)
- Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding (90 citations)
- Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection (86 citations)
What are the main themes of his work throughout his whole career to date?
Jing-Ming Guo mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Image quality and Halftone.
Digital watermarking, Feature extraction, Ordered dithering, Block Truncation Coding and Pixel are the core of his Artificial intelligence study.
The Digital watermarking study which covers Watermark that intersects with Computational complexity theory and Singular value decomposition.
His Pattern recognition research integrates issues from Contextual image classification, Feature and Image retrieval.
Theoretical computer science and Electronic engineering is closely connected to Algorithm in his research, which is encompassed under the umbrella topic of Image quality.
His Halftone study combines topics in areas such as Error diffusion, Iterative reconstruction and Thresholding.
He most often published in these fields:
- Artificial intelligence (80.30%)
- Computer vision (55.56%)
- Pattern recognition (39.39%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (80.30%)
- Pattern recognition (39.39%)
- Image (11.62%)
In recent papers he was focusing on the following fields of study:
Jing-Ming Guo mainly investigates Artificial intelligence, Pattern recognition, Image, Deep learning and Halftone.
His Artificial intelligence study frequently draws connections to adjacent fields such as Computer vision.
His Pattern recognition study incorporates themes from Watermark, Matching, Pose, Fractal and Digital watermarking.
His work carried out in the field of Image brings together such families of science as Secret sharing and Security level.
He interconnects Image processing, Discriminative model and Eye tracking in the investigation of issues within Deep learning.
His studies in Halftone integrate themes in fields like Image quality, Feature vector and Image translation.
Between 2017 and 2021, his most popular works were:
- Improved Beta Chaotic Image Encryption for Multiple Secret Sharing (15 citations)
- A Note on Multiple Secret Sharing Using Chinese Remainder Theorem and Exclusive-OR (13 citations)
- Multi-Person Pose Estimation via Multi-Layer Fractal Network and Joints Kinship Pattern (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Image, Pattern recognition, Secret sharing and Deep learning.
His study in Computer vision extends to Artificial intelligence with its themes.
The study incorporates disciplines such as Fractal, Pose, Feature and Security level in addition to Image.
His Pattern recognition study integrates concerns from other disciplines, such as Image quality, Noise and Block Truncation Coding.
His Secret sharing research is multidisciplinary, incorporating elements of Lossy compression, Chinese remainder theorem, Encryption and Grayscale.
In his study, which falls under the umbrella issue of Deep learning, Video tracking, Tracking and Benchmark is strongly linked to Eye tracking.
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