James Okae

Computer Engineering

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Research Summary

(inferred from publications by AI)

This researcher has made significant contributions to the field of vision-based systems, advancing the integration of advanced computational techniques into real-world applications across diverse domains. Their work focuses on developing innovative methods for stereo matching and depth estimation, with a particular emphasis on leveraging mutual interaction principles for robust system performance. The researcher's research is characterized by its interdisciplinary approach, merging concepts from computer vision, robotics, and energy management to address challenges in smart grid optimization and image processing.

Research Themes

All (11 Papers)

Physical Sciences (11 Papers)

All Papers

Robust Scale-Aware Stereo Matching Network(2021)

Robust Stereo Matching Using Discriminative Multilevel Features and Multimodal Bifurcated Cost Volume Network(2023)

Improved stereo matching framework with embedded multilevel attention(2022)

Robust statistical approach to stereo disparity maps denoising and refinement(2020)

DMCNet: Toward Lightweight Volumetric Stereo Matching(2024)

Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion(2023)

A novel depth estimation approach based on bidirectional matching for stereo vision systems(2020)

The Design and Realization of Smart Energy Management System based on Supply-Demand Coordination(2017)

Optimization of stereo vision depth estimation using edge-based disparity map(2017)

Concealed multiscale feature extraction network for automatic four-bar target detection in infrared imaging(2022)

A Novel Method for Night-Time Single Image Dehazing(2019)

Collaboration Network

436baea1-cb26-48d9-8c6d-d48bfdadc350

Research Collaboration Map

Collaboration Frequency

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.