Hicham Tribak

Papers

Total Citations

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About

Hicham Tribak is a researcher whose work sits at the intersection of computer vision and industrial quality assessment, with a particular focus on practical, real-world applications. His most cited paper, "Remote QR code recognition based on HOG and SVM classifiers" (2016, 10 citations), addresses the growing need for efficient data storage and retrieval in commercial settings. By applying Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classifiers, Tribak developed a robust method for recognizing QR codes from a distance, enhancing the utility of these 2D barcodes in product tracking and web redirection. This work demonstrates his ability to solve tangible problems in automation and logistics. In a related vein, his paper "Solar Panels Frames Quality Assessment" (2017, 2 citations) tackles the critical challenge of maintaining solar plant efficiency. Here, Tribak employs image stitching techniques to create panoramic views of solar arrays, enabling supervisors to detect cracks, dust, and other defects that can compromise energy production. This contribution underscores his commitment to sustainable energy infrastructure. Though his citation counts are modest, Tribak’s research is notable for its direct applicability, bridging the gap between algorithmic innovation and industrial needs, making him a valuable contributor to applied computer vision.