@article{scholars16415,
           title = {SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images},
            note = {cited By 4},
          volume = {22},
          number = {17},
             doi = {10.3390/s22176552},
       publisher = {MDPI},
         journal = {Sensors},
            year = {2022},
          author = {Anwer, A. and Ainouz, S. and Saad, M. N. M. and Ali, S. S. A. and Meriaudeau, F.},
            issn = {14248220},
        abstract = {Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. Most modern techniques proposed are inadequate at dealing with real-world images taken under uncontrolled conditions with the presence of complex textures, multiple objects, and bright colours, resulting in reduced accuracy and false positives. To detect specular pixels in a wide variety of real-world images independent of the number, colour, or type of illuminating source, we propose an efficient Specular Segmentation (SpecSeg) network based on the U-net architecture that is expeditious to train on nominal-sized datasets. The proposed network can detect pixels strongly affected by specular highlights with a high degree of precision, as shown by comparison with the state-of-the-art methods. The technique proposed is trained on publicly available datasets and tested using a large selection of real-world images with highly encouraging results. {\^A}{\copyright} 2022 by the authors.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137597770&doi=10.3390\%2fs22176552&partnerID=40&md5=8b08d60fc30f4e00a590165488e0bc82},
        keywords = {Large dataset; Pixels; Textures, Condition; False positive; Highlights detection; Images segmentations; Modern techniques; Multiple objects; Network-based; Non trivial problems; Real-world image; Specular highlight, Image segmentation, article; false positive result; image segmentation}
}