How Multi-Illuminant Scenes Affect Automatic Colour Balancing

Many illumination-estimation methods are based on the assumption that the imaged scene is lit by a single course of illumination; however, this assumption is often violated in practice. We investigate the effect this has on a suite of illumination-estimation methods by manually sorting the Gehler et al. ColorChecker set of 568 images into the 310 of them that are approximately single-illuminant and the 258 that are clearly multiple-illuminant and comparing the performance of the various methods on the two sets. The Grayworld, Spatio-Spectral-Statistics and Thin-Plate-Spline methods are relatively unaffected, but the other methods are all affected to varying degrees

Skin Lesion Segmentation and Classification using Deep Learning and Handcrafted Features

Skin cancer is one of cancer type that has a significant impact on society in the United States and across the world. Recently, several Computer-Aided Diagnosis (CAD) system papers have been presented. However, there is still an opportunity for further development in the accuracy of its diagnosis. In this research, we propose an algorithm for skin cancer segmentation and classification at a more treatable stage. Our current approach is computationally efficient and combines information from both deep learning and handcrafted features. Our system creates robust hybrid features that have a stronger discrimination ability than single method features. These features are used as inputs to a decision-making model that is based on a Support Vector Machine (SVM) classifier. Our results evaluated online validation and test databases. Our score was 0.841 on the validation dataset and 0.701 on the test dataset for the classification task. We participated in the ISIC Challenge 2018, being ranked 59th for disease classification and 85th for skin lesion segmentation out of 141 methods listed on the competition leaderboard. These statistics do not include the rankings of the groups who did not qualify for the leaderboard. Also, it is important to note that many of the successful methods that were ranked highly used additional external data for training. The ISIC 2018 competition does not provide the external data that they used. We only utilized the competition which provided data for training.https://ecommons.udayton.edu/stander_posters/2616/thumbnail.jp

Color Constancy Convolutional Autoencoder

In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table