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Deep learning, a branch of machine learning, has been gaining ground in many research fields as well as practical applications.Such ongoing boom can be traced back mainly to the availability and the affordability of potential processing facilities, which were not widely accessible than just a decade ago for instance.IUPRAI-ICVGIP will form a committee of 3-5 experts every year for shortlisting and selection of the award.
Each nomination should be forwarded by the thesis advisor (not the student).
Nomination should be one zipped file from the thesis advisor, which should include (all information will be kept strictly confidential): Important Dates Submission deadline: 8th December 2018 Review Process The nomination letter, list of publications and summary of the thesis will be used in initial screening phase to shortlist dissertation for subsequent in-depth evaluation.
Further, in order to improve the representativeness of the extracted features, we reinforce them with a feature learning stage by means of an autoencoder model.
This latter is topped with a logistic regression layer in order to detect the presence of objects if any.
Although it has demonstrated cutting-edge performance widely in computer vision, and particularly in object recognition and detection, deep learning is yet to find its way into other research areas.
Furthermore, the performance of deep learning models has a strong dependency on the way in which these latter are designed/tailored to the problem at hand.Such method has the advantage of being fast and adequate for applications that characterized by small datasets.Face recognition (FR) offers unmatched advantages as compared to other biometrics, such as easy access or needless explicit cooperation from users, and today, it has attained the reliability and the maturity required by real applications .This, thereby, raises not only precision concerns but also processing overheads.The success and applicability of a deep learning system relies jointly on both components.This raises questions on its applicability to unidimensional data.Thus, a third contribution of this thesis is devoted to the design of a unidimensional architecture of the CNN, which is applied to spectroscopic data.In a second topic, we suggest to exploit the same model, i.e., autoencoder in the context of cloud removal in remote sensing images.Briefly, the model is learned on a cloud-free image pertaining to a certain geographical area, and applied afterwards on another cloud-contaminated image, acquired at a different time instant, of the same area.Only English language versions of the thesis will be accepted.Nomination Process The deadline for nomination is 8th December, 2018.