DeepImSeq: Deep image sequencing for unsynchronized cameras

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dc.contributor.author Kanojia, Gagan
dc.contributor.author Raman, Shanmuganathan
dc.date.accessioned 2018-12-01T05:10:35Z
dc.date.available 2018-12-01T05:10:35Z
dc.date.issued 2018-11
dc.identifier.citation Kanojia, Gagan and Raman, Shanmuganathan,"DeepImSeq: Deep image sequencing for unsynchronized cameras", Pattern Recognition Letters, DOI: 10.1016/j.patrec.2018.11.014, Nov. 2018. en_US
dc.identifier.issn 0167-8655
dc.identifier.uri https://doi.org/10.1016/j.patrec.2018.11.014
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4035
dc.description.abstract Consider a set of n images of a dynamic scene captured using multiple hand-held devices. The order in which these images are captured is unknown. For n images, there can be n! possible arrangements, which makes this problem extremely challenging. In this work, we address the problem of sequencing such a set of unordered images in its temporal order. We propose an LSTM-based deep neural network which addresses this problem in an end-to-end manner. The network takes the set of images as input and outputs their order of capture. We formulate the problem as a sequence-to-sequence mapping task, in which each image is mapped to its position in the ordered sequence. We do not provide any other information to the network apart from the input images. We show that the proposed approach obtains the state-of-the-art results on the standard dataset. Further, we show through experimental results that the network learns better when the target sequence is reversed.
dc.description.statementofresponsibility by Gagan Kanojia and Shanmuganathan Raman
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Image sequencing en_US
dc.subject Deep learning en_US
dc.subject Neural network en_US
dc.subject Pattern recognition en_US
dc.title DeepImSeq: Deep image sequencing for unsynchronized cameras en_US
dc.type Article en_US
dc.relation.journal Pattern Recognition Letters


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