F
Fatih Porikli
Researcher at Australian National University
Publications - 444
Citations - 25662
Fatih Porikli is an academic researcher from Australian National University. The author has contributed to research in topics: Video tracking & Computer science. The author has an hindex of 66, co-authored 412 publications receiving 20807 citations. Previous affiliations of Fatih Porikli include Commonwealth Scientific and Industrial Research Organisation & Huawei.
Papers
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Book ChapterDOI
Region covariance: a fast descriptor for detection and classification
TL;DR: A fast method for computation of covariances based on integral images, and the performance of the covariance features is superior to other methods, as it is shown, and large rotations and illumination changes are also absorbed by the covariances matrix.
Journal Article
Region Covariance : A Fast Descriptor for Detection and Classification
TL;DR: In this paper, a fast method for computation of covariance matrices based on integral images is described, which is more general than the image sums or histograms, which were already published before, and with a series of integral images the covariances are obtained by a few arithmetic operations.
Journal ArticleDOI
Pedestrian Detection via Classification on Riemannian Manifolds
TL;DR: A novel approach for classifying points lying on a connected Riemannian manifold using the geometry of the space of d-dimensional nonsingular covariance matrices as object descriptors.
Posted Content
Image Segmentation Using Deep Learning: A Survey
Shervin Minaee,Yuri Boykov,Fatih Porikli,Antonio Plaza,Nasser Kehtarnavaz,Demetri Terzopoulos +5 more
TL;DR: A comprehensive review of recent pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings are provided.
Journal ArticleDOI
Image Segmentation Using Deep Learning: A Survey.
Shervin Minaee,Yuri Boykov,Fatih Porikli,Antonio Plaza,Nasser Kehtarnavaz,Demetri Terzopoulos +5 more
TL;DR: A comprehensive review of deep learning-based image segmentation can be found in this article, where the authors investigate the relationships, strengths, and challenges of these DL-based models, examine the widely used datasets, compare performances, and discuss promising research directions.