Fatih Porikli
Australian National University
468 Papers
4.1K Citations
Fatih Porikli is an academic researcher from Australian National University. The author has contributed to research in topics: Computer science & Video tracking. The author has an hindex of 66, co-authored 412 publications. Previous affiliations of Fatih Porikli include Commonwealth Scientific and Industrial Research Organisation & Huawei.
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Papers
Semi-Supervised Video Object Segmentation with Super-Trajectories
TL;DR: This work introduces a semi-supervised video segmentation approach based on an efficient video representation, called as “super-trajectory”, that is capable of extracting the target objects from complex backgrounds, and even reidentifying them after prolonged occlusions, producing high-quality video object segments.
183
Scalable Active Learning for Multiclass Image Classification
TL;DR: A new interaction modality for training which requires only yes-no type binary feedback instead of a precise category label is proposed, which is especially powerful in the presence of hundreds of categories.
Inter-camera color calibration by correlation model function
Fatih Porikli
- 24 Nov 2003
TL;DR: A novel solution to the inter-camera color calibration problem, which is very important for multicamera systems is presented and it is shown that the distance metric can be reduced to other commonly used metrics with suitable simplification.
165
Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors
TL;DR: A hyper-laplacian reflectance priors inspired retinex variational model is proposed to enhance underwater images and an alternating minimization algorithm is developed that is efficient on element-wise operations and independent of additional prior knowledge of underwater conditions.
157
Correspondence Driven Saliency Transfer
TL;DR: It is shown that large annotated data sets have great potential to provide strong priors for saliency estimation rather than merely serving for benchmark evaluations, and a novel image saliency detection method called saliency transfer is presented.