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The Secrets of Salient Object Segmentation
- Yin Li, Xiaodi Hou, Christof Koch, James M. Rehg, A. Yuille
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 11 June 2014
An extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets identifies serious design flaws of existing salient object benchmarks and proposes a new high quality dataset that offers both fixation and salient objects segmentation ground-truth.
Statistical Color Models with Application to Skin Detection
- Michael J. Jones, James M. Rehg
- Computer ScienceProceedings. IEEE Computer Society Conference on…
- 23 June 1999
This work describes the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labelled pixels and suggests that color can be a more powerful cue for detecting people in unconstrained imagery than was previously suspected.
Video Segmentation by Tracking Many Figure-Ground Segments
- Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, James M. Rehg
- Computer ScienceIEEE International Conference on Computer Vision
- 1 December 2013
An unsupervised video segmentation approach by simultaneously tracking multiple holistic figure-ground segments that outperforms state-of-the-art approaches in the dataset, showing its efficiency and robustness to challenges in different video sequences.
CENTRIST: A Visual Descriptor for Scene Categorization
- Jianxin Wu, James M. Rehg
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 August 2011
CENsus TRansform hISTogram (CENTRIST), a new visual descriptor for recognizing topological places or scene categories, is introduced and is shown to be a holistic representation and has strong generalizability for category recognition.
Learning to recognize objects in egocentric activities
The key to this approach is a robust, unsupervised bottom up segmentation method, which exploits the structure of the egocentric domain to partition each frame into hand, object, and background categories and uses Multiple Instance Learning to match object instances across sequences.
Multiple Hypothesis Tracking Revisited
- Chanho Kim, Fuxin Li, A. Ciptadi, James M. Rehg
- Computer ScienceIEEE International Conference on Computer Vision…
- 7 December 2015
It is demonstrated that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets, and it is shown that appearance models can be learned efficiently via a regularized least squares framework.
Fine-Grained Head Pose Estimation Without Keypoints
- Nataniel Ruiz, Eunji Chong, James M. Rehg
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 2 October 2017
An elegant and robust way to determine pose is presented by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles directly from image intensities through joint binned pose classification and regression.
Motion Coherent Tracking with Multi-label MRF optimization
This work proposes a novel energy formulation which incorporates both segmentation and motion estimation in a single framework, and utilizes state-of-the-art methods to efficiently optimize over a large number of discrete labels.
Learning to Recognize Daily Actions Using Gaze
An inference method is presented that can predict the best sequence of gaze locations and the associated action label from an input sequence of images and demonstrates improvements in action recognition rates and gaze prediction accuracy relative to state-of-the-art methods.
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video
A novel deep model is proposed for joint gaze estimation and action recognition in First Person Vision that describes the participant’s gaze as a probabilistic variable and models its distribution using stochastic units in a deep network to generate an attention map.