Tackling the Optimization and Precision Weakness of Deep Cascaded Regression for Facial Key-Point Localization

2018 ◽  
pp. 85-108
Author(s):  
Yuhang Wu ◽  
Shishir K. Shah ◽  
Ioannis A. Kakadiaris
Keyword(s):  
Cascaded Regression

scholarly journals Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences

2020 ◽  
Vol 10 (14) ◽  
pp. 4947
Author(s):  
Jang Pyo Bae ◽  
Malinda Vania ◽  
Siyeop Yoon ◽  
Sojeong Cheon ◽  
Chang Hwan Yoon ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Left Atrium ◽  
Cardiac Ct ◽  
Computation Time ◽  
3D Models ◽  
Active Appearance Models ◽  
Point Correspondence ◽  
Construction Methods ◽  
Ct Segmentation ◽  
Cascaded Regression

The creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentation software with probabilistic point and appearance correspondence. Group-wise registration of point sets constructs the point correspondence from probabilistic matches, and the proposed method also calculates appearance correspondence from these probabilistic matches. Final point correspondence of group-wise registration constructed independently for three surfaces of the double-shaped model. Stochastic appearance selection of cascaded regression enables the effective construction in the aspect of memory usage and computation time. The two correspondence construction methods of active appearance models were compared in terms of the paired segmentation of the left atrium (LA) and left ventricle (LV). The proposed method segmented 35 cardiac CTs in six-fold cross-validation, and the symmetric surface distance (SSD), Hausdorff distance (HD), and Dice coefficient (DC), were used for evaluation. The proposed method produced 1.88 ± 0.37 mm of LV SSD, 2.25 ± 0.51 mm* of LA SSD, and 2.06 ± 0.34 mm* of the left heart (LH) SSD. Additionally, DC was 80.45% ± 4.27%***, where * p < 0.05, ** p < 0.01, and *** p < 0.001. All p values derive from paired t-tests comparing iterative closest registration with the proposed method. In conclusion, the authors developed a cascaded regression framework for 3D cardiac CT segmentation.

scholarly journals Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression

2018 ◽  
Vol 20 (8) ◽  
pp. 2073-2085 ◽  
Author(s):  
Guanjun Guo ◽  
Hanzi Wang ◽  
Chunhua Shen ◽  
Yan Yan ◽  
Hong-Yuan Mark Liao
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Cascaded Regression

Cascaded regression for CT slice localization

2011 ◽  
Author(s):  
Olga C. Avila-Montes ◽  
Uday Kurkure ◽  
Ryo Nakazato ◽  
Daniel S. Berman ◽  
Damini Dey ◽  
...  
Keyword(s):  
Cascaded Regression

Robust face alignment by cascaded regression and de-occlusion

2020 ◽  
Vol 123 ◽  
pp. 261-272 ◽  
Author(s):  
Jun Wan ◽  
Jing Li ◽  
Zhihui Lai ◽  
Bo Du ◽  
Lefei Zhang
Keyword(s):  
Face Alignment ◽  
Cascaded Regression

scholarly journals Simultaneous multi-descent regression and feature learning for facial landmarking in depth images

2019 ◽  
Vol 32 (24) ◽  
pp. 17909-17926 ◽  
Author(s):  
Janez Križaj ◽  
Peter Peer ◽  
Vitomir Štruc ◽  
Simon Dobrišek
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Limited Range ◽  
Face Alignment ◽  
Classification Problems ◽  
3D Face ◽  
Face Expression ◽  
Gating Mechanism ◽  
Wide Range ◽  
Cascaded Regression

AbstractFace alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations.

Sign in / Sign up

Export Citation Format

Share Document