کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
562486 1451955 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Supervised sparse manifold regression for head pose estimation in 3D space
ترجمه فارسی عنوان
رگرسیون منیفولد پراکنده برای برآورد پوزیشن سر در فضای سه بعدی تحت نظارت
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• A supervised sparse manifold regression method is proposed for manifold learning.
• A low-dimensional projection is embedded to represent intrinsic features.
• A projection is used to fulfill the sparse representation of high dimension features.
• The local geometry is preserved and the dominant features are revealed better.
• The proposed method is beneficial for head pose angle estimation.

In estimating the head pose angles in 3D space by manifold learning, the results currently are not very satisfactory. We need to preserve the local geometry structure effectively and need a learned projective function that can reveal the dominant features better. To address these problems, we propose a Supervised Sparse Manifold Regression (SSMR) method that incorporates both the supervised graph Laplacian regularization and the sparse regression into manifold learning. In SSMR, on the one hand, a low-dimensional projection is embedded to represent intrinsic features by using supervised information while the local structure can be preserved more effectively by using the Laplacian regularization term in the objective function. On the other hand, by casting the problem of learning projective function into a regression with L1 norm regularizer, a projection is mapped to carry out the sparse representation of high dimension features, rather than a compact linear combination, so as to describe the dominant features better. Experiments show that our proposed method SSMR is beneficial for head pose angle estimation in 3D space.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 112, July 2015, Pages 34–42
نویسندگان
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