707 research outputs found
Gender Recognition from Unconstrained and Articulated Human Body
Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition
How is Gaze Influenced by Image Transformations? Dataset and Model
Data size is the bottleneck for developing deep saliency models, because
collecting eye-movement data is very time consuming and expensive. Most of
current studies on human attention and saliency modeling have used high quality
stereotype stimuli. In real world, however, captured images undergo various
types of transformations. Can we use these transformations to augment existing
saliency datasets? Here, we first create a novel saliency dataset including
fixations of 10 observers over 1900 images degraded by 19 types of
transformations. Second, by analyzing eye movements, we find that observers
look at different locations over transformed versus original images. Third, we
utilize the new data over transformed images, called data augmentation
transformation (DAT), to train deep saliency models. We find that label
preserving DATs with negligible impact on human gaze boost saliency prediction,
whereas some other DATs that severely impact human gaze degrade the
performance. These label preserving valid augmentation transformations provide
a solution to enlarge existing saliency datasets. Finally, we introduce a novel
saliency model based on generative adversarial network (dubbed GazeGAN). A
modified UNet is proposed as the generator of the GazeGAN, which combines
classic skip connections with a novel center-surround connection (CSC), in
order to leverage multi level features. We also propose a histogram loss based
on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in
terms of luminance distribution. Extensive experiments and comparisons over 3
datasets indicate that GazeGAN achieves the best performance in terms of
popular saliency evaluation metrics, and is more robust to various
perturbations. Our code and data are available at:
https://github.com/CZHQuality/Sal-CFS-GAN
Finite Strain Topology Optimization with Nonlinear Stability Constraints
This paper proposes a computational framework for the design optimization of
stable structures under large deformations by incorporating nonlinear buckling
constraints. A novel strategy for suppressing spurious buckling modes related
to low-density elements is proposed. The strategy depends on constructing a
pseudo-mass matrix that assigns small pseudo masses for DOFs surrounded by only
low-density elements and degenerates to an identity matrix for the solid
region. A novel optimization procedure is developed that can handle both simple
and multiple eigenvalues wherein consistent sensitivities of simple eigenvalues
and directional derivatives of multiple eigenvalues are derived and utilized in
a gradient-based optimization algorithm - the method of moving asymptotes. An
adaptive linear energy interpolation method is also incorporated in nonlinear
analyses to handle the low-density elements distortion under large
deformations. The numerical results demonstrate that, for systems with either
low or high symmetries, the nonlinear stability constraints can ensure
structural stability at the target load under large deformations. Post-analysis
on the B-spline fitted designs shows that the safety margin, i.e., the gap
between the target load and the 1st critical load, of the optimized structures
can be well controlled by selecting different stability constraint values.
Interesting structural behaviors such as mode switching and multiple
bifurcations are also demonstrated.Comment: 77 pages, 44 Figure
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