167 research outputs found
Simultaneous Inference of a Partially Linear Model in Time Series
We introduce a new methodology to conduct simultaneous inference of the
nonparametric component in partially linear time series regression models where
the nonparametric part is a multivariate unknown function. In particular, we
construct a simultaneous confidence region (SCR) for the multivariate function
by extending the high-dimensional Gaussian approximation to dependent processes
with continuous index sets. Our results allow for a more general dependence
structure compared to previous works and are widely applicable to a variety of
linear and nonlinear autoregressive processes. We demonstrate the validity of
our proposed methodology by examining the finite-sample performance in the
simulation study. Finally, an application in time series, the forward premium
regression, is presented, where we construct the SCR for the foreign exchange
risk premium from the exchange rate and macroeconomic data.Comment: 61 pages, 6 figure
A novel facial expression recognition based on the curevlet features
Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image analysis are described. Then the coefficients of curvelet in selected scales and angles are used as features for image analysis. Consequently the Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are used to reduce and optimize the curvelet features. Finally we use the nearest neighbor classifier to recognize the facial expressions based on these features. The experimental results on JAFFE and Cohn Kanade two benchmark databases show that the proposed approach outperforms the PCA and LDA techniques on the original image pixel values as well as its counterparts with the wavelet features
Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
As an emerging concept, steganography without embedding (SWE) hides a secret
message without directly embedding it into a cover. Thus, SWE has the unique
advantage of being immune to typical steganalysis methods and can better
protect the secret message from being exposed. However, existing SWE methods
are generally criticized for their poor payload capacity and low fidelity of
recovered secret messages. In this paper, we propose a novel
steganography-without-embedding technique, named DF-SWE, which addresses the
aforementioned drawbacks and produces diverse and natural stego images.
Specifically, DF-SWE employs a reversible circulation of double flow to build a
reversible bijective transformation between the secret image and the generated
stego image. Hence, it provides a way to directly generate stego images from
secret images without a cover image. Besides leveraging the invertible
property, DF-SWE can invert a secret image from a generated stego image in a
nearly lossless manner and increases the fidelity of extracted secret images.
To the best of our knowledge, DF-SWE is the first SWE method that can hide
large images and multiple images into one image with the same size,
significantly enhancing the payload capacity. According to the experimental
results, the payload capacity of DF-SWE achieves 24-72 BPP is 8000-16000 times
compared to its competitors while producing diverse images to minimize the
exposure risk. Importantly, DF-SWE can be applied in the steganography of
secret images in various domains without requiring training data from the
corresponding domains. This domain-agnostic property suggests that DF-SWE can
1) be applied to hiding private data and 2) be deployed in resource-limited
systems
Boundary refinement network for colorectal polyp segmentation in colonoscopy images
Precise polyp segmentation is vitally essential for detection and diagnosis of early colorectal cancer. Recent advances in artificial intelligence have brought infinite possibilities for this task. However, polyps usually vary greatly in shape and size and contain ambiguous boundary, bringing tough challenges to precise segmentation. In this letter, we introduce a novel Boundary Refinement Network (BRNet) for polyp segmentation. To be specific, we first introduce a boundary generation module (BGM) to generate boundary map by fusing both low-level spatial details and high-level concepts. Then, we utilize the boundary-guided refinement module to refine the polyp-aware features at each layer with the help of boundary cues from the BGM and the prediction from the adjacent high layer. Through top-down deep supervision, our BRNet can localize the polyp regions accurately with clear boundary. Extensive experiments are carried out on five datasets, and the results indicate the effectiveness of our BRNet over seven recently reported methods
- …