Zhengzhi Lu

The nonsubsampled Laplacian pyramid (NSLP) is widely used as a common multiresolution decomposition method in various nonsubsampled image transforms. However, the NSLP has a fixed spectrum partition and thus cannot represent images accurately and flexibly. We propose a new adaptive arbitrary multiresolution decomposition to solve these problems. Fi...

In this paper we propose a novel model of sparse representation for image denoising that we call an adaptive contourlet hidden Markov model (HMM)–pulse-coupled neural network (PCNN). In this study, we first adopted a contourlet transform to decompose a noisy image to be some subband coefficients of various directions at various scales. The contourl...

Image representation is an essential problem in image processing. The most effective image representation method is multiscale geometric analysis (MGA). However, the current representative MGA method has some shortcomings, such as the fixed division of the scale spectrum and the direction spectrum, which cannot achieve the sparsest representation o...

Multiscale geometric analysis (MGA) is not only characterized by multi-resolution, time-frequency localization, multidirectionality and anisotropy, but also outdoes the limitations of wavelet transform in representing high-dimensional singular data such as edges and contours. Therefore, researchers have been exploring new MGA-based image compressio...

This paper proposes a novel visual saliency model based on the CovSal algorithm of the region covariance matrices and histogram contrast (HC) method. First, we give a new CovSal algorithm of the local saliency contrast by improving the center-surrounder segmentation method. Second, we add the HC algorithm of the global saliency contrast, and then w...

Recent research has revealed that the deep neural network (DNN)-based synthetic-aperture radar (SAR) automatic target recognition (ATR) techniques are vulnerable to adversarial examples, which poses significant security risks for their deployment in real-world systems. At the same time, the adversarial examples often exhibit transferability across...

Recently, methods for skeleton-based human activity recognition have been shown to be vulnerable to adversarial attacks. However, these attack methods require either the full knowledge of the victim (i.e. white-box attacks), access to training data (i.e. transfer-based attacks) or frequent model queries (i.e. black-box attacks). All their requireme...

Most deep learning-based action recognition models focus only on short-term motions, so the model often causes misjudgments of actions that are combined by multiple processes, such as long jump, high jump, etc. The proposal of Temporal Segment Networks (TSN) enables the network to capture long-term information in the video, but ignores that some un...

Abstract In visual object tracking methods, improving both the run time and the accuracy in the face of complex situations has always been an important issue. Many complex tracking algorithms, such as part‐based algorithms, have better accuracy when facing occlusions, but they have much greater computational complexity. In response to the above pro...

This paper proposes a sparse representation layer in the feature extraction stage of a convolutional neural network (CNN). Our goal is to add sparse transforms to a target network to improve its performance without introducing an extra calculation burden. First, the proposed method was achieved by inserting the sparse representation layers into a t...

It is very good to apply the saliency model in the visual selective attention mechanism to the preprocessing process of image recognition. However, the mechanism of visual perception is still unclear, so this visual saliency model is not ideal. To this end, this paper proposes a novel image recognition approach using multiscale saliency model and G...

Fast and robust vision-based road detection in an unstructured environment is very challenging. In this paper, we focus on vanishing-point (VP) detection in unstructured roads and propose a response-modulated line-voting method based on a contourlet transform, followed by a voter selection process for VP detection. We first adopt the contourlet tra...

Image fusion is an important task in both image processing and computer vision research that use multisensor processing and multiscale analysis. This paper proposed a novel image fusion algorithm using a nonsubsampled contourlet transform (NSCT) and a pulse-coupled neural network (PCNN) with digital filtering. First, we decomposed two original imag...