Adversarial training has proved to be competitive against supervised learning methods on computer vision tasks. However, studies have mainly been confined to generative tasks such as image synthesis. In this paper, we apply adversarial training techniques to the discriminative task of learning a steganographic algorithm. Steganography is a collection of techniques for concealing the existence o...

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itse...

In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning model is proposed to handle uncontrolled imbalanced real-world image-sentence dataset. We collect FlickrNYC dataset from Flickr as our testbed with 306, 165 images and the original text descriptions uploaded by the users are utilized as the ground truth for training. Descriptions in FlickrNYC dataset vary dramatically rang...

We need to capture properties of face image variations from training persons and generalize them to a new test person for robust image retrieval, which is essential for the case that there is only a single image of the test person to retrieval face images of the equal person from database. Conventional methods of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be e...

In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work...

Image enhancement plays a vital role in various applications. There are many techniques to remove the noise from the image and produce the clear visual of the image. Moreover, there are several filters and image smoothing techniques available in the literature. All these available techniques have certain limitations. Recently, neural networks are found to be a very efficient tool for image enha...

We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method balances the generator and discriminator during training. Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality. We also derive a way of controlling the trad...

Recently, motivated by the search technology, a data-driven annotation approach turns up to be effective [8, 9]. Given a query image and a labeled keyword, X. J. Wang et al [8] apply the search result cluster (SRC) algorithm into a three-layer annotation model. In [9], an improvement on [8] is made by C. Wang et al, who propose a scalable search-based approach to annotate the web personal image...

We present a comparative study of the effects of applying pre-processing and post-processing to remote sensing data both in the spatial image domain and the feature domain. We use a neural network for classification since it is not biased by a priori assumptions about the distributions of the spectral values of the classes. Spatial smoothing was applied both as preand post-processing steps. Pre...

The goal of this study was to determine whether injury, level of surgeon training, and patient factors are associated with increased use of fluoroscopy during open reduction and internal fixation of ankle fractures. These relationships are not well defined. The study was a retrospective chart review of patients treated at an academic institution with primary open reduction and internal fixation...