Yining Quan

Single image haze removal based on haze physical characteristics and adaptive sky region detection

Outdoor images are often degraded by haze and other inclement weather conditions, which affect both consumer photographs and computer vision applications severely. Therefore, researchers have proposed plenty of restoration approaches to deal with this problem. However, it is hard to tackle the color distortion problem in restored images with ignoring the differences between fog and haze. Meanwhile, the atmospheric light is also an important variable that influences the global illumination of images. In this paper, we analyze the physical meaning of atmospheric light first, and estimate atmospheric light by a novel method of obtaining the sky region in images, which is based on our newly proposed sky region prior. Then after exploring physical characteristics of fog and haze, we explain why images taken in haze appear yellowish, and eliminate this phenomenon by our adaptive channel equalization method. Quantitative comparisons with seven state-of-art algorithms on a variety of real-world haze images demonstrate that our algorithm can remove haze effectively and keep color fidelity better. HighlightsWe propose a novel single image haze removal approach.Our approach is based on haze physical character and adaptive sky region detection.We analyze the haze physical character and meaning of atmospheric scattering model.We propose a sky region prior based on thousands of outdoor images.We propose a new adaptive sky region detection method to estimate atmospheric light.We eliminate distortion in hazy images by our adaptive channel equalization method.We convert to HSI color space to compensate over-saturation phenomenon in RGB space.

Modular ensembles for one-class classification based on density analysis

One-Class Classification (OCC) is an important machine learning task. It studies a special classification problem that training samples from only one class, named target class, are available or reliable. Recently, various OCC algorithms have been proposed, however many of them do not adequately deal with multi-modality, multi-density, the noise and arbitrarily shaped distributions of the target class. In this paper, we propose a novel Density Based Modular Ensemble One-class Classifier (DBM-EOC) algorithm which is motivated by density analysis, divide-and-conquer method and ensemble learning. DBM-EOC first performs density analysis on training samples to obtain a minimal spanning tree using density characteristics of the target class. On this basis, DBM-EOC automatically identifies clusters, multi-density distributions and the noise in training samples using extreme value analysis. Then target samples are categorized into several groups called Local Dense Subset (LDS). Samples in each LDS are close to each other and their local densities are similar. A simple base OCC model e.g. the Gaussian estimator is built for each LDS afterwards. Finally all the base classifiers are modularly aggregated to construct the DBM-EOC model. We experimentally evaluate DBM-EOC with 6 state-of-art OCC algorithms on 5 synthetic datasets, 18 UCI benchmark datasets and the MNIST dataset. The results show that DBM-EOC outperforms other competitors in majority cases especially when the datasets are multi-modality, multi-density or noisy. We propose a modular ensemble OCC algorithm DBM-EOC based on density analysis.We analyze peculiarities of the target class which are crucial for OCC.DBM-EOC obtains a tree structure of the target class considering density.DBM-EOC can automatically detect clusters and remove noise samples.DBM-EOC solves OCC problems with the divide-and-conquer method.

Fast structural ensemble for One-Class Classification

We propose a fast structural ensemble OCC framework FS-EOCC.FS-EOCC does not need the determining of the number of clusters.FS-EOCC could convert a common OCC algorithm to a structural one.FS-EOCC run faster than most existing clustering based ensemble OCC methods. One of the most important issues of One-Class Classification (OCC) algorithm is how to capture the characteristics of the positive class. Existing structural or clustering based ensemble OCC algorithms build description models for every cluster of the training dataset. However, the introduction of clustering algorithm also causes some problems, such as the determination of the number of clusters and the additional computational complexity. In this paper, we propose Fast Structural Ensemble One-Class Classifier (FS-EOCC) which is a fast framework for converting a common OCC algorithm to structural ensemble OCC algorithm. FS-EOCC adopts two rounds of complementary clustering with fixed number of clusters. This number is calculated according to the number of training samples and the complexity of the base OCC algorithm. Each partition found in the previous step is used to train one base OCC model. Finally all base models are modularly aggregated to build the structural OCC model. Experimental results show that FS-EOCC outperforms existing structural or clustering based OCC algorithms and state-of-the-art non-structural OCC algorithms. The comparison of running time for these algorithms indicates that FS-EOCC is an efficient framework because the cost of converting a common OCC algorithm to a structural OCC algorithm is small and acceptable.

Color topographical map segmentation Algorithm based on linear element features

In order to overcome the discontinuity of geographic elements during the digitization of scanned topographic maps, a color map segmentation algorithm, which is used to segment color maps into different layers based on linear element features, is proposed in this paper. Linear elements are regarded as the elementary units in this method. We use background removal, thinning, nodes disconnection, labeling and dilation to get the elementary units. Then the main color, which could accurately represent the color feature of linear element, is extracted for clustering on the basis of Fuzzy c-means algorithm. At last, disconnected nodes are merged into the corresponding layers to keep the continuity of the results. The experimental results show that the proposed algorithm outperforms other segmentation approaches that regarding pixels as the elementary units.

Remote Sensing Image Fusion Based on Shearlet and Genetic Algorithm

Image fusion is a technology which can effectively enhance the utilization ratio of image information, the accuracy of target recognition and the interpretation ability of image. However, traditional fusion methods may lead to the information loss and image distortion. Hence a novel remote sensing image fusion method is proposed in this paper. As one of the multi-scale geometric analysis tools, Shearlet has been widely used in image processing. In this paper, Shearlet is used to decompose the image. Genetic Algorithm, a intelligent optimization algorithm, is also applied to image fusion and it aims to optimize the weighted factors in order to improve the quality of fusion. Experimental results prove the superiority and feasibility of this method.

An Ensemble Cost-Sensitive One-Class Learning Framework for Malware Detection

Machine learning is among the most popular methods in designing unknown and variant malware detection algorithms. However, most of the existing methods take a single type of features to build binary classifiers. In practice, these methods have limited ability in depicting malware characteristics and the binary classification suffers from inadequate sampling of benign samples and extremely imbalanced training samples when detecting malware. In this paper, we present a malware detection Framework based on ENsemble One-Class Learning, namely FENOC. It uses hybrid features at different semantic layers to ensure a comprehensive insight of the program to be analyzed. We construct the malware detector by a novel learning algorithm called Cost-sensitive Twin One-class Classifier (CosTOC), which uses a pair of one-class classifiers to describe malware and benign programs respectively. CosTOC is more flexible and robust in comparison to conventional binary classifiers when training samples are extremely imbalanced or the benign programs are inadequately sampled. Finally, random subspace method and clustering-based ensemble method are developed to enhance the generalization ability of CosTOC. Experimental results show that FENOC gives a comparative detection rate and a lower false positive rate than many other binary classification algorithms, especially when the detector are trained with imbalanced data, or evaluated in terms of false positive rate.

BoostFS: A Boosting-Based Irrelevant Feature Selection Algorithm

In a learning process, features play a fundamental role. In this paper, we propose a Boosting-based feature selection algorithm called BoostFS. It extends AdaBoost which is designed for classification problems to feature selection. BoostFS maintains a distribution over training samples which is initialized from the uniform distribution. In each iteration, a decision stump is trained under the sample distribution and then the sample distribution is adjusted so that it is orthogonal to the classification results of all the generated stumps. Because a decision stump can also be regarded as one selected feature, BoostFS is capable to select a subset of features that are irrelevant to each other as much as possible. Experimental results on synthetic datasets, five UCI datasets and a real malware detection dataset all show that the features selected by BoostFS help to improve learning algorithms in classification problems, especially when the original feature set contains redundant features.

Multiscale road centerlines extraction from high-resolution aerial imagery

Abstract Accurate road extraction from high-resolution aerial imagery has many applications such as urban planning and vehicle navigation system. The common road extraction methods are based on classification algorithm, which needs to design robust handcrafted features for road. However, designing such features is difficult. For the road centerlines extraction problem, the existing algorithms have some limitations, such as spurs, time consuming. To address the above issues to some extent, we introduce the feature learning based on deep learning to extract robust features automatically, and present a method to extract road centerlines based on multiscale Gabor filters and multiple directional non-maximum suppression. The proposed algorithm consists of the following four steps. Firstly, the aerial imagery is classified by a pixel-wise classifier based on convolutional neural network (CNN). Specifically, CNN is used to learn features from raw data automatically, especially the structural features. Then, edge-preserving filtering is conducted on the resulting classification map, with the original imagery serving as the guidance image. It is exploited to preserve the edges and the details of the road. After that, we do some post-processing based on shape features to extract more reliable roads. Finally, multiscale Gabor filters and multiple directional non-maximum suppression are integrated to get a complete and accurate road network. Experimental results show that the proposed method can achieve comparable or higher quantitative results, as well as more satisfactory visual performance.

A multi-scale fusion scheme based on haze-relevant features for single image dehazing

Abstract Outdoor images are often degraded by aerosols suspending in atmosphere in bad weather conditions like haze. To cope with this phenomenon, researchers have proposed many approaches and single image based techniques draw attention mostly. Recently, a fusion-based strategy achieves good results, which derives two enhanced images from single image and blends them to recover haze-free image. However, there are still some deficiencies in the fusion-input images and weight maps, which leads their restoration less natural. In this paper, we propose a multi-scale fusion scheme for single image dehazing. We first use an adaptive color normalization to eliminate a common phenomenon, color distortion, in haze condition. Then two enhanced images, including our newly presented local detail enhanced image, are derived to be blended. Thereafter, five haze-relevant features of dark channel, clarity, saliency, luminance and chromatic are investigated since those can serve as weight maps for fusion. Dark channel, clarity and saliency features are finally selected due to their expression abilities and less interconnection. The fusion is processed with a pyramid strategy layer-by-layer. The multi-scale blended images are combined in a bottom-up manner. At last quantitative experiments demonstrate that our approach is effectiveness and yields better results than other methods.

Filtering LiDAR data based on adjacent triangle of triangulated irregular network

The filtering of LiDAR points cloud data is a fundamental procedure in the production of Digital Elevation Model. Against the lack of using the relationship between the adjacent terrain and the points to be judged in the point cloud filtering, a LiDAR points cloud data filtering algorithm based on adjacent triangles in TIN (Triangulated Irregular Network) is proposed. It utilizes the elevation information of each triangle’s adjacent triangles to detect the building edge points, and acquires the building points by region growing, then detects the isolated points with the morphological filtering algorithm, finally determines the ground point set and generates DEM. We evaluate the performance of the proposed method on the ISPRS LiDAR reference dataset. Experimental results show that the algorithm can effectively remove non-ground points, keep the ground points and minimize total error rates effectively while maintaining acceptable Type I and Type II error rates.