Wen Zhuo

Cloud Classification of Ground-Based Images Using Texture–Structure Features

AbstractCloud classification of ground-based images is a challenging task. Recent research has focused on extracting discriminative image features, which are mainly divided into two categories: 1) choosing appropriate texture features and 2) constructing structure features. However, simply using texture or structure features separately may not produce a high performance for cloud classification. In this paper, an algorithm is proposed that can capture both texture and structure information from a color sky image. The algorithm comprises three main stages. First, a preprocessing color census transform (CCT) is applied. The CCT contains two steps: converting red, green, and blue (RGB) values to opponent color space and applying census transform to each component. The CCT can capture texture and local structure information. Second, a novel automatic block assignment method is proposed that can capture global rough structure information. A histogram and image statistics are computed in every block and are con...

Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics

Image thresholding is one of the most important approaches for image segmentation and it has been extensively used in many image processing or computer vision applications. In this paper, a new image thresholding method is presented using type-2 fuzzy sets based on GLSC histogram of human visual nonlinearity characteristics (HVNC).The traditional GLSC histogram takes the image spatial information into account in a different way from two-dimensional histogram. This work refines the GLSC histogram by embedding HVNC into GLSC histogram. To select threshold based on the redefined GLSC histogram, we employ the type-2 fuzzy set, whose membership function integrates the effect of pixel gray value and local spatial information to membership value. The type-2 fuzzy set is subsequently transformed into a type-1 fuzzy set for fuzziness measure computation via type reduction. Finally, the optimal threshold is obtained by minimizing the fuzziness of the type-1 fuzzy set after an exhaustive search. The experiment on different types of images demonstrates the effectiveness and the robustness of our proposed thresholding technique.

mCLOUD: A Multiview Visual Feature Extraction Mechanism for Ground-Based Cloud Image Categorization

AbstractIn this paper, a novel Multiview CLOUD (mCLOUD) visual feature extraction mechanism is proposed for the task of categorizing clouds based on ground-based images. To completely characterize the different types of clouds, mCLOUD first extracts the raw visual descriptors from the views of texture, structure, and color simultaneously, in a densely sampled way—specifically, the scale invariant feature transform (SIFT), the census transform histogram (CENTRIST), and the statistical color features are extracted, respectively. To obtain a more descriptive cloud representation, the feature encoding of the raw descriptors is realized by using the Fisher vector. This is followed by the feature aggregation procedure. A linear support vector machine (SVM) is employed as the classifier to yield the final cloud image categorization result. The experiments on a challenging cloud dataset termed the six-class Huazhong University of Science and Technology (HUST) cloud demonstrate that mCLOUD consistently outperforms...

Robust key point descriptor for multi-spectral image matching

Histogram of collinear gradient-enhanced coding (HCGEC), a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough struc- tures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of- the-art descriptors.

A New Dew and Frost Detection Sensor Based on Computer Vision

AbstractMany weather features such as precipitation and snow depth can be recorded using automatic surface observation systems. However, automatically observing dew and frost presents several problems. Many studies have used various wetness sensors and passive microwave devices to detect dew. Unfortunately, several of these sensors are complex, and only a few are capable of detecting frost. This paper proposes a novel method for indirectly detecting dew and frost based on computer vision. The setup is simple, inexpensive, and only requires images of several glass substrates near the underlying surface. Images taken during dew or frost formation exhibit distinct changes in hierarchical visual features. These changes are detected by tracking the variations of several low-level statistical features that are extracted from the images in time. Additionally, an effective texture analysis method is proposed to describe the morphology of frost. Field experiments were conducted at several weather stations in Beiji...

Building localization from forward-looking infrared images for UAV guidance

This paper proposes a new approach to localizing a designated building from forward-looking infrared images under complex scenes, which can be used in UAV guidance. The approach makes full use of the scene information and is able to localize small or occluded buildings. The experiment results prove the algorithm efficiency.

An automatic DSM and remote sensing images registration scheme using template matching technique

In this paper, a novel automatic DSM and remote sensing images registration scheme using template matching technique is developed. Due to the heterogeneity of DSM and remote sensing images, the emphases of our scheme are to describe the common feature between DSM and remote sensing images, and to generate a suitable template for template matching. Based on the sparse representation theory, we present a new feature descriptor, which can highlight the similarities of DSM and remote sensing images, and can be used to form a new kind of feature image. Meanwhile we present a criterion to choose the proper region from the feature image as the template which will ensure perfect template matching performance. The experiment results show that our scheme is efficient to fulfill the task of registration.

A preprocessing method based on non-subsampled contourlet transform

This paper focuses on preprocessing methods for the infrared images. Due to noises, low contrast and local difference of materials, Forward-looking Infrared targets have a great difference of local gray levels. Such different gray levels add the difficulty of targets detection and may lead a decrease in accuracy and rate of detection in Gray-based template matching. To overcome the difficulty, we present a novel preprocessing method based on non-subsampled contourlet transform (NSCT) which is good at multi-direction and multi-scale. This method firstly do Non-subsampled contourlet transform to infrared images. The level of decomposition is decided by the size of the template which is considered as the basic structure and the directions of decomposition are chosen by the imaging angles. Then we reconstruct the image according to the coefficients of the decomposition. Experiments based on template matching in forward-looking infrared images show that the proposed preprocessing method based on NSCT can significantly improve the detection rate (DR) and the stability. Experimental results also show that our proposed method has better performance than some other preprocessing methods.

Joint distribution of nonsubsampled contourlet domain and its application to texture retrieval

In this paper, the joint distribution of the Nonsubsampled Contourlet Transform coefficients is studied. It is found that the estimation of the joint distribution is implement impossible due to the complex of joint empirical distribution function and dependence of NSCT coefficient vector components. To distinguish different joint distributions of different images, the sample covariance matrix feature is proposed. The texture retrieval experiment is conducted in order to evaluate the performance of the sample covariance matrix feature. The result shows that the proposed feature is efficient in representing the texture and the difference of the joint distribution of the Nonsubsampled Contourlet Transform coefficients.