Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

Automatic texture exemplar extraction based on global and local textureness measures

Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques eInphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, of taining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant post- processing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly erop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured t）atches from non-textured patches, in conjunction with SVM prediction. To identify real texture, exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure （using t）inary Gabor pattern （BGP）） and dominant color features （using color histograms） between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures mauually selected by an artist and a statistical study demonstrate its effectiveness.

Local information enhanced LBP

Based on the observation that there exists multiple information in a pixel neighbor,such as distance sum and gray difference sum,local information enhanced LBP(local binary pattern)approach,i.e.LE-LBP,is presented.Geometric information of the pixel neighborhood is used to compute minimum distance sum.Gray variation information is used to compute gray difference sum.Then,both the minimum distance sum and the gray difference sum are used to build a feature space.Feature spectrum of the image is computed on the feature space.Histogram computed from the feature spectrum is used to characterize the image.Compared with LBP,rotation invariant LBP,uniform LBP and LBP with local contrast,it is found that the feature spectrum image from LE-LBP contains more details,however,the feature vector is more discriminative.The retrieval precision of the system using LE-LBP is91.8%when recall is 10%for bus images.

Adsorption and Desorption of Cry1Ab Proteins on Differently Textured Paddy Soils

In recent years, selected cry genes from Bacillus thuringiensis（Bt） encoding the production of Cry proteins（Bt toxins） have been engineered into crop plants（Bt-crops）. Through the cultivation of Bt crops and the application of Bt pesticides, Cry proteins could be introduced into arable soils. The interaction between the proteins and soils was analyzed in this study to investigate the affinity of Cry proteins in paddy soil ecosystems. Four Paddy soils were selected to represent different soil textures. Cry proteins were spiked in soils, and the amount of protein adsorbed was measured over 24 h. Desorption of Cry1Ab proteins from paddy soils was performed by washing with sterile Milli-Q water（H_2O_（MQ））, and subsequently extracted with an extraction buffer. The paddy soils had a strong affinity for Cry1Ab proteins. Most of the Cry1Ab proteins added（＆gt; 98%） were rapidly adsorbed on the paddy soils tested. More Cry1Ab proteins were adsorbed on non-sterile soils than on sterile soils. Less than 2% of the adsorbed Cry1Ab proteins were desorbed using H2 OMQ, while a considerable proportion of the adsorbed proteins could be desorbed with the buffer, ranging from 20% to 40%.The amount of proteins desorbed increased with the increases in the initial amount of Cry1Ab proteins added to the paddy soils. The concentration of Cry1Ab proteins desorbed from the paddy soils was higher for sterile soils than non-sterile ones. Our results indicate that Bt toxins released via the cultivation of Bt crops, the application of Bt pesticides can be adsorbed on paddy soils, and soil texture could impose an impact on the adsorption capability.

Detecting maize leaf water status by using digital RGB images

To explore the correlation between crop leaf digital RGB(Red,Green and Blue)image features and the corresponding moisture content of the leaf,a Canon digital camera was used to collect image information from detached leaves of heading-stage maize.A drying method was adopted to measure the moisture content of the leaf samples,and image processing technologies,including gray level co-occurrence matrices and grayscale histograms,was used to extract the maize leaf texture feature parameters and color feature parameters.The correlations of these feature parameters with moisture content were analyzed.It is found that the texture parameters of maize leaf RGB images,including contrast,correlation,entropy and energy,were not significantly correlated with moisture content.Thus,it was difficult to use these features to predict moisture content.Of the six groups of eigenvalues for the leaf color feature parameters,including mean,variance,energy,entropy,kurtosis and skewness,mean and kurtosis were found to be correlated with moisture content.Thus,these features could be used to predict the leaf moisture content.The correlation coefficient(R2)of the mean-moisture content relationship model was 0.7017,and the error of the moisture content prediction was within±2%.The R2 of the kurtosis-moisture content relationship model was 0.7175,and the error of the moisture content prediction was within±1.5%.The study results proved that RGB images of crop leaves could be used to measure moisture content.

A surface plasmon resonance imaging system for the stimulated living cell analysis

In this paper, a surface plasmon resonance imaging(SPRI) system for cell analysis is developed for obtaining the surface plasmon resonance(SPR) signal from the interactions between cells and different stimuli. The system is constructed with a red laser light source, a P-polarizer, a glass prism, a 5× objective lens, a charge coupled device(CCD) camera, a gold sensor chip, a polydimethylsiloxane(PDMS) reaction well and a mechanical scanning device. The system is applied to mapping living cells in response to stimuli by characterization of the refractive index(RI) changes. Cell responses to K+ in KCl solutions with concentrations of 5 mmol/L, 20 mmol/L, 50 mmol/L and 100 mmol/L are collected, which indicates that the SPRI method can distinguish the concentration of the stimuli. Furthermore, cell responses to epidermal growth factor(EGF) and vascular endothelial growth factor(VEGF) are studied independently. The binding of EGF receptor(EGFR) and EGF is collected as the first signal, and the internal change in cells is recorded as the second signal. The cell response to VEGF is different from that to EGF, which indicates that the SPRI as a label-free, real-time, fast and quantitative method has a potential to distinguish the cell responses to different stimuli.

Pulmonary Crackle Detection Based on Fractional Hilbert Transform

Crackles are an important kind of abnormal and discontinuous lung sounds,which have been found to be correlated to types of pulmonary diseases.The purpose of this work is to show a new perspective to solve the problem of crackle detection,based on an emerging theory of fractional Hilbert transform.By applying fractional Hilbert transform to lung sound signals,a two-dimension texture image can be generated.The texture features corresponding to crackles are quite easy to be extracted.Experiments illustrate the effectiveness of our method.