基于图像区分模型的标牌可读性度量方法

为了能够对界面设计的优劣给出一个客观的度量标准,研究了背景图片上文字的可读性,提出了一种基于图像区分模型文本可读性度量方法。对基于JND的图像区分模型进行了认知与分析,在此基础上建立了文本可读性预测分析模型。分析了全局遮掩指数在预测文本可读性方面存在的问题与不足,综合考虑了文本及背景的对比度对背景RMS对比度的影响,提出了改进后的文本可读性预测算法。实验过程中,应用改进后的算法对高级气象显示情况下飞机标牌的可读性进行了计算,实验结果表明,用改进后的度量算法预测出来的结果更接近与真实值。从而表明了该方法的实用性。

利用改进的LZC算法对图像与文字进行分类

在传统二值化方法的基础上,将信号序列分为多个区间,进行多尺度二值化粗粒化处理,不增加符号数目,得到更精细,更准确的复杂性.经过实验,分析可知文字与图像随着计算尺度的减小,文字复杂性增大的幅度远小于图像.通过该结论,不同尺度下复杂性的差异可以作为图像与文字分类的特征.因此,我们可以将改进后的LZC算法应用于图像与文字的区分.

应用多层螺旋CT三维影像的拓扑学组织特征自动区分正常和异常的肺组织

应用多层螺旋CT（MDCT）基于cT值的拓扑学提出一个新的、强有力的算法，用于肺组织的分类，并与密度测量法得到的结果比较。21例有或无肺部病变者（肺气肿，纤维化）行MDCT胸部检查（各项同性的体素大小，边长0．6mm），获得了275个立方感兴趣区（VOI，边长40个像素）。2名放射医师对所有的VOI进行观察，并作一致分类，分别以Minkowski函数值（MF）及CT密度值判定组织特征。

一种基于主成分的多表图像哈希检索方法

大数据时代的到来,快速而准确的索引算法对信息检索至关重要。针对基于随机投影构成的单表哈希检索方法导致搜索性能低的问题,提出一种基于主成分的多表图像哈希检索方法。为了得到高效的哈希编码保证不同语义样本特征的区分性,首先通过主元分析方法保留训练集具有区分性图像特征,此外利用特征聚类作为学习哈希投影的指引构建多个索引表;其次采用正交旋转矩阵对哈希投影进行优化,保证了相同语义的样本具有相似的哈希码。最后分别在CIFAR-10和Caltech-256数据集上与相关方法进行比较,实验结果表明提出的方法提高了检索性能。

HESSIAN MATRIX BASED SADDLE POINT DETECTION FOR GRANULES SEGMENTATION IN 2D IMAGE

Segmenting the touching objects in an image has been remaining as a hot subject due to the problematic complexities, and a vast number of algorithms designed to tackle this issue have come into being since a decade ago. In this paper, a new granule segmentation algorithm is developed using saddle point as the cutting point. The image is binarized and then sequentially eroded to form a gray-scale topographic counterpart, followed by using Hessian matrix computation to search for the saddle point. The segmentation is performed by cutting through the saddle point and along the maximal gradient path on the topographic surface. The results of the algorithm test on the given real images indicate certain superiorities in both the segmenting robustness and execution time to the referenced methods.

Image Segmentation Using an Improved Watershed Algorithm

As watershed algorithm suffers from over-segmentation problem, this paper presented an efficient method to resolve this problem. First, pre-process of the image using median filter is made to reduce the effect of noise. Second, watershed algorithm is employed to provide initial regions. Third, regions are merged according to the information between the region and boundary. In the merger processing based on the region information, an adaptive threshold of the difference between the neighboring regions is used as the region merge criteria, which is based on the human visual character. In the merger processing on the boundary information, the gradient is used to judge the true boundary of the image to avoid merging the foreground with the background regions. Finally, post-process to the regions using mathematical morphology open and close filter is done to smooth object boundaries. The experimental results show that this method is very efficient.

EFFICIENT IMAGE SEGMENTATION FOR SEMANTIC OBJECT GENERATION

This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.

Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm

Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the pattern classification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the oversegmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle(UAV). We put forward an approach to evaluate the segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.

Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

Case study on the extraction of land cover information from the SAR image of a coal mining area

In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.

Integrating Remote Sensing and Field Survey to Map Shallow Water Benthic Habitat for the Kingdom of Bahrain

Identification and classification, as well as mapping of marine habitats, are of primary importance to plan management activities, especially in disturbed ecosystems like the ones in the marine areas of Bahrain. Remotely sensed Landsat-8 imagery coupled with field survey was used to identify, classify and map the benthic habitats in Bahrain marine area. The used geospatial techniques include advanced image processing procedures, which comprise of radiometric and atmospheric corrections, sun glint removal, water depth correction and image classification. Extensive ground-truthing analyses through in-situ field surveys by a team of scuba divers were conducted in October 2014 and June 2015 to inform and refine the classifications. The variables collected from this survey included physical and chemical characteristics of the water, habitat type, substrata, fauna and flora. A total of 176 field points were collected and utilized to perform an accurate assessment of the image classification. Initial habitat classification resulted in 20 habitat categories. However, due to the inability of the Landsat-8 sensors to accurately discriminate that level of classification, categories were merged into seven classes. The derived map shows that the benthic marine habitats of Bahrain consist of deep water （2,523 km2）, rock （1,738 km2）, sand （1,191 km2）, deep water/sand （1,006 km2）, algae （922 km2）, seagrass （591 km2） and corals （275.50 km2）. Although limited by the spatial and spectral resolutions of Landsat 8, the used methods produced a suitable map of the benthic habitats within the marine area of Bahrain with an overall accuracy of 84.1%. The use of very high spatial resolution satellite imagery will most likely increase such accuracy significantly.

A Modified Fixed Point Method for the Perona Malik Equation

In this work we present a new method to solve the Perona Malik equation for the image denoising. The method is based on a modified fixed point algorithm which is fast and stable. We discretize the equation using a finite volume method by integrating the equation using a fuzzy measure on the control volume. To make our algorithm move faster in time, we have used an optimized domain decomposition which generalize the wave relaxation method. Several test of noised images illustrate this approach and show the efficiency of the proposed new method.

Colored 3D surface reconstruction using Kinect sensor

A colored 3D surface reconstruction method which effectively fuses the information of both depth and color image using Microsoft Kinect is proposed and demonstrated by experiment.Kinect depth images are processed with the improved joint-bilateral filter based on region segmentation which efficiently combines the depth and color data to improve its quality.The registered depth data are integrated to achieve a surface reconstruction through the colored truncated signed distance fields presented in this paper.Finally,the improved ray casting for rendering full colored surface is implemented to estimate color texture of the reconstruction object.Capturing the depth and color images of a toy car,the improved joint-bilateral filter based on region segmentation is used to improve the quality of depth images and the peak signal-to-noise ratio(PSNR)is approximately 4.57 d B,which is better than 1.16 d B of the joint-bilateral filter.The colored construction results of toy car demonstrate the suitability and ability of the proposed method.

Research on Auto-Classification Method of Remote Sensing Images in Mountainous Areas——An Application in Southwest of China

In mountainous areas, it is the undulant terrain, various types of geomorphic and land use that make the remote sensing images great metamorphism. Moreover, due to the elevation, there are many areas covered with shadow, clouds and snow that make the images more inaccurate. As a result, it would be very difficult to carry out auto-classification of RS images in these areas. The study took Southwest China as the case study area and the TM images, SPOT images as the basic information sources assisted by the auxiliary data of DEM, NDVl, topographical maps and soil maps to preprocess the images. After preprocessing by topographic correction and wiping off clouds, snow and shadows, all the image data were stacked together to form the images to be classified. Then, the research used segmentation technology and hierarchical method to extract the main types of land use in the area automatically. The results indicated that the qualitative accuracies of all types of land use extracted in Southwest China were above 90 percent, and the quantitative accuracies was above 86 percent. The goal of reducing workloads had been realized.

Occurrence, Structure and Mineral Phases of Nanoparticles in an Anthrosol

Soils contain various kinds of crystalline to amorphous solid particles with at least one dimension in the nanoscale （〈 100 nm）. These nanoparticles contribute greatly to dynamic soil processes such as soil genesis, trace element cycling, contaminant transport, and chemical reaction. The nano-sized fraction of an Anthrosol was obtained to determine the occurrence, chemical composition, structure, and mineral phases of nanoparticles using high-resolution transmission electron microscopy （HRTEM） equipped with an energy-dispersive X-ray spectroscopy. Selected area electron diffraction or the fast Fourier transform of high-resolution images was used in structural characterization of the nanoparticles with HRTEM. Two nanoscale mineral types, i.e., mineral nanoparticles and nanomi- nerals, were observed in the Anthrosol. Mineral nanoparticles in soil included well crystalline aluminumsilicate nanosheets, nanorods, and nanoparticles. Nanosheets with a length of 120-150 nm and a width of about 10-20 nm were identified as chlorite/vermiculite series. The presence of clear lattice fringe spacing in HRTEM image of nanoparticles indicated that mineral nanoparticles had a relatively good crystallinity. The nanomineral ferrihydrite also existed in the Anthrosol. The HRTEM images and the particle size distribution histogram suggested that these ferrihydrite nanoparticles were quite homogeneous, and had a narrow size distribution range （1-7 nm） with a mean diameter of 3.6 4- 1.6 nm. Our HRTEM observation indicated that mineral nanoparticles and nanominerals were common and widely distributed in Anthrosols. HRTEM and selected area diffraction or lattice fringe spacing characterization provided further proofs to the structure of nanoparticles formed in soil.