北缘蔗区甘蔗专用留种田栽培技术研究(续完)──Ⅲ.蔗种茎粗对大田生产的影响的研究

本研究以湘北蔗区常规甘蔗品种NCo310为材料,在1998年进行甘蔗蔗种茎粗对大田生产的影响试验,观察了甘蔗的出苗率、有效茎数、株高、茎粗、单茎重、产量等农艺性状,结果表明蔗种茎粗对大田生产有显著影响,当蔗种茎粗小于1．7cm时,甘蔗大田生产产量显著减少,本研究表明甘蔗中小茎品种如NCo310等只有当播种行距在0．5。～0．6m时,才能获得较多茎粗大于1．7cm的甘蔗种芽．通过1996～1998年三年研究表明,在北缘蔗区建立甘蔗专用留种田栽培制度是切实可行的,但要适当的控制行距和密度．采用甘蔗专用留种田栽培技术,可以明显地提高北缘蔗区的甘蔗生产水平。     

基于分层神经网络的宽频段DOA估计方法

该文提出了一种采用智能识别技术解决宽段来波方位估计的新方法。提取已知来波方位信号的协方差矩阵的上三角部分作为样本特征,然后构建区域估计和方位估计的分层模型,实现对未知来波方位的精确估计。所提出的分层方法降低了模型构造的复杂度,实现了宽频段来波方位估计。给出了应用该方法的具体步骤。仿真实验结果表明,该方法具有很高的测向精度,具有广阔的应用前景。     

Automated coronal hole segmentation from Solar EUV Images using the watershed transform

Region of interest segmentation in solar images is the subject of frequent research in solar physics. This study outlines watershed by immersion segmentation to identify coronal hole areas in solar images acquired using the Extreme UV Imaging Telescope (EIT). Solutions presented here produce highly accurate segmentation results of coronal holes of irregular shape, and what is more, they do so for images representing varied solar activity, recorded in different years and months. In addition, the solutions presented here make all the methods used operate very quickly. These methods include: the preprocessing step before the watershed segmentation, the watershed segmentation itself, and also the postprocessing of solar images after the watershed segmentation. The mean duration of the entire segmentation process of solar images amounts to 342 ms for a single coronal hole, without the parallel implementation of the methods used. The experiments were carried out on a computer with an Intel Core i7 CPU @ 2 GHz and 4 GB RAM. After the seed point is identified inside the coronal hole, the segmentation runs automatically.     

基于JPEG2000的遥感图像感兴趣区域编码新算法及其VLSI设计

为解决空间遥感图像数据量及信道带宽之间的矛盾,该文提出一种基于JPEG2000的感兴趣区域(Region Of Interest, ROI)编码算法。主流的JPEG2000 ROI编码算法难以兼顾ROI质量和系统计算量,且在低码率编码时有完全丢失背景的隐患。该算法通过精确控制各子带中背景系数的精度,使ROI分配到更多码流。并引入了人眼视觉特性,使较少的背景码流产生尽量好的视觉效果。另外,根据该算法提出了针对矩形ROI的超大规模集成电路(VLSI)设计,此设计经过简单调整,亦可适用于主流的ROI编码算法。测试结果表明,该算法在ROI质量和重建图像视觉效果上均表现优异,且支持任意形状ROI编码,兼容JPEG2000协议。该VLSI设计仅使JPEG2000系统运行时间增加一个周期,具有极高的吞吐率,可满足实时处理要求。     

甲状腺结节超声图像分割算法研究

超声甲状腺结节分割是发现与识别甲状腺良恶性肿瘤的关键技术之一.针对模糊聚类法无法准确分割超声图像甲状腺结节边缘,而局部拟合(RSF)模型法对手动初始化轮廓敏感的问题,提出一种融合空间约束模糊C均值聚类和局部拟合RSF模型的分割结节方法.用空间约束模糊C均值聚类法(SKFCM)对图像进行聚类并二值化聚类结果作为RSF模型法初始轮廓,克服了RSF模型法对初始轮廓敏感问题,水平集演化参数也将通过聚类结果自动给出,不再需要人为设定.同时改进了RSF模型法拟合项,并利用高斯正则化规则RSF模型水平集,提高了RSF模型演化效率,缩短了收敛时间.仿真实验结果表明,提出的甲状腺结节超声图像分割方法能够快速准确地分割出结节区域.     

A multi-object image segmentation C–V model based on region division and gradient guide

The Chan–Vese (C–V) model is an ineffective method for processing images in which the intensity is inhomogeneous. This is especially true for multi-object segmentation, in which the target may be missed or excessively segmented. In addition, for images with rich texture information, the processing speed of the C–V is slow. To overcome these problems, this paper proposes an effective multi-object C–V segmentation model based on region division and gradient guide. First, a rapid initial contour search is conducted using Otsu’s method. This contour line becomes the initial contour for our multi-object segmentation C–V model based on a gradient guide. To achieve the multi-object segmentation the image is then converted to a single level set whose evolution is controlled using an adaptive gradient. The feasibility of the proposed model is analyzed theoretically, and a number of simulation experiments are conducted to validate its effectiveness.     

Structural image retrieval using automatic image annotation and region based inverted file

Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.     

三峡地区的水土流失与水土保持

三峡地区沟壑纵横、地形破碎，交通不便、生态环境恶化，经济和社会发展一直比较缓慢，三峡工程的兴建，给三峡地区的经济和社会发展带来了良好的机遇，了对这一地区的农业生产和生态保护提出出更高的要求。１９８８年，国务院批准将长江上游列为国家水土保持重点防治区，这是增强三峡地区的农业基础设施改善生态环境，实现该地区经济可持续发展的基础工程。     

Automatic segmentation of color images with transitive closure

In this paper, a mask based automatic segmentation algorithm for color images which uses pixel similarity has been presented. Main concept of the algorithm relies on spatial mask for course segmentation and the Warshall's transitive closure (TC) computation algorithm for region merging. Although the proposed spatial mask approach reduces the computational burden required for segmentation or clustering techniques such as seeded region growing (SRG) or fuzzy c-means (FCM) in which user supplied parameters are essential, it has over segmentation drawback. Therefore, the transitive closure algorithm, which uses adjacency and similarity matrix associated to undirected graph of the over segmented image, has been employed to merge the regions. After comparing to existing methods, the obtained experimental results confirmed that the color images as well as gray level images could be segmented with considerable accuracy. Also computational complexity of image segmentation is significantly reduced. Furthermore, there is no need any user supplied parameter such as the number of clusters or seed points.     

基于自然场景统计的无参考图像质量评价算法

为了度量多种失真类型的图像质量,提出一种基于图像空域自然场景统计特征的无参考图像质量评价算法。该算法通过度量失真图像和原始图像在统计规律上的偏差,对失真图像质量做出评价。与现有无参考图像质量评价算法相比,该算法不需要使用原始图像及其失真图像进行训练,也不需要知道图像的失真类型,是一种更具实际意义的通用型无参考图像质量评价算法。同时,考虑到人眼观察图像时感兴趣区域的影响,该算法加入了视觉显著性区域提取的过程。实验结果表明,该算法对于人的主观感知具有较好的一致性。