用于柑桔成熟度无损检测的色度频度序列法研究

农产品内部品质无损检测技术是确定水果最合适的采收期和对成熟度不一致的农产品进行准确分级的关键．为了建立利用计算机视觉技术进行柑桔成熟度的无损检测的方法,本研究建立了用于柑桔成熟度检测的计算机视觉系统,研究了柑桔图像颜色的描述方法,通过分析比较,认为在利用水果可见光彩色图像检测水果成熟度时,宜采用HSI颜色模型空间;提出了用与各个色度对应的像素在图像中出现的频度构成的频度序列描述图像的颜色信息的新方法,并利用人工神经网络方法建立了根据柑桔图像的色度频度序列判断柑桔成熟度的映射器,这一映射器检验252只成熟度不同的尾张系柑桔的结果为,对成熟果实和未熟果实的判断正确率分别为 79 1％和63．6％,总的判断正确率为77．8％,这表明尾张系柑桔果实的表皮颜色与成熟度之间具有相关性,可以通过利用计算机视觉技术测定柑桔的表皮颜色信息来判断柑桔的成熟度．     

几种染色剂对桔小实蝇产卵孔的染色效果

为寻找水果上桔小实蝇产卵情况的快速检测方法,本文测定了甲基蓝、龙胆紫、品红、曙红、藏红和刚果红等6种染色剂对水果上桔小实蝇Bactrocera dorsalis（Hendel）产卵孔的染色效果。结果表明染色剂对可疑水果进行染色,若有产卵孔,产卵孔可迅速被染色,染色率最高可达100％。筛选出甲基蓝、藏红和刚果红作为芒果、番石榴和夏橙的最适染色剂,各种染色剂在浓度为0．5％时染色效果较佳,同时对持续冷藏保存后的水果上的产卵孔仍然具有较高的染色率。     

水果酵素对小鼠胃肠道消化功能的影响

目的:研究水果酵素对小鼠胃肠道消化功能的影响。方法:80只小鼠随机分为4组,分别为对照组、实验组1、2、3。实验组分别喂养浓度为2.5%、5%、7.5%的水果酵素,对照组喂养正常饲料。饲养28天后,作胃排空实验以及肠推进功能检测。结果:1与对照组相比较,实验组小鼠的肠推进功能较强。2随着水果酵素浓度的增高,小鼠的胃内残留率呈下降趋势。3实验组小鼠红细胞数与血小板数水平高于对照组,白细胞水平随喂养水果酵素浓度的上升而呈下降趋势,淋巴细胞则相反。4与对照组相比,实验组小鼠,平均体重明星减轻。结论:水果酵素对小鼠胃肠动力、肠道机械运动功能提升均有促进作用,且随着水果酵素喂养浓度的增加,促进作用逐渐增强。     

广州地区农产品中有机磷农药残留调查

采用国家标准方法气相色谱(GC)法进行分析,通过对蔬菜、水果、粮食中16种有机磷农药残留量的检测,了解有机磷农药在广州地区农产品中的残留及其分布情况.50份农产品样品中有机磷农药各组分总的检出率为96.0%,其中蔬菜、水果、粮食检出率分别为96.8%、100%、80.0%.50份农产品样品中有机磷农药各组分总的超标率为36.0%,其中蔬菜、水果、粮食超标率分别为41.9%、21.4%、40.0%,16种有机磷农药中以马拉硫磷的超标率最高,为32.0%.不同组分有机磷农药在农产品中的残留量,以杀螟磷的平均含量最高,为1308.7μg·kg^-1,其次是辛硫磷和甲胺磷,分别为970.1μg·kg^-1和737.6μg·kg^-1;因此应提倡合理使用农药,加强农药使用的引导和监管.     

中国水果和蔬菜昆虫授粉的经济价值评估

水果和蔬菜是二大类最主要的虫媒作物, 在农业生产中占据十分重要的地位, 但是近几十年来授粉昆虫在多国出现了不同程度的下降, 影响了农业生产的经济效益。为了明确中国主要授粉昆虫的现状以及昆虫授粉在中国水果和蔬菜生产中的经济地位, 本文分析了1961-2009年间中国主要授粉昆虫蜜蜂的数量动态以及水果和蔬菜种植的变化特征; 并以2008年种植的与人类食品密切相关的44种水果和蔬菜为研究对象, 引入农作物对昆虫授粉的依赖性参数, 应用生物经济学的方法评估了昆虫授粉对中国水果和蔬菜产生的经济价值。结果表明: 1961-2009年的49年之间, 中国主要授粉昆虫蜜蜂蜂群数量增加了161%, 水果和蔬菜种植面积增加了472%, 产量增加了833%。中国水果和蔬菜中虫媒作物产量的提高, 与作物种植面积的增长密切相关（r=0.995, P<0.01）, 也与主要授粉昆虫蜜蜂蜂群数量增加有关（r=0.804, P<0.01）。2008年昆虫授粉对中国水果和蔬菜产生的经济价值为521.7亿美元, 占44种水果和蔬菜总产值的25.5%。水果类对昆虫授粉的依赖程度较高, 授粉产生的经济价值大于蔬菜类。在昆虫授粉的贡献中, 苹果、西瓜、梨、芒果和李占据前5位。昆虫授粉对中国水果和蔬菜产生的经济价值十分巨大, 中国水果和蔬菜对昆虫授粉的依赖程度超过15.9%的全球平均水平。随着中国水果和蔬菜种植面积的持续增长, 中国需要更多的授粉昆虫为其提供授粉服务。     

粤东地区野果植物资源

本文报道了粤东地区野生水果植物资源的调查结果,记录到该地区野生水果植物有57科、101属、205种(含变种),分别占全国野生果树73科、173属、1157种(变种)的78．1％、58．4％、17．7％,含15种以上的科有蔷薇科、壳斗科、芸香科、桑科4个科,为该地区野生水果的优势科,这说明粤东地区的野生水果植物资源极为丰富．     

介绍两种水果保鲜贮存的简易方法

介绍两种水果保鲜贮存的简易方法方法一：有些水果成熟是由于内源产生激素乙烯促催而熟。为延缓成熟，用高锰酸钾近饱和溶液氧化乙烯。为增加其接触表面积。再浸泡除去细灰的炉渣，晾干用纱布包０．５～０．７５ｋｇ／包放于水果筐上部，用塑料袋将筐封起来，使ＫＭｎ（）...     

近红外窄带光照下不同水果背景中桔小实蝇的图像分割

【目的】为了增强水果背景中桔小实蝇Bactrocera dorsalis Hendel（双翅目实蝇科）的识别效果,研究了该种昆虫与不同水果之间的反射光谱差异。【方法】采用紫外 可见光 近红外分光光度计测量了桔小实蝇与16种水果在400~2 500 nm波段的反射光谱。在中心波长为565 nm和827 nm的窄谱带光源及日光3种光源分别照射下,分别拍摄各种水果背景中的桔小实蝇照片,并用大津Otsu算法对照片进行二值化处理。【结果】发现桔小实蝇的反射率随波长增加而缓慢地增大,最大反射率小于40%。而16种水果的最强反射峰全部或部分落在在777～896 nm。不同水果平均最大反射率为41.10%～97.89%,与桔小实蝇在此波段的低反射率（约30%）形成强烈的反差。在827 nm中心波长窄带光源照射下拍摄的照片中,发现桔小实蝇为黑色,而背景水果呈现大面积的白色,形成高反差,桔小实蝇很容易被辨识。相反,在日光和565 nm中心波长窄带光源照射的照片中,水果背景存在较多的黑色斑块,容易与桔小实蝇的黑区混淆;或者该虫形成白斑,从而无法识别。【结论】选用近红外波段的窄带光源照射能明显提高桔小实蝇与水果图像的对比度,增强桔小实蝇的分割效果。     

水果质量安全检测现状

主要论述了影响水果质量安全的农药残留和防腐保鲜剂残留的危害及其检测方法。     

湖北后河自然保护区果子狸食物组成初步研究

2005年4~10月在湖北后河自然保护区,采集果子狸(Paguma larvata)粪便,应用频次法进行分析,再结合胃内容物分析,同时根据采食痕迹,对该物种春、夏和秋季的食物组成进行了研究。结果表明,果子狸食物组分以水果类植物(37·5%)、节肢动物(25·2%)为主,其次为非水果类植物(9·9%)和小型哺乳动物(7·1%)等。果子狸食物组成在不同的季节间有一定差异。春季,果子狸食物组成中以节肢动物(35·6%)为主,其次是非水果类植物(26·7%)和小型哺乳动物(15·6%),另外还有少量的软体动物(4·4%)和水果类植物(8·9%);夏季,果子狸食物组成转为以水果类植物(39·8%)和节肢动物(29·2%)为主,此外还有少量非水果类植物(2·7%)和小型哺乳动物(5·3%),以及少量鸟类(0·9%);秋季,果子狸食物组分中含有大量的水果类植物(63·9%),其他食物类别比例很小,包括鸟类(1·0%)、小型哺乳动物(0·6%)和爬行动物(0·4%)等。食物多样性指数显示,随着季节变化,果子狸食物多样性逐渐下降,小型哺乳动物和非水果类植物在其食性组成中逐渐下降,而水果类植物则转为最主要食物。     

Artificial neural networks for blur identification and restoration of nonlinearly degraded images.

In this paper, an image restoration algorithm is proposed to identify noncausal blur function. Image degradation processes include both linear and nonlinear phenomena. A neural network model combining an adaptive auto-associative network with a random Gaussian process is proposed to restore the blurred image and blur function simultaneously. The noisy and blurred images are modeled as continuous associative networks, whereas auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the system. The self-organization like structure provides the potential solution of the blind image restoration problem. The estimation and restoration are implemented by using an iterative gradient based algorithm to minimize the error function.     

Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese–Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred–PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.     

Coded-exposure camera and its circuits design

In the industrial production line, the motion of the target is the main reason for blurred image of the camera monitoring. A coded-exposure devices and circuits are designed to get restored image from this motion blurring. A given binary code sequence which represent open or close of shutter in CCD circuits driven by FPGA is used to control the exposure-time. The sampled images are processed by deconvolution algorithm and the high frequency information of them could be preserved by using the coded-exposure sequence resulting in blurred image restoration. The de-blurred problem could be converted to a well-posed from an ill-posed one. Experiments demonstrate that using the coded-exposure, the device proposed is able to improve the quality of blurred image.     

An image enhancement approach for coral reef fish detection in underwater videos

Coral reefs are rich in fisheries and aquatic resources, and the study and monitoring of coral reef ecosystems are of great economic value and practical significance. Due to complex backgrounds and low-quality videos, it is challenging to identify coral reef fish. This study proposed an image enhancement approach for fish detection in complex underwater environments. The method first uses a Siamese network to obtain a saliency map and then multiplies this saliency map by the input image to construct an image enhancement module. Applying this module to the existing mainstream one-stage and two-stage target detection frameworks can significantly improve their detection accuracy. Good detection performance was achieved in a variety of scenarios, such as those with luminosity variations, aquatic plant movements, blurred images, large targets and multiple targets, demonstrating the robustness of the algorithm. The best performance was achieved on the LCF-15 dataset when combining the proposed method with the cascade region-based convolutional neural network (Cascade-RCNN). The average precision at an intersection-over-union (IoU) threshold of 0.5 (AP₅₀) was 0.843, and the F1 score was 0.817, exceeding the best reported results on this dataset. This study provides an automated video analysis tool for marine-related researchers and technical support for downstream applications.     

Error Management Theory and biased first impressions: How do people perceive potential mates under conditions of uncertainty?

People must make inferences about a potential mate's desirability based on incomplete information. Under such uncertainty, there are two possible errors: people could overperceive a mate’s desirability, which might lead to regrettable mating behavior, or they could underperceive the mate’s desirability, which might lead to missing a valuable opportunity. How do people balance the risks of these errors, and do men and women respond differently? Based on an analysis of the relative costs of these two types of error, we generated two new hypotheses about biases in initial person perception: the Male Overperception of Attractiveness Bias (MOAB) and the Female Underperception of Attractive Bias (FUAB). Participants (N = 398), who were recruited via social media, an email distribution list, and snowball sampling, rated the attractiveness of unfamiliar opposite-sex targets twice: once from a blurred image, and once from a clear image. By randomizing order of presentation (blurred first vs. clear first), we isolated the unique effects of uncertainty—which was only present when the participant saw the blurred image first. As predicted, men overperceived women's attractiveness, on average. By contrast, as predicted, women underperceived men's attractiveness, on average. Because multiple possible decision rules could produce these effects, the effects do not reveal the algorithm responsible for them. We explicitly addressed this level of analysis by identifying multiple candidate algorithms and testing the divergent predictions they yield. This suggested the existence of more nuanced biases: men overperceived the attractiveness of unattractive (but not attractive) women, whereas women underperceived the attractiveness of attractive (but not unattractive) men. These findings highlight the importance of incorporating algorithm in analyses of cognitive biases.     

Lung Nodule Detection Based on YOLOv3 Deep Learning with Limited Datasets

The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is needed. In order to overcome the problem of less training data when starting a new study of Computer Aided Diagnosis (CAD), we firstly pick up a small number of diseased regions to simulate a limited datasets training procedure: 5 nodule patterns are selected and deformed into 110 nodules by random geometric transformation before fusing into 10 normal lung CT images using Poisson image editing. According to the experimental results, the Poisson fusion method achieves a detection rate of about 65.24% for testing 100 new images. Secondly, 419 slices from common database RIDER are used to train and test our YOLOv3 network. The time of lung nodule detection by YOLOv3 is shortened by 2–3 times compared with the mainstream algorithm, with the detection accuracy rate of 95.17%. Finally, the configuration of YOLOv3 is optimized by the learning data sets. The results show that YOLOv3 has the advantages of high speed and high accuracy in lung nodule detection, and it can access a large amount of CT image data within a short time to meet the huge demand of clinical practice. In addition, the use of Poisson image editing algorithms to generate data sets can reduce the need for raw training data and improve the training efficiency.     

基于信号稀疏表示的高光谱散射图像苹果粉质化分类

苹果的粉质化是指苹果果肉发软、汁液减少等一系列物理和生理变化现象,采用高光谱散射图像技术结合信号稀疏表示分类算法(SRSA)研究了苹果的粉质化分类问题。首先利用平均反射算法(MEAN)提取了600～1000 nm的高光谱散射图像特征;引入遗传算法(GA)解决分类样本的不均衡问题,在此基础上,把苹果的粉质化分类问题,转化为一个求解待识别样本对于整体训练样本的稀疏表示问题。仿真结果表明,基于信号稀疏表示分类算法的苹果粉质化分类精度为79.8%,高于偏最小二乘判别分析(PLSDA)的74.8%,为苹果的粉质化分类提供了一种新的有效的方法。     

An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.     

An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.     

Contour extraction of Drosophila embryos

Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blurred embryo boundaries. We test the proposed framework on a data set of 8,000 embryonic images, and achieve promising accuracy (88 percent), that is, substantially higher than the-state-of-the-art results.