工程造价管理体系的信息化沟通

目前,我国的项目投资控制力度不够,工程"三超"现象严重,主要原因在于项目建设各阶段的投资控制政出多门,各部门控制措施上缺乏必要的联系,本文从利用互联网信息传递快捷、方便的优点出发,拟在投资估算、设计概算、施工图预算、工程评标之间形成信息交流的通道,这一点也非常适合我国目前正推行的工程量清单招标,与此同时,建立包含工程建设项目相关情况的信息资源库,加强信息资源的开发和利用,强化信息资源共享,借鉴国际通行的工程量清单方法确定和控制投资。本文所提出的思路将为工程造价管理的软件开发提供一定的参考。

Application of Computer Vision Technique to Maize Variety Identification

Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.

Performance evaluation of high frequency sub-bands of wavelet transform for palmprint recognition

Wavelet decomposition has been applied in palmprint recognition successfully. However, only the low frequency sub-band was used for further feature extraction, while the high frequency sub-bands were consid2 ered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. In this pa- per, we firstly investigate the performances of all the sub-bands by using principal component analysis （PCA） on the BJTU and PolyU palmprint databases, and then use mean filtering to enhance the robustness of the high frequency sub-bands. We find that the preprocessed high frequency sub-bands not only can be used for palm- print recognition but also contain complementary information with the low frequency sub-band. The experimental results show that the performances of the horizontal and vertical high frequency sub-bands can be promoted up to a competitive level, and the fusion scheme, which combines the matching scores of high frequency sub-bands with that of low frequency sub-band, is superior to the conventional recognition methods.

A new semi-fragile watermarking algorithm for image authentication

This paper presents a new semi-fragile watermarking algorithm for image authentication which extracts image features from the low frequency domain to generate two watermarks: one for classifying of the intentional content modification and the other for indicating the modified location. The algorithm provides an effective mechanism for image authentication. The watermark generation and watermark embedment are disposed in the image itself, and the received image authentication needs no information about the original image or watermark. The algorithm increases watermark security and prevents forged watermark. Experimental results show that the algorithm can identify intentional content modification and incidental tampering, and also indicate the location where a modification takes place.

A Skeleton Extraction Framework Based on Inner-Product and Border Gap

A novel algorithm for skeleton extraction is proposed in the paper. By numbering objeet＇s border dements on spatial position, the border gap （BG） of inner pixel of the object is calculated; an 8-connected medial-axis hierarchy is derived by the BG; a thinning method including slicing and counting is proposed to improve the processing speed; branches with minor importance are truncated by vector diversity Vd and length-width ratio （LWR） with support vector machine （SVM） classifier. Experiments demonstrate that the derived skeletons keep good connectivity, especially in long and narrow area.

Storage capacity of complex Hopfield model

The Dirac symbol is used to represent the discrete complex Hopfield neural network model.The signal-to-noise theory and the computer numerical solution are made to analyse the storage capacity of the model.The storage capacity ratio of the model equals to that of the Hopfield model.Finally,using the model to recognize the 4-level grey or color patterns is discussed.

Principle and realization method of tunnel deformation detection based on image recognition and data transmission technology

To detect the deformation of the tunnel structure based on image sensor networks is the advanced study and application of spatial sensor technology. For the vertical settlement of metro tunnel caused by internal and external stress after its long period operation, the overall scheme and measuring principle of tunnel deformation detection system is in- troduced. The image data acquisition and processing of detection target are achieved by the cooperative work of image sensor, ARM embedded system. RS485 communication achieves the data transmission between ARM memory and host computer. The database system in station platform analyses the detection data and obtains the deformation state of tunnel inner wall, which makes it possible to early-warn the tunnel deformation and take preventive measures in time.

A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

Video learning based image classification method for object recognition

Automatic image classification is the first step toward semantic understanding of an object in the computer vision area.The key challenge of problem for accurate object recognition is the ability to extract the robust features from various viewpoint images and rapidly calculate similarity between features in the image database or video stream.In order to solve these problems,an effective and rapid image classification method was presented for the object recognition based on the video learning technique.The optical-flow and RANSAC algorithm were used to acquire scene images from each video sequence.After the selection of scene images,the local maximum points on comer of object around local area were found using the Harris comer detection algorithm and the several attributes from local block around each feature point were calculated by using scale invariant feature transform （SIFT） for extracting local descriptor.Finally,the extracted local descriptor was learned to the three-dimensional pyramid match kernel.Experimental results show that our method can extract features in various multi-viewpoint images from query video and calculate a similarity between a query image and images in the database.

EFFICIENT IMPLEMENTATION OF 3D FILTER FOR MOVING OBJECT EXTRACTION

In this paper the design and implementation of Multi-Dimensional (MD) filter, particularly 3-Dimensional (3D) filter, are presented. Digital (discrete domain) filters applied to image and video signal processing using the novel 3D multirate algorithms for efficient implementation of moving object extraction are engineered with an example. The multirate (decimation and/or interpolation) signal processing algorithms can achieve significant savings in computation and memory usage. The proposed algorithm uses the mapping relations of z-transfer functions between non-multirate and multirate mathematical expressions in terms of time-varying coefficient instead of traditional polyphase de- composition counterparts. The mapping properties can be readily used to efficiently analyze and synthesize MD multirate filters.

Research on Network Training System Facing Enterprise Knowledge Management

The existing computer and network technology makes the enterprise training transform from the traditional mode into new mode. The paper studies how to combine enterprise knowledge management and network training to make the enterprise training meet the demands of knowledge management and improve the competitiveness of enterprises. And the paper puts forwards the new opinion combining enterprise knowledge management and network training system. The purpose of applying knowledge map and knowledge push to training system is to integrate knowledge management into training system to make the enterprises face the challenge of knowledge economy.

Three-phase Pupil Localization Method in Non-ideal Eye Images

Pupil localization is a very important preprocessing step in many reel applications. Accurate and robust pupil localization in non-ideal eye images is a challenging task. A detailed method of pupil localization in non-ideal eye images is proposed. This method is implemented in three main phases： first, segment the rough pupil region based on Gaussian Mixture Model： then modify the rough segmentation result using morphological method to minimize the influence of some disturbing factors; last estimate the pupil parameters based on minimizing the least square error. The proposed method is first tested on CASIA iris image dataset, and then on our self-captured iris dataset which contains a wider variety of iris images. Experiments show that the proposed method can perform well for nonideal eye images of various qualities.

Online Sequential Extreme Multilayer Perception with Time Series Learning Machine Based Output Self Feedback for Prediction

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.

Time-Series Forecasting Using Autoregression Enhanced k-Nearest Neighbors Method

This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.

Component SPD matrices： A low-dimensional discriminative data descriptor for image set classification

In pattern recognition,the task of image set classification has often been performed by representing data using symmetric positive definite(SPD)matrices,in conjunction with the metric of the resulting Riemannian manifold.In this paper,we propose a new data representation framework for image sets which we call component symmetric positive definite representation(CSPD).Firstly,we obtain sub-image sets by dividing the images in the set into square blocks of the same size,and use a traditional SPD model to describe them.Then,we use the Riemannian kernel to determine similarities of corresponding subimage sets.Finally,the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets;its i,j-th entry measures the similarity between the i-th and j-th sub-image sets.The Riemannian kernel is shown to satisfy Mercer’s theorem,so the CSPD matrix is symmetric and positive definite,and also lies on a Riemannian manifold.Test on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification.

Recognition of Red Cell and Megakaryocyte Based L-Shaped Envelope Function

In order to explore the cell composition and its metabolism,it is important to let computer recognize the cells and get the counts of different cells for a sample.This paper proposes an L-shaped envelop function and the related fuzzy clustering method as a way to identify the megakaryocyte and the red cell from the sliced marrow image.This method is useful when the staining is insufficient and the color cannot be used as the identifying factor.This method uses the experimental histogram data to fit the L-shaped function and then use it as the envelop for the match test.The fuzzy c-means(FCM) performance index is used to test the adjacent area and get the minimum and finally secure the identification.The new method is not limited to megakaryocyte or red cell and can be used for general purposes of cell recognition.Tests show that this envelop function can ensure the recognition rate with different staining batches and can reach satisfied counting under similar illumination condition.