Texture Extraction for Image Retrieval Using
I nternational J ournal of A dvanced R esearch in E lectrical,
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Vol. 3, Issue 7, July 2014
DOI: 10.15662/ijareeie.2014.0307020 Texture Extraction for Image Retrieval Using
Local Tetra Pattern
Nitin Narayankar1, Sanjay Dhaygude2
Lecturer , Dept. of ELN, Walchand College of Engineering, Sangli, Maharashtra , India1 Associate Professor, Dept. of ELN, Walchand College of Engineering, Sangli, Maharashtra , India2
ABSTRACT: Content Based Image Retrieval(CBIR) is one of the prominent area to retrieve images from a large collection of database. There is wide range of texture analysis techniques used for feature extraction of an image. In this paper, we have proposed image indexing and retrieval algorithm for texture extraction using Local tetra pattern (LTrP). The local binary pattern (LBP) and local ternary pattern (LTP) encode the texture features of an image depending on the grey level difference between reference pixel and its neighbors. The LTrP encodes the relationship between the reference pixel and its neighbors by using the first-order derivatives in vertical and horizontal directions. Local tetra pattern (LTrP) extracts information based on the distribution of edges which are coded using four directions. To get the retrieval result we used Corel 1000 database. The performance of the proposed method is measured in terms of average precision and average recall. The performance analysis shows that the proposed method improves the retrieval result as compared with standard LBP.
KEYWORDS: Content Based Image Retrieval, Local Tetra Pattern (LTrP), Magnitude Pattern, Precision, Recall.
I.INTRODUCTION
A.General
Recent years have seen a rapid increase in the size of digital image collections. The image retrieval techniques are becoming very important part in the multimedia information retrieval, and they are most widely used in applications, such as in web related applications, agricultural applications, biomedical applications, earth and space sciences etc. Basically there are two research communities, the first one is text based image retrieval and the other is content based image retrieval (CBIR). Text based image retrieval gives less complexity method and they are widely used in image retrieval. But manual annotation is required to assist the text based retrieval process. Due to that, the text based image retrieval is not preferable in case of images. The feature extraction in CBIR is a prominent step whose effectiveness depends upon the method adopted for extracting features from given images. The CBIR utilizes visual contents of an image such as color, texture, shape, faces, spatial layout, etc., to represent and index the image database [1].Feature (content) extraction is the basis of content-based image retrieval. In this work, we propose a novel image texture feature extraction algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR).
The proposed method encodes the relationship between the referenced pixel and its neighbours, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In retrieval process, every pixel value of query image is compared with every pixel of test image using a distance measure to find some similar pictures to the query image. Two major approaches including spatial and transform domain based methods can be identified in CBIR systems. The first approach usually uses pixel or a group of adjacent pixels features like color, texture, and shape. Other uses different transforms like Gabor transform, Wavelet transform &Daubechieswavelet coefficients etc.[2][3].
B. Related work:
The LBP, the LDP, and the LTP extract the features based on the distribution of edges, which are coded using only two directions. The performance of these methods can be improved by differentiating the edges in more than two directions. This observation has motivated us to propose the four direction code, referred to as local tetra patterns
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DOI: 10.15662/ijareeie.2014.0307020
(LTrPs) for CBIR. The versions of the LBP and the LDP in the open literature cannot adequately deal with the range of appearance variations that commonly occur in unconstrained natural images due to illumination, pose, facial expression, age, etc. In order to address this problem, the local ternary pattern (LTP) has been introduced for face recognition under different lighting conditions. The local binary pattern (LBP) feature has emerged as a silver lining in the field of texture classification and retrieval which are converted to a rotational invariant version for texture classification. The LBP operator on facial expression analysis and recognition is successfully reported in proposed a multiscale heat-kernel-based face representation as heat kernels are known to perform well in characterizing the topological structural information of face appearance. The LBP operator on facial expression analysis and recognition is successfully reported and proposed a multiscaleheatkernel- based face representation as heat kernels are known to perform well in characterizing the topological structural information of face appearance. Various techniques for extraction and representation of image features like histograms local (corresponding to regions or sub-image) or global, color layouts, edges, boundaries & regions, textures and shapes have been reported in the literature.
II. DIFFERENT PATTERNS USED FOR TEXTURE
The different patterns used to extract texture feature are summarized in following sections.
A .Local Binary Pattern(LBP) :The standard local binarypattern (LBP) encodes the relationshipbetween the referenced pixel and its surrounding neighborsby calculating gray-level difference.The Local Binary Pattern was introduced for texture classification. It has at most two bitwise transitions from 0 to 1 or vice versa [4].
B .Local Ternary Pattern(LTP) : Local Ternary Pattern is extended version of LBP. It has three-valued code in accordance with grey values of its neighbors. In Local Ternary Pattern, gray values in the zone of width (±) t around are quantized to zero, those above ( + t) are quantized to +1, and those below are quantized to 1, i.e., indicator is replaced with three-valued function and the binary code is replaced by a ternary code[5].
C .Local Derivative Pattern(LDP) : The local Derivativepattern (LDP) encodes the pattern features based on local derivative variations. It gives more detailed information as LBP cannot obtain from image. The order LDP captures the detailed relationship in local neighborhood. LDP is micro pattern Representation modeled by histogram to preserve the information [6].
D .Local Tetra Pattern(LTrP) : The LBP, the LDP, and the LTP extract the texture features of an image based on the distribution of edges, which are coded using only two directions. The possible directions may be positive direction or negative direction. It is clear that the performance of these methods can be improved by differentiating the edges in more than two directions. So, The local tetra patterns (LTrPs) are adopted to encode information based on the four direction.
The idea of LTrP is based on local patterns described in LBP, LTP & LDP. The LTrP gives the spatial structure of the local texture using the direction of the centergraypixel .
Consider an image I , the first-order derivatives along 0 and 90 directions are denoted as | , . Let denotes the center pixel in I , and let & denote the horizontal and vertical neighborhoods of respectively. Then, the first-order derivatives at the center pixel can be written as
= ( ? )
= ( ? ) (1)
the direction of the center pixel can be formulated as
( )=????? 1 ( )≥0 ( )≥0 2 ( )<0 ( )≥0 3 ( )<0 ( )<0 4 ( )≥0 ( )<0?
???? (2)
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From above equation (5) we have four possible direction depending on first order derivatives for each center pixel. The possible values can be either 1, 2, 3, or 4, and finally, the image is converted into four values, i.e., directions. The second-order ( ) is defined as
( )=??????? ( ( ), ( ) ( ( ), ( ) ( ( ), ( )... ( ( ), ?
?????? (3) ( ( ), = 0 ( )=
(4)
From (3) and (4), we get 8-bit tetra pattern for each center pixel. Then, we separate all patterns into four parts
depending on the direction of center pixel. Finally, the tetra patterns for each part (direction) are converted to three binary patterns.
Let the direction of center pixel ( )obtained using (4) be “1”; then, can be defined by separating
it into three binary patterns as follows:
| , , = 2( )× ( )
| ?
( ) | ?= 1 ( )=? 0 (5) Similarly, the other three tetra patterns for remaining three directions of center pixels are converted to binary
patterns. Hence, we get 12 binary patterns (4 directions ×3 Patterns of each direction).
E .Advantages of the LTrPOver Other Patterns.
The advantages of the LTrP over the LBP, the LDP, and the LTP are as follows.
1) The LBP, the LDP, and the LTP are able to encode images with only two and three distinct values. However, the LTrP is able to encode images with four distinct values as it is able to extract more detailed information.
2) The LBP and the LTP encode the relationship between the gray value of the center pixel and its neighbors, whereas the LTrP encodes the relationship between the center pixels and its neighbors based on directions.
Figure 1. Calculation of tetra pattern bits for the center-pixel direction “1” using the direction of neighbors.
III. ESTIMATION OF MAGNITUDE COMPONENT & HISTOGRAM
The magnitude component & sign component extracts more useful information, as it is proved that the combination can provide better results which are not evident in any one individual component. So we used the Thirteenth binary pattern which is calculated by using the magnitudes of horizontal and vertical first-order derivatives[7].
= ( ) +( )
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= 2( )× ( ? ( )
| (6)
For the local pattern a pixel with P neighbourhoods, 2 combinations of LBPs are possible, which results the feature vector length of 2 . The computational cost of this feature vector is very high. In order to reduce the computational cost, we use the uniform patterns [8].The uniform appearance of pattern that has limited discontinuities in the circular binary representation is referred as uniform pattern.
In this paper, those patterns which have less than or equal to two discontinuities in the circular binary representation is referred to as the uniform patterns, and the remaining patterns are referred to as nonuniform. Therefore, the uniform patterns for a given query image would be ( ?1)+2. The possible uniform patterns for =8 would be 58.
Finally, after getting the local pattern the whole image is represented by constructing a histogram ( ) =
× ∑∑ ( ( , ), ); ∈[0, ( ?1)+2]
( , )= 1 = 0 (7) Where × represents the size of the input image.
Figure 2. Example to obtain the tetra and magnitude patterns
The possible local pattern transitions resulting in an LTrP for direction “1” of the center pixel are illustrated in figure
3.When the direction of center pixel is equal to neighboring pixel then the value of LTrP is coded to “0”, otherwise it is coded as the direction of neighborhood pixel. Similarly, LTrPs are calculated for center pixels having directions 2, 3, and
4.
An example of the second-order LTrP computation resulting in direction “1” for a center pixel marked with red has been illustrated in Fig. 4. The above equations are implemented to get tetra pattern. After coding the tetra
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DOI: 10.15662/ijareeie.2014.0307020 pattern, we separate it into three binary patterns as follows. Referring to the generated LTrP, the first pattern is obtained by keeping “1” where the tetra pattern value is “2” and “0” for other values, i.e., “0 0 0 0 0 0 1 0.” In the same way, the other two binary patterns “1 0 1 0 0 1 0 0” and “0 0 0 1 0 0 0 0” are computed for tetra pattern values “3” and “4,” respectively. Similarly, tetra patterns for center pixels having directions 2, 3, and 4 are computed. Hence, we get four tetra patterns and 12 binary patterns. The magnitude of the first-order derivatives is used to get 13th binary pattern.
IV.ALGORITHM FOR FEATURE EXTRACTION
A. Algorithm:
Input: Query image; Output: Retrieval result
1. Initially load the image, and convert it into gray scale.
2. In horizontal and vertical axis apply the first-order derivatives.
3. Find the direction for every pixel whether it is 1,2,3 or
4.
4. Based on the direction of the center pixel divide the patterns into four parts.
5. Calculate the tetra patterns, and separate them into three binary patterns.
6. Calculate the histograms of binary patterns.
7. Calculate the magnitudes of center pixels.
8. Construct the binary patterns, and plot their histogram.
9. Combine all histograms calculated from steps 6 and 8.
10. Construct the feature vector.
11. Compare the query image with the images in the database used.
12. Based on the best matches retrieve the images from database.
B. Sample images from database DB (one image per category).
Figure 3 : Sample images from database DB
C. . Image retrieval system framework
Figure 6 illustrates the flowchart of the proposed image retrieval system.
Figure 4. Image retrieval system framework
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DOI: 10.15662/ijareeie.2014.0307020 In order to analyze the performance of the proposed method, experiments were conducted on Corel databases. This database consists of a large number of images of various categories such as animals, sceneries, foods, sketches etc. These images have been preclassified into different categories each of size 100 by domain professionals. Some
researchers have the opinion that the Corel database [9] meets all the requirements to evaluate an image retrieval system, due to its large size and heterogeneous content. For our experiment, we have collected 1000 images to form database DB. These images are collected from ten different domains, namely, Africans, beaches, buildings, buses, dinosaurs, elephants, flowers, horses, mountains, and food. It has resolution of either 256× 384 or 384× 256. Fig. 5 shows the sample images of DB database (one image from each category). The results are discussed in the following
subsections.
V.QUERY MATCHING
The feature vector for the query image represented as =(
,
,
…..,
,
) is obtained from feature
extraction. Similarly, each image belongs to database is represented with the feature vector
=
,
,
…..,
,
=1,2,….. . The aim is to select best images that resemble the query image. This
can be done by selecting top-matched images by measuring the distance between the query image and the images in database . So similarity distance metric is used to match images.
( , )=∑
(8)
VI. RETRIEVAL RESULT AND PERFORMANCE OF PROPOSED METHOD
The performance of this method is calculated in terms of average precision and average recall, and it is defined
as.
=
=
?
In this experiment, every image in the database is used as the query image. For each query, the system collects database images with the shortest distance computed using (11). If the retrieved image belongs to same category as that of the query image, then we can conclude that the system has correctly identified the expected image, otherwise , the system has failed to find the expected image.
A.Table 1 : Average Precision
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B.Table 2 : Average Recall
C.Category wise retrieval result of the LTrP.
The figure 7 shows category wise retrieval result for Africans, Buses, Dinosaurs, flowers & food
respectively for retrieved images of size =4.
Query image Retrieved images for =4.
Figure 5: Retrieval result of proposed method.
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VII.CONCLUSION
In this paper, we have presented an approach for texture feature for CBIR using LTrPs. The LTrP encodes the images based on the direction of pixels that are calculated by horizontal and vertical derivatives. The magnitude of the binary pattern is collected using magnitudes of derivatives. The effectiveness of this method is measured in terms of average precision and average recall.
REFERENCES
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[4] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolutiongray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.
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[9] Corel 1000 and Corel 10000 image database [Online]. Available: https://www.sodocs.net/doc/1c14518983.html,/docs/related.shtml.
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智能图像分析系统
智能图像分析系统 解 决 方 案
北京恒泰同兴科技有限公司北京恒泰同兴科技有限公司是注册在中关村科技园区的高科技企业，成立于2004年，具有稳定的研发、生产、销售、服务队伍。恒泰同兴坚持自主开发之路，以“创造最大核心价值”为目标，以数字化、网络化、智能化为发展方向，专业从事图像智能识别、分析判断及自动处理产业化研究；公司研发的智能图像处理系统，与传统监控系统配合，为视频监控系统提供具有智能图像识别分析和告警的功能。可实现周界警戒与入侵检测、警戒线穿越检测、重要物品看护、遗留/遗弃物品检测、人体行为识别、道路交通检测等功能，可在各种恶劣气候、环境条件下进行目标识别和检测，避免了人工监控存在的易疲劳、易疏忽、反应速度慢、人工费用高等诸多不足，为客户提供了最佳安全监控系统解决方案。同时公司成功地开发大型行业联网解决方案，并有大量的实际案例，在视频监控行业积累了丰富的经验，智能监控和联网平台为用户提供了全方位的解决方案。公司本着诚实守信的经营之道，整合各种先进的技术资源，为客户定制最先进的行业解决方案，与各界用户一道，共同推进图像视频监控数字化、智能化和网络化进程。 恒泰同兴：持之以恒、稳如泰山 诚实、守信、专业、共赢
一、智能产品简介 智能视频分析系统是由位于前端或后端视频分析服务器，对监控摄像机所拍摄的视频图像进行分析，能将影像中的人、车或者物体的状态从任何背景中分离出来，加以辨认、分析与追踪。比对出所追踪对象的行为模式与预设的诸项安全规则，若发现违规之处，立刻进行报警通知，同时由使用平台进行信息记录或显示。 二、智能分析的功能 目前，智能视频分析系统在视频监控方向的应用主要在对运动目标的识别、分类和追踪。可以设置的规则、功能为以下几种：1、绊线检测 针对人、车通过特定运动方向绊线的监控；其应用如：警戒线、单向闸门流向、 栅栏攀爬…等；支持警戒区内多个目标同时告警、显示、报警图片抓拍、而且有 声音提示
图像处理外文翻译 (2)
附录一英文原文 Illustrator software and Photoshop software difference Photoshop and Illustrator is by Adobe product of our company, but as everyone more familiar Photoshop software, set scanning images, editing modification, image production, advertising creative, image input and output in one of the image processing software, favored by the vast number of graphic design personnel and computer art lovers alike. Photoshop expertise in image processing, and not graphics creation. Its application field, also very extensive, images, graphics, text, video, publishing various aspects have involved. Look from the function, Photoshop can be divided into image editing, image synthesis, school tonal color and special effects production parts. Image editing is image processing based on the image, can do all kinds of transform such as amplifier, reducing, rotation, lean, mirror, clairvoyant, etc. Also can copy, remove stain, repair damaged image, to modify etc. This in wedding photography, portrait processing production is very useful, and remove the part of the portrait, not satisfied with beautification processing, get let a person very satisfactory results. Image synthesis is will a few image through layer operation, tools application of intact, transmit definite synthesis of meaning images, which is a sure way of fine arts design. Photoshop provide drawing tools let foreign image and creative good fusion, the synthesis of possible make the image is perfect. School colour in photoshop with power is one of the functions of deep, the image can be quickly on the color rendition, color slants adjustment and correction, also can be in different colors to switch to meet in different areas such as web image design, printing and multimedia application. Special effects production in photoshop mainly by filter, passage of comprehensive application tools and finish. Including image effects of creative and special effects words such as paintings, making relief, gypsum paintings, drawings, etc commonly used traditional arts skills can be completed by photoshop effects. And all sorts of effects of production are
工具软件 翻译软件概述
工具软件翻译软件概述 翻译软件的产生是随计算机技术的进步而产生的一种应用软件。早期的翻译软件功能较弱，只能根据固定的词汇或词组进行翻译，仅相当于电子版本的词典。人工智能技术的发展为翻译软件提供了极大的技术支持。现代的翻译软件已经能够识别各种自然语言的简单语法，并根据一定的语义环境进行智能翻译。 1．翻译软件分类 根据翻译软件的功能，可以将翻译软件分为词典软件、屏幕翻译软件以及辅助翻译软件等三大类。 ●词典软件 词典软件是类似实体书词典的软件。其功能是将各种语言的词汇翻译存储到数据库中，供用户调用。当用户输入某个词汇后，即可将该词汇翻译为另一种语言，如图4-1所示。 图4-1 词典软件的原理 由于计算机存储数据和查找数据非常便捷，因此，词典软件的出现，免去了用户在实体书词典中翻找的不便，提高了用户查找词汇的效率。国内常用的词典软件包括金山词霸、东方大典等。 ●屏幕翻译软件 词典软件要翻译的主要是各种词汇和短语等，而屏幕翻译软件则需要对各种语句、段落甚至文章进行翻译。相对词典软件而言，屏幕翻译软件更加智能化，功能也更加强大。屏幕翻译软件的工作原理如图4-2所示。 图4-2 屏幕翻译软件的工作原理 屏幕翻译软件往往可以根据要翻译的内容词汇，自主选择相应的词典，然后根据词典的语义进行智能翻译。虽然屏幕翻译软件可以翻译一些简单的句子和段落，但仍然无法取代人工翻译。仅能在少数领域作为人工翻译的补充而存在。目前国内常用的屏幕翻译软件主要包括金山快译、灵格斯词霸等。 ●辅助翻译软件 辅助翻译软件是辅助人工翻译的软件。其作用是以数据库的方式储存原文和译文，在翻译时以电脑分析与搜寻翻译记忆库，找出相同或类似的句子，共译者参考。使用辅助翻译软
智能视频分析系统
智能视频分析系统
目录 一、项目背景及建设目标 (3) 1.1 项目背景 (3) 1.2 技术优势 (4) 二、厂区智能视频分析整体设计方案 (5) 2.1传统对射系统与智能视频分析系统比较 (5) 2.2厂房周界入侵报警系统 (6) 2.2.1 周界入侵检测 (7) 2.2.2 周界警戒线警戒区预警 (8) 2.3厂房仓库物资看护 (8) 2.3.1 可疑人员接近仓库提醒 (8) 2.3.2 仓库物品看护 (9) 2.3.3 夜间停车场、厂区内部、附近可疑逗留检测 (9) 2.4夜间厂区办公楼内可疑人员检测 (10) 2.5生产车间危险区域或者夜间下班后人员检测 (10) 2.6系统拓扑结构 (11)
一、项目背景及建设目标 1.1 项目背景 慧视科技智能视频分析系统是以软件的形式实现智能视频分析功能，拥有自主的软件知识产权，可满足各行业的需要，也满足各厂家设备的接入，同时可以与各种监控平台进行二次对接。传统报警设备的误报多漏报多操作复杂不直观已经成为行业共识，且传统的视频监控系统数量庞大画面单一，工作人员很难从视频中发现问题，往往更多用于事后取证，智能图像分析通过图像中目标的识别和规则运用来进行预警，报警速度快且精确度高，可辅助工作人员从繁琐重复的工作中解放出来，真正体现科技为人服务的理念。 国内现有厂房的视频监控系统主要由摄像机、光缆、矩阵、硬盘录像机和电视墙等组成。由于视频监控图像数量大，内容枯燥，现有系统即使配备值班人员，在大多数情况下仍处于无人观看的状态下。当犯罪事件发生时，从硬盘录像机中调取录像回放、取证变成系统主要的价值之一。即使值班人员在岗，由于人的生理特点，不可能长时间有效观察多路图像，很可能造成遗漏可疑事件，对安全形式产生错误判断。 智能视频监控技术可以理解为用计算机来帮助值班人员"看"监控录像。现代计算机的高可靠性可以提供24小时不间断地保护。从根本上杜绝由于人员疲劳造成的遗漏问题。同时也可以防止出现监控人员内外勾结的可能性。
图像处理中常用英文词解释
Algebraic operation 代数运算一种图像处理运算，包括两幅图像对应像素的和、差、积、商。 Aliasing 走样（混叠）当图像像素间距和图像细节相比太大时产生的一种人工痕迹。Arc 弧图的一部分；表示一曲线一段的相连的像素集合。 Binary image 二值图像只有两级灰度的数字图像（通常为0和1，黑和白） Blur 模糊由于散焦、低通滤波、摄像机运动等引起的图像清晰度的下降。 Border 边框一副图像的首、末行或列。 Boundary chain code 边界链码定义一个物体边界的方向序列。 Boundary pixel 边界像素至少和一个背景像素相邻接的内部像素（比较：外部像素、内部像素） Boundary tracking 边界跟踪一种图像分割技术，通过沿弧从一个像素顺序探索到下一个像素将弧检测出。 Brightness 亮度和图像一个点相关的值，表示从该点的物体发射或放射的光的量。 Change detection 变化检测通过相减等操作将两幅匹准图像的像素加以比较从而检测出其中物体差别的技术。 Class 类见模或类 Closed curve 封闭曲线一条首尾点处于同一位置的曲线。 Cluster 聚类、集群在空间（如在特征空间）中位置接近的点的集合。 Cluster analysis 聚类分析在空间中对聚类的检测，度量和描述。 Concave 凹的物体是凹的是指至少存在两个物体内部的点，其连线不能完全包含在物体内部（反义词为凸） Connected 连通的 Contour encoding 轮廓编码对具有均匀灰度的区域，只将其边界进行编码的一种图像压缩技术。 Contrast 对比度物体平均亮度（或灰度）与其周围背景的差别程度 Contrast stretch 对比度扩展一种线性的灰度变换 Convex 凸的物体是凸的是指连接物体内部任意两点的直线均落在物体内部。Convolution 卷积一种将两个函数组合成第三个函数的运算，卷积刻画了线性移不变系统的运算。 Corrvolution kernel 卷积核1，用于数字图像卷积滤波的二维数字阵列，2，与图像或信号卷积的函数。 Curve 曲线1，空间的一条连续路径，2 表示一路径的像素集合（见弧、封闭曲线）。 Deblurring 去模糊1一种降低图像模糊，锐化图像细节的运算。2 消除或降低图像的模糊，通常是图像复原或重构的一个步骤。 Decision rule 决策规则在模式识别中，用以将图像中物体赋以一定量的规则或算法，这种赋值是以对物体特征度量为基础的。 Digital image 数字图像 1 表示景物图像的整数阵列，2 一个二维或更高维的采样并量化的函数，它由相同维数的连续图像产生，3 在矩形（或其他）网络上采样一连续函数，并才采样点上将值量化后的阵列。 Digital image processing 数字图像处理对图像的数字化处理；由计算机对图片信息进
基于MATLAB的图像处理字母识别
数字图像处理 报告名称：字母识别 学院：信息工程与自动化学院专业：物联网工程 学号：201310410149 学生姓名：廖成武 指导教师：王剑 日期：2015年12月28日 教务处制
目录 字母识别 1.---------------------测试图像预处理及连通区域提取 2.---------------------样本库的建立采集feature 3.---------------------选择算法输入测试图像进行测试 4.---------------------总结
字母识别 1.imgPreProcess（联通区域提取）目录下 conn.m：连通区域提取分割（在原图的基础上进行了膨胀、腐蚀、膨胀的操作使截取的图像更加接近字母） %%提取数字的边界，生成新的图 clear; clc; f=imread('5.jpg'); f=imadjust(f,[0 1],[1 0]); SE=strel('square',5); %%膨胀、腐蚀、膨胀 A2=imdilate(f,SE); SE=strel('disk',3) f=imerode(A2,SE) SE=strel('square',3); f=imdilate(f,SE); gray_level=graythresh(f); f=im2bw(f,gray_level); [l,n]=bwlabel(f,8) %%8连接的连接分量标注 imshow(f) hold on for k=1:n %%分割字符子句 [r,c]=find(l==k); rbar=mean(r); cbar=mean(c); plot(cbar,rbar,'Marker','o','MarkerEdgeColor','g','MarkerFaceColor',' y','MarkerSize',10); % plot(cbar,rbar,'Marker','*','MarkerEdgecolor','w'); row=max(r)-min(r) col=max(c)-min(c) for i=1:row for j=1:col seg(i,j)=1; end
常用工具软件试题库
《常用工具软件》考试题库 一．判断题（每小题1分，共10分） 1. Realone Player不支持多节目连续播放。（X） 2.网际快车可以上传和下载文件。（√） 3. Internet上所有电子邮件用户的E-mail地址都采用同样的格式：用户名@主机名。（√） 4.Adobe Acrobat Reader可以解压缩文件。（X） 5.ACDSee是目前最流行的数字图像处理软件，它能广泛应用于图片的获取、管理、浏览、优化，甚至和他人的分享。（√） 6.天网防火墙的拦截功能是指数据包无法进入或出去。（X） 7.Symantec Ghost可以实现数据修复。（X） 8. 用户可以向金山词霸词库中添加没有收录的中、英文单词。（√） 9.系统长时间使用之后，会留下一堆堆垃圾文件，使系统变得相当臃肿，运行速度大为下降，但是系统不会频繁出错甚至死机。（√） 10.在使用FlashFXP软件下载网络中的FTP资源时，只需掌握FTP服务器的URL地址即可。（√） 11.在安装瑞星防火墙时，旧版本的瑞星防火墙无需卸载。（X） 12.压缩文件管理工具WinRAR只能压缩文件，不能对文件进行解压。（X） 13.在使用Virtual CD时，映像文件是不能被Windows资源管理器直接读取的，必须从Virtual CD中提取。（√） 14.在用Nero - Burning Rom软件制作CD时，可将数据文件从本地资源管理器中拖入了刻录机虚拟资源管理器中。（X） 15. 超级解霸3000能截取当前视频窗口中的图像存为图形文件。（√） 16.用MSN聊天时，可以隐身登录。（√） 17.ACDSee是目前最流行的数字图像处理软件，它能广泛应用于图片的获取、管理、浏览、优化，甚至和他人的分享。（√） 18、病毒不属于计算机软件（×） 19、优化大师就是让系统运行后没有垃圾文件（×） 20、注册表直接影响系统运行的稳定性（√） 21、清理注册表就是删除注册表中无用软件的注册信息（×） 22、360杀毒不能对单个文件进行病毒查杀（×） 23、根据工具软件使用的领域不同，但是一般都包含有标题栏、菜单栏、工具栏、状态栏、工作区。（√） 24、在进行实验操作时，为了不破坏现有的操作系统以及相关设置，我们可以使用虚拟机软件。（√） 25、在使用虚拟机的时候，按键盘右边的ALT可以在虚拟机和宿主机之间切换。（√） 26、CuteFTP是一个基于文件传输协议客户端软件。（√） 27、虚拟光驱是一种模拟CD-ROM工作的工具软件，它能在操作系统中模拟出新的光盘驱动器，是对物理光驱的一种仿真。（√） 28、利用ghost可以备份windows操作系统。（√） 29、常见的压缩格式ZIP格式、RAR格式、CBA格式、ACE格式。（√） 30、利用CuteFTP软件可以上传网站文件。（√） 31、Deamon Tools是一个优秀的虚拟光驱工具。（√）
基于matlab的人脸识别算法(PCA)
3.基于matlab的人脸识别算法 3.1 问题描述 对于一幅图像可以看作一个由像素值组成的矩阵，也可以扩展开，看成一个矢量，如一幅 N*N 象素的图像可以视为长度为N2 的矢量，这样就认为这幅图像是位于N2 维空间中的一个点，这种图像的矢量表示就是原始的图像空间，但是这个空间仅是可以表示或者检测图像的许多个空间中的一个。不管子空间的具体形式如何，这种方法用于图像识别的基本思想都是一样的，首先选择一个合适的子空间，图像将被投影到这个子空间上，然后利用对图像的这种投影间的某种度量来确定图像间的相似度，最常见的就是各种距离度量。因此，本次试题采用PCA算法并利用GUI实现。 对同一个体进行多项观察时，必定涉及多个随机变量X1，X2，…，Xp，它们都是的相关性, 一时难以综合。这时就需要借助主成分分析来概括诸多信息的主要方面。我们希望有一个或几个较好的综合指标来概括信息，而且希望综合指标互相独立地各代表某一方面的性质。 任何一个度量指标的好坏除了可靠、真实之外，还必须能充分反映个体间的变异。如果有一项指标，不同个体的取值都大同小异，那么该指标不能用来区分不同的个体。由这一点来看，一项指标在个体间的变异越大越好。因此我们把“变异大”作为“好”的标准来寻求综合指标。3.1.1 主成分的一般定义 设有随机变量X1，X2，…，Xp，其样本均数记为，，…，，样本标准差记为S1，S2，…，Sp。首先作标准化变换，我们有如下的定义： (1) 若C1=a11x1+a12x2+ … +a1pxp，…，且使 Var(C1)最大，则称C1为第一主成分； (2) 若C2=a21x1+a22x2+…+a2pxp，…，(a21，a22，…，a2p)垂直于(a11，a12，…，a1p)，且使Var(C2)最大，则称C2为第二主成分； (3) 类似地，可有第三、四、五…主成分，至多有p个。 3.1.2 主成分的性质 主成分C1，C2，…，Cp具有如下几个性质： （1) 主成分间互不相关，即对任意i和j，Ci 和Cj的相关系数 Corr(Ci，Cj)=0 i j (2) 组合系数(ai1，ai2，…，aip)构成的向量为单位向量， (3) 各主成分的方差是依次递减的，即 Var(C1)≥Var(C2)≥…≥Var(Cp)
常用CAT计算机辅助翻译软件
常用CAT计算机辅助翻译软件 1、SDL TRADOS SDL Trados为他们克服了在不同国家地区的文化、语言障碍，从而为他们的全球化铺平了发展道路。因为SDL Trados用户通常能够将完成工作的速度提高50%左右(具体数值依不同文档，项目会有变化)，更准确地评估时间和成本，显著减少翻译错误，编写更为一致的翻译（对技术、法律和医学翻译来说，这一点尤其重要）。这正因为其功能强大，在操作性方面就有所不足，在国内来说普及度不高。 2、iCAT iCAT辅助翻译工具免费软件，内嵌到Word工具中，支持最新的Word2013，支持64、32的系统，支持中文、繁体中文、英、日、韩、德、法、俄、西班牙等。它提供独立的术语和翻译记忆库（TM），可以同时挂多个术语库，同时通过火云术语配合使用，实现术语分享和收藏功能，达到云端保存的效果。自带机器翻译，术语批准等功能，同时译员通过使用该工具能及时了解自己最新的翻译字数。对于译后稿，提供3种保存格式，解决了译员对译后稿件的排版麻烦。该公司有兼全职译员3W多名，同时在各高校MTI教学和外语类实验室广泛使用，故在国内知名度很高。 3、passolo Passolo 是一款功能强大的软件本地化工具，它支持以Visual C++ 、Borland C++ 及Delphi 语言编写的软件（.exe、.dll、.ocx）的本地化。以往针对这两种不同语言编写的软件，我们大多是需要分别使用Visual Localize 和Language Localizator 来进行软件的中文化。而现在，Passolo 把二者的功能结合在了一起，并且性能稳定、易于使用，用户即不需要进行专门的训练，也不需要丰富的编程经验，在本地化的过程中可能发生的许多错误也都能由Passolo 识别或自动纠正。所以，passolo是软件本地化不二的选择。 4、Transmate Transmate 提供了独立的翻译操作界面，不依赖、也无需与其他应用程序交互（如MS WORD），在单一的程序界面中集成了翻译记忆库（TM）、术语库和翻译单元列表，界面简洁，操作方便。不像使用老版Trados 那样，需要启动多个不同的应用程序来分别操作记忆库、术语库和相关的文件。因其Transmate其实验室产品在高校广泛应用，故儿成为国内大多数译员比较熟悉的工具，在翻译公司的应用率也相对较高。 5、WordFast Wordfast 是结合Microsoft Word 使用的翻译记忆引擎。它可以在PC 或Mac 操作系统下运行。(请参阅技术规格或支持的操作系统)Wordfast 数据具有易用性和开放性，同时又与Trados 和大多数计算机辅助翻译(CAT) 工具兼容。它不仅可被用来翻译Word, Excel, Powerpoint, Access 文件，还可被用来翻译各种标记文件。此外，Wordfast 还可以与诸如PowerTranslator?，Systran?，Reverso? 等机器翻译(MT) 软件连接使用。另外，它还具有强大的词汇识别功能。所以，个人译员使用的比例相对较高. 6、Logoport Lionbridge 的免费产品，嵌入Word工具中，至于它的RTF文件是怎么做出来的，不得而知。它使用在线的TM服务器，可以很多译员同时翻译一个文件，TM时时共享，这和免费使用可以说是Logoport最大的优势，但是因为使用在线的TM，可能是他们服务器在国外的原因，每打开一个翻译单元格，都要花费一两秒钟的时间，译员怨声不断。初次看到分析出来的Log文件，可能会受到误导，认为那些１００％匹配不用翻译，其实Logoport是用本文件将要翻译出来的TM结果分析未曾翻译的文件，乍一看好似很多匹配，实际上都是需要翻译的"新词"，不过，匹配部分算钱的办法还算合理，比Trados匹配部分要高很多。所以,CAT爱好者可以玩一玩.
智能视频分析系统解决方案
智能视频分析系统解决方案 1.1 系统概述 智能视频（Intelligent Video）技术源自计算机视觉（Computer Vision）与人工智能（Artificial Intelligent）的研究，其发展目标是在图像与事件描述之间建立一种映射关系，使计算机从纷繁的视频图像中分辩、识别出关键目标物体。这一研究应用于安防视频监控系统，将能借助计算机强大的数据处理能力过滤掉图像中无用的或干扰信息，自动分析、抽取视频源中的关键有用信息，从而使传统监控系统中的摄像机成为人的眼睛，使“智能视频分析”计算机成为人的大脑，并具有更为“聪明”的学习思考方式。这一根本性的改变，可极大地发挥与拓展视频监控系统的作用与能力，使监控系统具有更高的智能化，大幅度节省资源与人员配置，同时必将全面提升安全防范工作的效率。因此，智能视频监控不仅仅是一种图像数字化监控分析技术，而是代表着一种更为高端的数字视频网络监控应用。 智能视频分析包含视频诊断、视频分析和视频增强等，它们各自又包含了大量的功能算法，比如清晰度检测、视频干扰检测、亮度色度检测、PTZ（云台）控制功能检测，以及视频丢失、镜头遮挡、镜头喷涂、非正常抖动等检测都属于视频诊断内容，而视频分析算法则包含区域入侵、绊线检测、遗留遗失检测、方向检测、人群计数、徘徊检测、流量统计、区域稠密度统计、人脸识别、车牌识别、烟火烟雾检测、自动 PTZ 跟踪等功能，视频图像增强则包括稳像、去雾、去噪、全景拼接等算法。由此组合衍生出的算法种类又有很多，应用方式也千变万化，所以智能视频分析的应用范围很广。 在以往的视频监控系统中，操作人员盯着屏幕电视墙超过 10 分钟后将漏掉90％的视频信息，而使视频监控工作失去意义。随着社会发展，视频监控被越来越广泛地应用到各行各业中，摄像机数量越来越庞大，这给传统的视频监控带来严峻的挑战。针对行业发展推出智能视频分析系统，主要解决以下问题：一个是将安防操作人员从繁杂而枯燥的“盯屏幕”任务解脱出来，由机器来完成分析识别工作；另外一个是为在海量的视频数据中快速搜索到想要找的的图象。 1.2 系统组成 智能视频分析系统以数字化、网络化视频监控为基础，用户可以设置某些特定的规则，系统识别不同的物体，同时识别目标行为是否符合这些规则，一旦发现监控画面中的异常情况，系统能够以最快和最佳的方式发出警报并提供有用信息，从而能够更加有效的协助安全人员处理危机，最大限度的降低误报和漏报现象。智能视频分析是在传统的监控系统中，加入智能视频技术，在整个系统中，系统分布图如下：
图像处理英文翻译
数字图像处理英文翻译 （Matlab帮助信息简介） xxxxxxxxx xxx Introduction MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas. The MATLAB system consists of these main parts: Desktop Tools and Development Environment This part of MATLAB is the set of tools and facilities that help you use and become more productive with MATLAB functions and files. Many of these tools are graphical user interfaces. It includes: the
基于MATLAB的人脸识别
基于ＭATＬAB的人脸识别
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图像识别 题目:基于MＡTＬAB的人脸识别 院系:计算机科学与应用系 班级: 姓名： 学号： 日期：
设计题目基于MAＴLAＢ的人脸识别设 计技术参数 测试数据库图片10张训练数据库图片20张图片大小１02４×7６8 特征向量提取阈值 1 设计要求综合运用本课程的理论知识,并利用MAＴＬAB作为工具实现对人脸图片的预处理,运用ＰCA算法进行人脸特征提取，进而进行人脸匹配识别。 工作量 两周的课程设计时间，完成一份课程设计报告书，包括设计的任务书、基本原理、设计思路与设计的基本思想、设计体会以及相关的程序代码； 熟练掌握Maｔlab的使用。 工作计划第1-2天按要求查阅相关资料文献,确定人脸识别的总体设计思路； 第３-4天分析设计题目，理解人脸识别的原理同时寻求相关的实现算法；第５-８天编写程序代码，创建图片数据库,运用ＰCＡ算法进行特征提取并编写特征脸，上机进行调试； 第9-12天编写人脸识别程序,实现总体功能； 第13-1４天整理思路,书写课程设计报告书。 参考资料1 黄文梅，熊佳林,杨勇编著．信号分析与处理——MATAＬB语言及应用.国防科技大学出版社，2000 2 钱同惠编著.数字信号处理.北京:机械工业出版社，２004 3 姚天任,江太辉编著.数字信号处理．第2版.武汉:武汉理工大学出版社,200０ 4 谢平，林洪彬，王娜.信号处理原理及应用．机械工业出版社，２00４ 5刘敏，魏玲.Mａｔｌａb.通信仿真与应用.国防工业出版社，20０5 6 楼顺天.基于Ｍatlab7．x 的系统分析与设计.西安电子科技大学,200２ 7孙洪.数字信号处理.电子工业出版社，２001 目录 引言?错误!未定义书签。 1 人脸识别技术?错误!未定义书签。 1.1人脸识别的研究内容?错误!未定义书签。 1.1.1人脸检测(Faｃe Dｅｔection）........... 错误!未定义书签。