Description: DameWare DBtnMap ActiveX component is a 32-bit custom control for use with Visual Basic, Visual C++ and Web pages or any environment that can host ActiveX controls. DBtnMap will allow any picture file (.JPG, .GIF, .BMP, TIFF, etc.) type supported, to be used as an Image Map. This functionality works identically to an Image Map created for a Web page. DBtnMap supports RECT, CIRCLE and POLY types for the Image Map. DBtnMap utilizes unique animation characteristics with fly-by/hover and click features for each object type specified within the Image Map. Another distinguishing feature available within DBtnMap is sound support. DBtnMap has several properties including PictureFile, SoundClick and SoundHover. Each of these sound properties can be customized from within your application, or Web page to point to your own sound file(s) (.WAV). Platform: |
Size: 95175 |
Author:fwh |
Hits:
Description: DameWare DBtnMap ActiveX component is a 32-bit custom control for use with Visual Basic, Visual C++ and Web pages or any environment that can host ActiveX controls. DBtnMap will allow any picture file (.JPG, .GIF, .BMP, TIFF, etc.) type supported, to be used as an Image Map. This functionality works identically to an Image Map created for a Web page. DBtnMap supports RECT, CIRCLE and POLY types for the Image Map. DBtnMap utilizes unique animation characteristics with fly-by/hover and click features for each object type specified within the Image Map. Another distinguishing feature available within DBtnMap is sound support. DBtnMap has several properties including PictureFile, SoundClick and SoundHover. Each of these sound properties can be customized from within your application, or Web page to point to your own sound file(s) (.WAV). Platform: |
Size: 95232 |
Author:fwh |
Hits:
Description: 对一幅具有一些特征点的图像 得到灰度图、二值图，最后利用获得的二值图像对原图像中的特征点进行计数。-Has some characteristic point of an image to be grayscale, binary map, the final binary image obtained using the original image of the feature points are counted. Platform: |
Size: 239616 |
Author:buffontus |
Hits:
Description: 提取图像的颜色特征，首先将图像颜色映射到指定的8个颜色，然后利用自相关和互相关计算得到图像的特征向量，并从原数据库中算出利用此方法检索图像的正确率，并将检索结果好的图像显示到指定文件夹中。-Extract image s color characteristics.First, the image color map to the specified eight colors, then use the auto-correlation and cross-correlation calculation of image feature vectors.and Calculated from the original database, retrieve the images using this method, the correct rate, and a good image display search results to a specified folder. Platform: |
Size: 324608 |
Author:董懂 |
Hits:
Description: Abstract—We present a fast feature selection algorithm
suitable for object detection applications where the image
being tested must be scanned repeatedly to detected the object
of interest at different locations and scales. The algorithm
iteratively estimates the belongness probability of image pixels
to foreground of the image. To prove the validity of the
algorithm, we apply it to a human detection problem. The
edge map is filtered using a feature selection algorithm. The
filtered edge map is then projected onto an eigen space of
human shapes to determine if the image contains a human.
Since the edge maps are binary in nature, Logistic Principal
Component Analysis is used to obtain the eigen human shape
space. Experimental results illustrate the accuracy of the human
detector. Platform: |
Size: 492544 |
Author:谷川 |
Hits:
Description: 汽车防撞，技术路径不外：1.雷达测距防撞；2.视差测距防撞。前者，一旦保有量较大，必定遭遇互相干扰问题；后者，以前主要问题是，算法复杂，实时性差。
本文公开了一种新算法（已申请发明专利），主要运算可以借助硬件组合逻辑模块并行执行，可以极大提高视差测距的实时性，满足汽车防撞的要求。-(Background) Stereo matching, requires in two images to identify two pixels to be matched each other, in the field of machine vision, this is a difficult, but central task To complete the task, the key is to strengthen pixels characteristics (status)In order to strengthen the characteristics of pixels , has developed combination of the pixel neighborhood features technology, a variety of window matching technology, and matching prominent features of pixels only, for example, the edge pixels in the image, or to match with the Sift technical characteristics of pixels The window matching Technology, in theory, the larger the window, the more prominent features of pixels to be matched, but the resulting: 1. the computational size is increase 2. the windows across the actual object boundary, so that， the same characteristics pixel in the other image is inexistence，making computing more difficult, so two questions The feature matching, can acquire sparse disparity map only, and mat Platform: |
Size: 318464 |
Author:李维纲 |
Hits:
Description: 本示例主要的功能是通过给定的一组成绩数据绘制出成绩的分布图（包括直方图和圆饼图）。这里没有用到数据库，因为是一个简单的成绩分布图，而用数据库的话在转到另一台机子上还要重新建表，对于学生信息也要涉及到录入问题，不容易实际操作，所以本示例采用了更为方便的通过手动录入数据或者是从文件（txt格式文本文档，第一个数为总人数，其余为分数，分数之间用逗号隔开）导入数据两种方式绘制出其统计图。录入的成绩可查看可修改，直方图采用了立体图模式，看起来更直观，各个分数段都有明显的分数人数标注，看起来直观形象。饼形图也同样标注了各颜色对应的分数段及人数，看起来很直观。数据录入部分与绘图部分集成在一个对话框上面，没有在单个文档中调用，减少了不必要的繁琐过程-The main feature of this example is given a set of performance data to map out the distribution of results (including histograms and pie charts). Database is not used here because the result is a simple map, but with the words in the database to another machine on the table but also to re-build, but also for the student information related to the input problem, is not easy to practice, so this example by using a more convenient data entry or manual from file (txt format text documents, the first number is the total number of rest points, scores, separated by commas) two ways to import data to map out their charts . Entry can be modified to view the results, using a three-dimensional histogram graph model, it seems more intuitive, each segment has a significant fraction of the number of marked points, it seems intuitive image. Pie charts are also marked with the color and number of segments corresponding to the score, it seems very intuitive. Data entry and some portion of the drawing i Platform: |
Size: 227328 |
Author:netangels |
Hits:
Description: 这是一篇基于局部不变映射的特征描述器算法文章，文章详细介绍了算法原理及应用，对图像图形处理专业人员的重要参考价值！-This is a description of the algorithms based on local invariant map features articles, the article describes in detail the algorithm and applications, an important reference value for the image graphics professionals! Platform: |
Size: 2595840 |
Author:fujeffson |
Hits:
Description: VC++ 给图片加水印效果示例，导入原图片、导入水印图片，可以将两张图叠加到一张图上，相当于加水印效果，不过实现起来里面有很多细节的，具体请请看代码。源程序编译正常，不过加水印功能有些错误，对水印图片要求也比较高，不过目的是为了交流学习，明白了原理就好办多了。-water mark
-VC++ to add a watermark sample pictures, import the original image, import the watermark image, and two maps can be superimposed on a map, which is equivalent to add the watermark, but to implement a lot of details, Please Look at Code. Source code to compile some errors, but add the Watermark feature high watermark image requirements, but the purpose is to exchange learning, understand the principle would be much easier.-Water mark Platform: |
Size: 1082368 |
Author:luissh |
Hits:
Description: 本文主要介绍怎样使用opencv来对图片进行傅里叶变换，其核心函数是opencv自带的dft()。DFT这个技术手段是将空间域映射到频率域中去，在图像处理有着举足轻重的地位。这里我们只是得到其变换的结果并看看贫域图有什么特点。
实验功能：
单击Open Image按钮，手动选择打开一副图片，不管其是否是彩色图片，这里统一将其转换成灰度图像显示，因为需要加快DFT运算速度。
将鼠标移动到图片显示区域，单击图片，这时会显示DFT中间结果图，也就是没有将低频域的点移动到图片的中心而已。显示的图片有文字提示。
继续单击图片，会显示最后重新布局后的DFT结果图，当然也有相应的文字提示。
继续单击图片时，则原图，dft未调整图，dft调整图这3幅图片会进行轮流切换显示。
-This article describes how to use opencv to the Fourier transform of the picture, its core functions is the the opencv comes with the dft (). DFT techniques is mapping the spatial domain to the frequency domain has a pivotal position in the image processing. Here we just get the transformation of the results and see what are the characteristics of poor domain map. Experimental feature: Click on the Open Image button to manually choose to open a picture, regardless of whether it is a color picture here unified convert to grayscale images, because of the need to accelerate the speed of the DFT operation. Move the mouse to the image display area, click the picture, then displays the DFT intermediate results diagram, that is, no low-frequency domain to point to move to the center of the picture. Shown in the picture text prompts. Click the picture, will show that the DFT results after the last re-layout diagram, of course, there is a corresponding text prompts. Continue to click the pictur Platform: |
Size: 931840 |
Author:wuwei |
Hits:
Description: 改写过的一个简单的daisy特征提取程序，依赖opencv2.3库，可以提取daisy特征向量，并通过特征匹配求得两幅图像的视差图-Rewritten a simple daisy feature extraction program, dependent opencv2.3 library, can be extracted daisy feature vectors, and by the feature match the disparity map of the image of the obtained two Platform: |
Size: 7907328 |
Author:陈铭 |
Hits:
Description: 本系统利用图片的surf特征进行匹配，KD-tree存储特征数据节点，实现相似图片的检索。实现类似于百度的以图搜图功能，结果返回最相似的前十名的图片。-The system uses a picture of the surf feature match, KD-tree storage feature data node, similar image retrieval. Similar to Baidu to search map function returns the results of the top ten most similar images. Platform: |
Size: 10711040 |
Author:luohao |
Hits:
Description: 指纹图像增强、求方向图、二值化、细化、特征提取、特征匹配等算法
的c语言源程序，还有实例演示，本人千辛万苦才找来的，
提供给大家分享。
-Fingerprint image enhancement, seeking direction map, binarization, thinning, feature extraction, feature matching algorithm c language source code, as well as examples of presentation, I just got a lot of hard work, to share with you. Platform: |
Size: 43008 |
Author:cool-wlb |
Hits:
Description: The basic steps of the edge-based text extraction algorithm are given below
1. Create a Gaussian pyramid by convolving the input image with a Gaussian kernel
and successively down-sample each direction by half. (Levels: 4)
2. Create directional kernels to detect edges at 0, 45, 90 and 135 orientations.
3. Convolve each image in the Gaussian pyramid with each orientation filter.
4. Combine the results of step 3 to create the Feature Map.
5. Dilate the resultant image using a sufficiently large structuring element (7x7 [1])
to cluster candidate text regions together.
6. Create final output image with text in white pixels against a plain black
background. Platform: |
Size: 2048 |
Author:Lee Kurian |
Hits:
Description: SIFT匹配（Scale-invariant feature transform，尺度不变特征转换)是一种电脑视觉的算法用来侦测与描述影像中的局部性特征，它在空间尺度中寻找极值点，并提取出其位置、尺度、旋转不变量，其应用范围包含物体辨识、机器人地图感知与导航、影像缝合、3D模型建立、手势辨识、影像追踪和动作比对。-Matching SIFT (Scale invariant feature transform, Scale invariant feature transform) is a computer vision algorithm is used to detect and describe the local characteristics of image, it seek extreme value point in the space Scale, and extract its location, Scale and rotation invariant, its application areas include perception and object recognition, robot map navigation, image stitching, 3 d modeling, gesture recognition, image tracking and action.
Platform: |
Size: 10620928 |
Author:陈天华 |
Hits:
Description: Zhang等人
利用相位一致性信息的特性，提出了
特征相似性方法(FSIMG) ，选取了相位一致性信息和梯度信息
作为它的两个特征，得到了较好的结果。
-A novel feature similarity
(FSIM) index for full reference IQA is proposed based on the fact that
human visual system (HVS) understands an image mainly according to
its low-level features. Specifically, the phase congruency (PC), which is a
dimensionless measure of the significance of a local structure, is used as the
primary feature in FSIM. Considering that PC is contrast invariant while
the contrast information does affect HVS’ perception of image quality, the
image gradient magnitude (GM) is employed as the secondary feature in
FSIM. PC and GM play complementary roles in characterizing the image
local quality. After obtaining the local quality map, we use PC again as a
weighting function to derive a single quality score. Extensive experiments
performed on six benchmark IQA s demonstrate that FSIM can
achieve much higher consistency with the subjective uations than
state-of-the-art IQA metrics. Platform: |
Size: 6144 |
Author:zhangwang |
Hits:
Description: Read positive and negative training sample image files specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector
Dry-run the newly trained custom HOG descriptor against training set and against camera images, if available-Read positive and negative training sample image files specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector
Dry-run the newly trained custom HOG descriptor against training set and against camera images, if available Platform: |
Size: 297984 |
Author:Walker |
Hits:
Description: 将每一张图的特征点采样聚类成图片的视觉单词 即视觉单词,就是对应图片的代表 创建数据库,将每张图片的视觉单词入库,并建立索引-Will feature a map of each sampling point clustered into visual images of words that is visual words, is to represent the corresponding picture of the is created, the visual image of each word storage and indexing Platform: |
Size: 22851584 |
Author:耿文浩 |
Hits:
Description: 最大似然（ML）准则和最大后验概率（MAP）准则，粒子图像分割及匹配均为自行编制的子例程，通过matlab代码，车牌识别定位程序的部分功能，包含了阵列信号处理的常见算法，是学习PCA特征提取的很好的学习资料。- Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Particle image segmentation and matching subroutines themselves are prepared, By matlab code, Part of the license plate recognition locator feature, Contains a common array signal processing algorithm, Is a good learning materials to learn PCA feature extraction. Platform: |
Size: 6144 |
Author:vcryc |
Hits:
Description: 提出一种利用视觉显著性对图像进行分割的方法。首先提取图像的底层视觉特征，从局部显著性、全局显著性和稀少性3个方面计算各特征图像中各像素的视觉显著性，得到各特征显著图；对各特征显著图进行综合，生
成最终的综合显著图。然后对综合显著图进行阈值分割，得到二值图像，将二值图像与原始图像叠加，将前景和背景分离，得到图像分割结果-It presents a significant advantage of visual image segmentation method. First, extract the underlying visual features of images, the local significant global significance and rarity three terms calculated for each feature image each pixel visual significance to give each feature saliency map for each feature saliency map integrated to generate the final integrated saliency map. Then the integrated saliency map threshold segmentation, binary image, binary image with the original image overlay, foreground and background separation, image segmentation results obtained Platform: |
Size: 340992 |
Author:宋晶 |
Hits:
Search - feature map of a image