Description: CStatic-derived histogram control.
The CAlexfStaticHistogram control is a lightweight class suitable for displaying histograms. You can add new columns, additional text, change text orientation and direction of motion (if you change data at runtime). -CStatic-derived histogram control. The CAlexfStaticHistogram control is a lightweight class suitable for displaying histograms. You can add new columns, additional text, change text orientation and direction of motion (if you change data at runtime). Platform: |
Size: 2967 |
Author:Tom |
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Description: CStatic-derived histogram control.
The CAlexfStaticHistogram control is a lightweight class suitable for displaying histograms. You can add new columns, additional text, change text orientation and direction of motion (if you change data at runtime). -CStatic-derived histogram control. The CAlexfStaticHistogram control is a lightweight class suitable for displaying histograms. You can add new columns, additional text, change text orientation and direction of motion (if you change data at runtime). Platform: |
Size: 3072 |
Author: |
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Description: 使用boosting,edge orientation histogram和cascade算法实现face detection的文章。解决了使用小数据库训练系统和区分肖像画的问题。-The use of boosting, edge orientation histogram and the cascade algorithm face detection article. Solution using a small database, training systems and the distinction between portraiture issues. Platform: |
Size: 521216 |
Author:刘洋 |
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Description: 提出了一种有效用于抽取特征、索引和检索彩色图像的技术途径. 通过提取图像的颜色不变量, 建立相应
的色度直方图(hue h istogram) 来表示图像的颜色分布特征. 为了描述图像中对象的位置及方向特征, 首先计算图像的色度轮廓并对其进行Radon 变换, 然后计算相应的“空间直方图”.-Proposes an effective feature for the extraction, indexing and retrieval of color images of the technical ways. By extracting the image color is not a variable, set up a corresponding color histogram (hue h istogram) to represent the color distribution of the image. In order to describe the image the location and orientation of objects characteristic of the first calculation of the image color profile and gain Radon transform, and then calculates the corresponding " space histogram." Platform: |
Size: 621568 |
Author:史习云 |
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Description: Opencv HOG (Histogram of Orientation Gradient) training based on Dalal method.
When you try to use Opencv HOG, you may not know how the training of the SVM model is done. Here is the example
Linux with Opencv2.1 Platform: |
Size: 18151424 |
Author:szproxy |
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Description: 本文提出一种用于手势识别的新方法，它将图像的方向直方图矢量(0Hv)与神经网络相结合。其特点在于选用
图像的方向直方图矢量作为手势的特征矢量，该特征矢量对于光线和手的平移变化具有较强的鲁棒性，这正是手势识别所要
解决的关键问题。在训练阶段，首先需要建立手势样本的特征矢量库；在识别阶段，本文选用三层BP网络作为分类器，获得
了90％以上的识别率。本文还对手势进行一定角度的旋转后的识别进行了讨论，识别结果达到预期要求。-In this paper，a new method of gesture recognition is proposed，which combines orientation histogram vector(OHV)
and neural net(NN)．Its high light is that OHV is selected as the feature vector of gestures，which is robust to illumination and
translation and these two problems are very important in gesture recognition．In the training stage，a database of feature vectors is set
up；In the recognition stage，a three layer BP neural net is selected tO classify gestures and the recognition rate is above 90％．The
recognition of gestures after turning some angle is also discussed in this paper，the result is satisfying． Platform: |
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Author:Decheng Yu |
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Description: IHOG, compute a 3D matrix of orientation histogram, storing a integral histogram for each pixel in the image Platform: |
Size: 46080 |
Author:caelyn |
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Description: Histogram of Oriented Gradients (HOG) are feature descriptors used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy. Platform: |
Size: 1024 |
Author:Mohammad |
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Description: 图像梯度方向直方图（HOG)特征基础上云模型运动目标检测算法,提出HOG特征为基础的均值漂移算法
-Moving target detection algorithm of image gradient orientation histogram (HOG) features based on cloud model proposed HOG feature based on mean shift algorithm Platform: |
Size: 20087808 |
Author:QQ |
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Description: 本文提出一种通过实时调整目标特征权值来进行背景自适应跟踪的算法。首先，定义了一种综合特征集合用以描述目标的颜色和局部轮廓。其次，提出了在滤波框架中对目标特征进行评估的算法，从而使得具有强区分能力的特征占有较大的权值，进而使其能够在跟踪过程起到较大的作用。采用传统的Kalman 滤波和粒子滤波对所提出的算法进行了验证。-In this paper, we propose a new adaptive visual object tracking method based on
online feature evaluation approach. First, a feature set is built by combining color
histogram (HC) with gradient orientation histogram (HOG), which emphasizes both
color and contour representation. Then a feature confidence evaluation approach is
proposed to make features with higher confidences play more important roles in the
instantaneous tracking ensuring that the tracking can adapt to the appearance change
of both the object and its background. The feature evaluation approach is fused with
filter frameworks, e.g. Kalman and Particle filter, to keep the temporal consistency of feature confidence evolution. Platform: |
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Author:wenping |
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Description: 本文提出了一种复杂条件下基于子空间梯度方向直方图跟踪的方法，通过大量样本的离线训练构建目标的投影子
空间，并用梯度方向直方图在子空间的投影作为新的目标描述特征．为了满足实时性的要求，采用积分直方图方法
提高粒子特征的计算速度；然后结合粒子滤波方法在子空间中计箅粒子与训练样本集之间的相似度，进而估计目标
的运动参数．实验结果表明，该方法能够在光照变化、噪声干扰、模糊、目标姿态和尺度改变，以及部分遮捎等恶劣条
件下实现准确跟踪，比传统的跟踪方法具有更高的跟踪精度和跟踪鲁棒性，能够满足地面侦察任务在多种复杂条件下对感兴趣目标进行准确跟踪的需求．-This paper presents a complex condition gradient orientation histogram based on subspace tracking methods, through a large number of samples offline training build target subspace projection and gradient orientation histogram in subspace projection as a new target descriptive characteristics. In order to meet the requirements of real-time, using integral histogram method improves the computing speed of the particle characteristics then combined with particle filter in subspace meter grate particles and the similarity between the training sample set, and then estimate the target motion parameters. Experimental results show that this method can change in the light, noise, blur, target attitude and scale changes, and take along some cover in bad condition for accurate tracking, than the traditional tracking method has higher tracking accuracy and robustness to meet the ground reconnaissance missions in a variety of complex conditions accurately track targets of interest requirements. Platform: |
Size: 555008 |
Author:wenping |
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Description: 方向梯度直方图（Histogram of Oriented Gradient, HOG）特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。Hog特征结合SVM分类器已经被广泛应用于图像识别中，尤其在行人检测中获得了极大的成功。需要提醒的是，HOG+SVM进行行人检测的方法是法国研究人员Dalal在2005的CVPR上提出的，而如今虽然有很多行人检测算法不断提出，但基本都是以HOG+SVM的思路为主。
-Histogram of Oriented Gradients (Histogram of Oriented Gradient, HOG) feature is a kind of computer vision and image processing used for object detection feature descriptor. It does this by calculating the image and statistical local areas to form a gradient orientation histogram features. Hog features combined with SVM classifier has been widely used in image recognition, especially in pedestrian detection was a great success. Need to be reminded, HOG+SVM for pedestrian detection method is to French researchers Dalal CVPR in 2005 raised, and now although there are many pedestrian detection algorithm continuously put forward, but the basic idea is to HOG+SVM based. Platform: |
Size: 44032 |
Author:王萌 |
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Description: 计算局部形状描述符梯度方向直方图（HOG），直接输入图像即可用。-Calculate the local shape descriptors gradient orientation histogram (HOG), the input image can be used directly. Platform: |
Size: 2048 |
Author:潘仁龙 |
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Description: 分析了灰度图像的脂质体，并产生不同的大小和形状参数。-This function analyses grayscale .tif images of liposomes (or any round shaped particles).
This function applies to the image:
1. Histogram Equalization.
2. Low Pass Filter (variable size, default size 12).
3. Illumination Equalization (default size 120).
4. Thresholding (user input).
5. Removing Edge Particles (user input).
6. Removing Edge Particles (by size, user input).
7. High Pass Filter (default size 3).
8. Size Analysis. Default: Orientation , MinorAxisLength , MajorAxisLength , Centroid , Area .
9. Final artifact removal (user input).
The output is a .txt file with a table containing all the parameters.
An example is provided with the code. Platform: |
Size: 818176 |
Author:格 |
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Search - orientation histogram