当前位置：
首页 资源下载
搜索资源 - Histogram of Oriented Gradients
搜索资源列表
-
4下载：
该程序分别提取正负样本图像的HOG和LBP特征，利用支持向量机进行样本训练，得到行人分类器。利用训练好的分类器进行检测，实验结果表明，该方法可以有效检测出图像中的行人，并达到了较好的检测结果。-A novel approach based on combining Histogram of oriented gradients (HOG) and LocalBinary Pattern(LBP) is suggested in the program.Also liner SVM is acte
-
-
0下载：
本文运用尺度空间理论检测人体，通过集成
面向梯度与histogramof尺度空间理论
-Human detection is the task of finding presence and position
of human beings in images. In this paper, we apply
scale space theory to detecting human in still images. By integrating
scale space
-
-
0下载：
HOG calculator, takes filename as input and calculates Histogram of oriented gradients from the input image.
-
-
0下载：
Histogram of oriented gradients 作者源码。 -Original Sourcecode of Histogram of Oriented Gradients
-
-
0下载：
Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application
domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of
-
-
0下载：
This paper presents a method that combines
colour and motion information to track pedestrians in video sequences captured by a fixed camera. Pedestrians are firstly
detected using the human detector proposed by Dalal and Triggs which involves com
-
-
0下载：
提取图像方向梯度的直方图特征，image processing（图像处理）-HOG feature extraction
Descr iption
This program is used to extract HOG(Histograms of oriented gradients) features from images. The integral histogram is used for fast histogram extraction. Both APIs and b
-
-
0下载：
为了准确地对监控场景中的运动目标进行语义上的分类， 提出了一种基于聚类的核主成分分析梯度方向直方图和二叉决策树支持向量机的运动目标分类算法．利用背景减法提取运动目标前景区域， 并识别出潜在候选运动目标．利
用提出的基于聚类的核主成分分析的梯度直方图描述子提取候选运动目标的特征， 以较低维数的数据有效地描述运动目标的有效特征． 将提取的运动目标特征输入二叉决策树支持向量机， 实现多类目标的准确分类． 通过在不同视频序列上的实验验证， 提出的算法对运动目标进行较好地分类， 而且在运算速度方面较传
-
-
0下载：
Histogram of Oriented Gradients (HOG) are feature descr iptors 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 i
-
-
0下载：
方向梯度直方图的全部代码，广泛应用于行人检测行人检测-Histogram of Oriented Gradients code Pedestrian Detection
-
-
0下载：
图像梯度方向直方图描述子:，该方法使用梯度方向直方图（Histogram of Oriented Gradients,简称HOG）
特征来表达人体，提取人体的外形信息和运动信息，形成丰富的特征集，然后使
用支持向量机线性SVM 分类器对这些特征集进行训练。-Image gradient orientation histogram descr iptor
-
-
0下载：
HOG（方向梯度直方图）图像特征提取，以lena图像作为示例-HOG (Histogram of Oriented Gradients) image feature extraction, image as an example to lena
-
-
0下载：
The code below is implementation of HOG (Histogram-of-Oriented Gradients) descr iptor and object detection, introduced by Navneet Dalal and Bill Triggs.
The computed feature vectors are compatible with the INRIA Object Detection and Localization
-
-
0下载：
efficient LBP Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found
-
-
0下载：
方向梯度直方图（Histogram of Oriented Gradient, HOG）特征,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的实现-Histogram of oriented gradients (Histogram of Oriented Gradient, HOG) characteristics, computer vision and image processing used for object detection feature descr iptor real
-
-
0下载：
histogram of oriented gradients for leaf identification
-
-
0下载：
HISTOGRAM OF ORIENTED GRADIENTS
-
-
6下载：
读取一24位彩色图像，计算其以下图像特征：
1）灰度直方图
2）颜色直方图（RGB3个通道）
3）方向梯度直方图（HOG，将图像分成4*4 16个块，8个梯度方向）
-Read a 24-bit color images, calculate the following image features: 1) Histogram 2) color histogram (RGB3 channels) 3) histogram of oriented gradients (HOG, t
-
-
0下载：
Histogram of Oriented Gridients(HOG) 方向梯度直方图，具备几何和光学转化不变性。常用于行人检测中-Histogram of Oriented Gridients (HOG) histogram of oriented gradients, with geometric and optical transformation invariance. Commonly used in pedestrian detection
-
-
1下载：
在这个项目中，我们的目标是建立一个识别和大小231x231图像呈现对象分类系统。我们得到了一组训练图像，每四个标签之一：1飞机；汽车2；3马，否则。我们提供了两个特点：一是方向梯度直方图（HOG），其尺寸为5408；另一个是overfeat ImageNet美国有线电视新闻网的特点，其尺寸37000。关于测试图像，我们只给出了每个图像的功能，没有标签，结果判断由平地机。我们的目标是提供二进制和多个预测。平衡错误率（BER）是我们的性能评估。为了解决这个问题，我们首先减少PCA的问题的维数，处理不
-
