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原创有开源代码的文献

目标检测开源代码汇总跟踪算法开源代码汇总人脸检测识别代码汇总人群分析、人群计数开源代码文献及数据库语义分割+视频分割开源代码文献集合网络优化加速开源代码汇总计算机视觉&amp;amp;amp;amp;amp;amp;amp;amp;amp;深度学习相关资源汇总 https://joshua19881228.github.io/2016-08-25-my-jumble-of-computer-vision/https://git.

2016-11-21 16:52:55 8057

原创 Fundamental concepts about Optics

Book: Optics F2f From Fourier to FresnelA particularly useful solution of the wave equation is a wave with a particular wavelength λ. This is known as the harmonic wave solution and corresponds to the case of monochromatic lightA phasor is a unit vector i

2023-08-28 09:27:02 251

原创 Fundamental concepts about Lithography

如果我们希望将 aperture 的pattern 在 image plane(reisit) 上成像，我们可以用 Collimating Lens 光照 pattern, 再用 Focusing Lens 收集光成像。Fraunhofer diffraction - far field. 弗朗霍菲衍射。Fresnel diffraction - near field. 菲涅耳衍射。

2023-08-26 16:32:45 340

原创 basics of level set

level set basics

2022-11-05 17:43:26 451

原创 Image Segmentation Based on Active Contours without Edges

Nonlinear Anisotropic DiffusionActive Contours without Edges

2022-10-30 11:42:31 309

原创 Segmentation under geometrical conditions using geodesic active contours and interpolation

Segmentation under geometrical conditions using geodesic active contours and interpolation using level set methodsThe level set approachUsing Euler–Lagrange theoremWe now propose an alternative proof (using Gâteaux derivative) of the evolution equatio

2022-10-29 17:49:17 491

原创 Geodesic Active Contours

2.1 Energy Based Active ContoursThe classical snakes approach (Kass et al., 1988) associates the curve C with an energy given byTherefore, curve smoothing will be obtained also with β = 0, having only the first regularization termcurve evolution equat

2022-10-29 17:41:53 529

原创 Lithography tutor

So how does this optical path difference affect the formation of an image? For light, the path length traveled is equivalent to a change in phase. Thus, the OPD can be expressed as a phase error,Our interpretation of defocus is that it causes a phase error

2022-10-27 13:13:41 223

原创 fundamental of Level set method

Ref: Level Set Methods and Dynamic Implicit Surfaces

2022-10-15 15:28:40 242

原创 Anisotropic Diffusion in Image Processing 2

Parameter Adaptation for Nonlinear Diffusion in Image Processingdiscrete approximation

2022-10-05 15:55:42 238

原创 Anisotropic Diffusion in Image Processing (1)

Adaptive Smoothing- A General Tool for Early Visionfor linear heat diffusion equation:for anisotropic diffusion:for 1D adaptive smoothing iteration equation:

2022-10-05 15:44:46 274

原创 MKL sparse QR solver for least square

COO to CSR format#include <vector>#include <iostream>#include <mkl.h>#ifdef __linux__#include <stdlib.h> //for aligned alloc!#include <cstring> //believe it or not for memcpy!!#endif#include "mkl_sparse_qr.h"// -----

2022-03-18 10:02:24 788

转载 1D and 2D Gaussian Derivatives

http://campar.in.tum.de/Chair/HaukeHeibelGaussianDerivativesThe Two-Dimensional CaseBase Function (0th order)Computes discrete 1D gaussian functionsfunction [ gaussian ] = gaussian( x, sigma, order, normalize ) if isempty(normalize)

2021-11-11 09:19:42 931

原创 MobileNet Unet

https://github.com/bubbliiiing/Semantic-Segmentationhttps://github.com/BBuf/Keras-Semantic-Segmentationhttps://github.com/bubbliiiing/Semantic-Segmentation

2021-05-18 11:09:00 4917

原创 U-NET 图像预处理

首先将图像格式及大小、类型、名称做出调整这里将 bmp 转为 png 大小统一为 500*500，按照数字序号命名bmp_png.pyfrom PIL import Imageimport globimport osout_dir = 'D:/图像数据/橙子/TestIMG/'cnt = 501for img in glob.glob('D:/图像数据/橙子/测试图像/*.bmp'): Image.open(img).resize((500,500)).save(os.

2021-05-07 09:45:36 692

原创一致性直线提取--Coherent Line Drawing

Coherent Line DrawingProc. NPAR 2007https://github.com/uva-graphics/coherent_line_drawinghttps://github.com/SSARCandy/Coherent-Line-Drawinghttps://ssarcandy.tw/2017/06/26/Coherent-Line-Drawing/所谓的 Line drawing 就是直线素描，在这里的意境就是：输入一幅图像，输出一副直线艺术风格画本文主要是

2021-03-15 14:44:07 1754

原创 Kullback-Leibler Divergence

http://alpopkes.com/files/kl_divergence.pdfKullback-Leibler 散度定义： Kullback-Leibler 散度用于度量两个分布的相似性（或差异）。对于两个离散概率分布 P 和 Q ，在一个点集合 X 上 Kullback-Leibler 散度定义如下：DKL(P∣∣Q)=∑x∈XP(x)log(P(x)Q(x)) D_{KL}(P||Q)=\sum_{x\in X}^{}P(x)log(\frac{P(x)}{Q(x)} ) DKL(

2021-01-08 15:01:53 695

转载 Bias Variance Tradeoff – Clearly Explained

Bias Variance Tradeoff is a design consideration when training the machine learning model. Certain algorithms inherently have a high bias and low variance and vice-versa. In this one, the concept of bias-variance tradeoff is clearly explained so you make a

2021-01-08 11:10:30 378

转载 What is Mahalanobis distance? 马氏距离

https://blogs.sas.com/content/iml/2012/02/15/what-is-mahalanobis-distance.htmlhttps://blogs.sas.com/content/iml/2012/02/08/.htmlA variance-covariance matrix expresses linear relationships between variables. Given the covariances between variables, did yo

2021-01-08 10:39:53 422

转载梯度下降原理解析

1 原理在机器学习的核心内容就是把数据喂给一个人工设计的模型，然后让模型自动的“学习”，从而优化模型自身的各种参数，最终使得在某一组参数下该模型能够最佳的匹配该学习任务。那么这个“学习”的过程就是机器学习算法的关键。梯度下降法就是实现该“学习”过程的一种最常见的方式，尤其是在深度学习(神经网络)模型中，BP反向传播方法的核心就是对每层的权重参数不断使用梯度下降来进行优化。梯度下降法(gradient descent)是一种常用的一阶(first-order)优化方法，是求解无约束优化问题最简单、最经典的方法

2020-08-05 11:44:25 484

原创加法神经网络--AdderNet: DoWe Really Need Multiplications in Deep Learning?

AdderNet: DoWe Really Need Multiplications in Deep Learning?CVPR2020https://arxiv.org/abs/1912.13200当前主流的CNN网络使用了大量的乘法运算来计算输入特征层和卷积滤波器的相似性（cross-correlation），由于乘法运算耗时明显大于加法运算耗时，所有本文提出一个加法神经网络，使用 l1 范数来计算输入特征层和卷积滤波器的相似性。这样在计算滤波器的输出响应时基本不用乘法运算。针对该加法神经网络

2020-06-11 15:15:59 1150 1

原创图像主轴相关知识

二值图像中物体几何主轴的提取方法https://www.docin.com/p-764752910.html主轴的定义：1)从投影的角度来说，沿着主轴方向做投影，物体所得到的宽度最小；2)从统计学的角度来说，主轴的方向就是该物体的主分量的方向，以该主分量为基础做线性变换可以去掉随机向量中各元素间的相关性；3)从纹理分析和频谱分析的角度来说，对规则的狭长型物体，主轴方向就是垂直于频谱图上能...

2019-10-29 15:19:50 1791

转载 opencv 凹凸性检测和缺陷分析

2019-10-29 15:17:39 5016

转载 c++ 类文件的动态库生成及调用例子

https://blog.csdn.net/josiechen/article/details/70174445 ...

2019-07-01 16:45:44 1467

原创多尺度目标检测--Scale-Aware Trident Networks for Object Detection

Scale-Aware Trident Networks for Object Detectionhttps://github.com/TuSimple/simpledet/tree/master/models/tridentnet本文将 Dilated convolution 用于多尺度目标检测，Dilated convolution 最先用于语义分割。多尺度目标检测的几个常见策略fe...

2019-06-21 11:48:20 813

原创快速目标检测--YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computershttps://github.com/reu2018DL/YOLO-LITEhttps://github.com/Stinky-Tofu/Stronger-yoloYOLO-LITE runs at about 21 FPS on ...

2019-06-20 14:40:25 1359

原创快速目标检测--Object detection at 200 Frames Per Second

Object detection at 200 Frames Per Second本文在 Tiny Yolo 的基础上设计了一个目标检测网络，在 Nvidia 1080ti 上可以达到 100帧每秒。本文主要成果有三点：1）网络结构上的设计改进；2） Distillation loss for Training，使用 teacher network 辅助训练；3）Effectiveness...

2019-06-14 16:46:20 1258

转载一文弄懂神经网络中的反向传播法——BackPropagation

https://www.cnblogs.com/charlotte77/p/5629865.html　　最近在看深度学习的东西，一开始看的吴恩达的UFLDL教程，有中文版就直接看了，后来发现有些地方总是不是很明确，又去看英文版，然后又找了些资料看，才发现，中文版的译者在翻译的时候会对省略的公式推导过程进行补充，但是补充的又是错的，难怪觉得有问题。反向传播法其实是神经网络的基础了，但是很多人在学的...

2019-06-12 11:43:14 1508

转载卷积网络基础知识---Group Convolution分组卷积

Group Convolution分组卷积，以及Depthwise Convolution和Global Depthwise Convolutionhttps://www.cnblogs.com/shine-lee/p/10243114.html写在前面Group Convolution分组卷积，最早见于AlexNet——2012年Imagenet的冠军方法，Group Convolutio...

2019-06-04 09:38:20 9817 3

转载卷积网络基础知识---Depthwise Convolution && Pointwise Convolution && Separable Convolution

https://yinguobing.com/separable-convolution/#fn2 卷积神经网络在图像处理中的地位已然毋庸置疑。卷积运算具备强大的特征提取能力、相比全连接又消耗更少的参数，应用在图像这样的二维结构数据中有着先天优势。然而受限于目前移动端设备硬件条件，显著降低神经网络的运算量依旧是网络结构优化的目标之一。本文所述的Separable Convolution就是降低...

2019-06-04 08:58:07 1754

转载机器学习--多标签softmax + cross-entropy交叉熵损失函数详解及反向传播中的梯度求导

https://blog.csdn.net/oBrightLamp/article/details/84069835正文在大多数教程中, softmax 和 cross-entropy 总是一起出现, 求梯度的时候也是一起考虑.softmax 和 cross-entropy 的梯度, 已经在上面的两篇文章中分别给出.1 题目考虑一个输入向量 x, 经 softmax 函数归一化处理后...

2019-06-03 16:48:10 2634

原创目标检测---Segmentation Is All You Need

Segmentation Is All You Needhttps://www.jiqizhixin.com/articles/2019-06-02-2目前目标检测算法中有两个模块比较重要： region proposal networks (RPNs) 和 non-maximum suppression (NMS) ，虽然这两个模块解决目标检测中的一些问题，但是它们也引入了一些难以克服的问...

2019-06-03 13:29:09 2045

转载脚崴了！又肿又疼怎么办？

罗大伦频道https://mp.weixin.qq.com/s?__biz=MzI1MjAyNDMwNw==&mid=2650718754&idx=1&sn=832f192593ae9ad9ca0e3d304997f093&chksm=f1e066bec697efa8ed439b987466ea73aa316d14ff90c92fbf04d33e23452bb30...

2019-05-31 08:37:29 899 1

转载 openGL 入门4 --- Following the data

Example 1.2. Buffer Object Initializationvoid InitializeVertexBuffer(){ glGenBuffers(1, &positionBufferObject); // 生成缓存对象，没有分配内存 glBindBuffer(GL_ARRAY_BUFFER, positionBufferObject); // 绑定对象 g...

2019-05-30 15:56:50 258

转载 OpenGL ---渲染流水线之世界矩阵，相机变换矩阵，透视投影变换矩阵

https://blog.csdn.net/qq_29523119/article/details/78577246OpenGL的渲染流水线:OpenGL的坐标系在3D图形学里,OpenGL为右手坐标系(准确来说，OpenGL的世界空间和相机空间是右手坐标系)。随便提一下，D3D11为左手坐标系。(1) 右手坐标系(2) 左手坐标系OpenGL的矩阵和向量结合方式...

2019-05-30 15:51:57 1197 2

转载 OpenGL--- 坐标系变换

下面这篇文章详细讲述了OpenGL里的坐标转换，清晰，明了。但是其所谓的渲染管线只包括modelview 转换和投影变换，我觉得不是这样的。这只是从坐标角度吧。比如什么顶点着色、光栅化、送至帧缓存都没有涉及到。原文地址：http://blog.csdn.net/zhulin...

2019-05-30 15:39:37 1026

转载 openGL--透视投影的原理和实现

https://blog.csdn.net/wong_judy/article/details/6283019#t2 透视投影的原理和实现by Goncely 摘要：透视投影是3D渲染的基本概念，也是3D程序设...

2019-05-30 14:21:54 4444

转载 OpenGL坐标系及坐标转换

https://blog.csdn.net/shimazhuge/article/details/25135009 OpenGL通过相机模拟、可以实现计算机图形学中最基本的三维变换，即几何变换(模型变换—视图变换（两者合称几何变换）)、投影变换、裁剪变换、视口变换等，同时，OpenGL还实现了矩阵堆栈等。理解掌握了有关坐标变换的内容，就算真正走进了精彩地三维世界。...

2019-05-30 11:33:05 1144 1

原创 openGL入门3 --- rasterization pipeline

Learning Modern 3D Graphics ProgrammingRasterization Overview这里简单介绍一下 rasterization 光栅化流程1）裁剪空间变换，归一化坐标系 transform the vertices of each triangle into normalized device coordinates2）窗口变换 from norm...

2019-05-29 08:52:54 963

转载中医点滴 2 --- 保和丸 + 口气重

口气重的两大原因https://mp.weixin.qq.com/s?__biz=MzI1MjAyNDMwNw==&mid=2650718736&idx=1&sn=c9d90360b73a7d9d365688102ad8d14d&chksm=f1e0668cc697ef9af47b112d1adc90d43ee0bc779b34189d787a78f6069864...

2019-05-29 08:36:34 1375

Accuracy of Laplacian Edge Detectors

The sources of error for the edge finding technique proposed by Marr and Hildreth (D. Marr and T. Poggio, Proc. R. Soc. London Ser. B204, 1979, 301–328; D. Marr and E. Hildreth, Proc. R. Soc. London Ser. B.207, 1980, 187–217) are identified, and the magnitudes of the errors are estimated, based on idealized models of the most common error producing situations. Errors are shown to be small for linear illuminations, as well as for nonlinear illuminations with a second derivative less than a critical value. Nonlinear illuminations are shown to lead to spurious contours under some conditions, and some fast techniques for discarding such contours are suggested.

2011-10-12

The Canny Edge Detector Revisited

Canny (1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny’s work suffers from two problems. First, his derivation of localization criterion is incorrect. Here we provide a more acurate localization criterion and derive the optimal detector from it. Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector. The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image. If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (1992). In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian. This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image. We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions.

2011-08-11

OpenCV 2 Computer Vision Application Programming Cookbook

Overview of OpenCV 2 Computer Vision Application Programming Cookbook Teaches you how to program computer vision applications in C++ using the different features of the OpenCV library Demonstrates the important structures and functions of OpenCV in detail with complete working examples Describes fundamental concepts in computer vision and image processing Gives you advice and tips to create more effective object-oriented computer vision programs Contains examples with source code and shows results obtained on real images with detailed explanations and the required screenshots

2011-06-24

Learning based Symmetric Features Selection for Vehicle Detection

Learning based Symmetric Features Selection for Vehicle Detection This paper describes a symmetric features selection strategy based on statistical learning method for detecting vehicles with a single moving camera for autonomous driving. Symmetry is a good class of feature for vehicle detection, but the areas with high symmetry and threshold for segmentation is hard to be decided. Usually, the additional supposition is added artificially, and this will decrease the robustness of algorithms. In this paper, we focus on the problem of symmetric features selection using learning method for autonomous driving environment. Global symmetry and local symmetry are defined and used to construct a cascaded structure with a one-class classifier followed by a two-class classifier.

2011-04-11

Intensity and Edge-Based Symmetry Detection Applied to Car-Following

Intensity and Edge-Based Symmetry Detection Applied to Car-Following We present two methods for detecting symmetry in images, one based directly on the intensity values and another one based on a discrete representation of local orientation. A symmetry finder has been developed which uses the intensity-based method to search an image for compact regions which display some degree of mirror symmetry due to intensity similarities across a straight axis. In a different approach, we look at symmetry as a bilateral relationship between local orientations. A symmetryenhancing edge detector is presented which indicates edges dependent on the orientations at two different image positions. SEED, as we call it, is a detector element implemented by a feedforward network that holds the symmetry conditions. We use SEED to find the contours of symmetric objects of which we know the axis of symmetry from the intensity-based symmetry finder. The methods presented have been applied to the problem of visually guided car-following. Real-time experiments with a system for automatic headway control on motorways have been successful.

2011-04-11

Accurate Robust Symmetry Estimation

Accurate Robust Symmetry Estimation Stephen Smith and Mark Jenkinson There are various applications, both in medical and non-medical image analysis, which require the automatic detection of the line (2D images) or plane (3D) of reflective symmetry of objects. There exist relatively simple methods of finding reflective symmetry when object images are complete (i.e., completely symmetric and perfectly segmented from image “background”). A much harder problem is finding the line or plane of symmetry when the object of interest contains asymmetries, and may not have well defined edges.

2011-04-11

Approach of vehicle segmentation based on texture character

2011-04-01

Method of removing moving shadow based on texture

2011-04-01

Environmentally Robust Motion Detection for Video Surveillance

Most video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system.

2011-03-17

Optimal multi-level thresholding using a two-stage Otsu optimization approach

Otsu’s method of image segmentation selects an optimum threshold by maximizing the between-class variance in a gray image. However, this method becomes very time-consuming when extended to a multi-level threshold problem due to the fact that a large number of iterations are required for computing the cumulative probability and the mean of a class. To greatly improve the efficiency of Otsu’s method, a new fast algorithm called the TSMO method (Two-Stage Multithreshold Otsu method) is presented. The TSMO method outperforms Otsu’s method by greatly reducing the iterations required for computing the between-class variance in an image. The experimental results show that the computational time increases exponentially for the conventional Otsu method with an average ratio of about 76. For TSMO-32, the maximum computational time is only 0.463 s when the class number M increases from two to six with relative errors of less than 1% when compared to Otsu’s method. The ratio of computational time of Otsu’s method to TSMO-32 is rather high, up to 109,708, when six classes (M = 6) in an image are used. This result indicates that the proposed method is far more efficient with an accuracy equivalent to Otsu’s method. It also has the advantage of having a small variance in runtimes for different test images.

2011-03-17

A Background Reconstruction Method Based on Double-background

In this paper, we show a new method to reconstruct and update the background. This approach is based on double-background. We use the statistical information of the pixel intensity to construct a background that represents the status during a long time, and construct another background with feedback information in motion detection that represents the recent changes at a short time. This couple of background images is fused to construct and update the background image used to motion detection. The background reconstruction algorithm can perform well on the tests that we have applied it to.

2011-03-17

Statistical Change Detection by the Pool Adjacent Violators Algorithm

In this paper we present a statistical change detection approach aimed at being robust with respect to the main disturbance factors acting in real-world applications, such as illumination changes, camera gain and exposure variations, noise. We rely on modeling the effects of disturbance factors on images as locally order-preserving transformations of pixel intensities plus additive noise. This allows us to identify within the space of all the possible image change patterns the subspace corresponding to disturbance factors effects. Hence, scene changes can be detected by a-contrario testing the hypothesis that the measured pattern is due to disturbance factors, that is by computing a distance between the pattern and the subspace. By assuming additive gaussian noise, the distance can be computed within a maximum likelihood non-parametric isotonic regression framework. In particular, the projection of the pattern onto the subspace is computed by an O(N) iterative procedure known as Pool Adjacent Violators algorithm.

2011-03-17

Cooperative Fusion of Stereo and Motion

Cooperative Fusion of Stereo and Motion This paper presents a new matching algorithm based on cooperative fusion of stereo and motion cues. In this algorithm, stereo disparity and image flow values are recovered from two successive pairs of stereo images by solving the stereo and motion corresponde

2011-03-09

A Treatise on Mathematical Theory of Elasticity (1944)(ISBN 0486601749)

Love, A Treatise on Mathematical Theory of Elasticity (1944)(ISBN 0486601749).djvu 第三部分（共三部分）

2011-02-27

A Treatise on Mathematical Theory of Elasticity (1944)(ISBN 0486601749)

Love, A Treatise on Mathematical Theory of Elasticity (1944)(ISBN 0486601749).djvu 第二部分（共三部分）

2011-02-27

Love, A Treatise on Mathematical Theory of Elasticity (1944)(ISBN 0486601749)

Love, A Treatise on Mathematical Theory of Elasticity (1944)(ISBN 0486601749) 第一部分（共三部分）

2011-02-27

Computation of Real-Time Optical Flow Based on Corner Features

This paper describes an approach to real-time optical flow computation that combines the corner features and pyramid Lucas-Kanade. Corners instead of all the points in the image are taken into optical flow computation, which could reduce the amount of calculation to a large extend. The experiment has shown that using this optical flow algorithm to track targets is effective and could meet the requirements of real-time applications.

2011-02-24

II-LK – A Real-Time Implementation for Sparse Optical Flow

In this paper we present an approach to speed up the computation of sparse optical flow fields by means of integral images and provide implementation details. Proposing a modification of the Lucas-Kanade energy functional allows us to use integral images and thus to speed up the method notably while affecting only slightly the quality of the computed optical flow. The approach is combined with an efficient scanline algorithm to reduce the computation of integral images to those areas where there are features to be tracked. The proposed method can speed up current surveillance algorithms used for scene description and crowd analysis.

2011-02-24

Medical Image Reconstruction A Conceptual Tutorial --pdf

Medical Image Reconstruction: A Conceptual Tutorial" introduces the classical and modern image reconstruction technologies, such as two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. This book presents both analytical and iterative methods of these technologies and their applications in X-ray CT (computed tomography), SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging). Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich's cone-beam filtered backprojection algorithm, and reconstruction with highly undersampled data with l0-minimization are also included.

2011-02-24

Extraction and recognition of license plates of motorcycles and vehicles on highways

2011-02-22

High Performance Implementation of License Plate Recognition in Image Sequences

2011-02-22

Vs-star-- A visual interpretation system for visual surveillance

2011-02-22

Robust fragments-based tracking with adaptive feature selection

2011-02-22

Robust and automated unimodal histogram thresholding and potential applications

2011-02-22

角点检测方法研究-- 毛雁明, 兰美辉

角点检测方法研究---根据实现方法不同可将角点检测方法分为两大类:基于边缘的角点检测方法与基于灰度变化的角点检测方法,并对现有的角点检测方法作了较为详细的分析与比较,指出角点检测技术的研究与发展方向.

2011-02-22

Fast image region growing

Fast image region growing---Fast image region growing

2011-02-22

Simple Low Level Features for Image Analysis

2011-02-22

Extracting Straight Lines

Extracting Straight Lines---line detection edge detection

2011-02-22

Direct methods for sparse matrices

second edition 2017, Oxford University Press

2024-04-07

百面机器学习.pdf

收录了超过100道机器学习算法工程师的面试题目和解答，本书将从特征工程、模型评估、降维等经典机器学习领域出发，构建一个算法工程师必-备的知识体系。其中大部分源于Hulu算法研究岗位的真实场景。

2019-06-01

CLIP-Q CVPR2018 code

CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization，CVPR2018 code

2018-10-30

Vehicle model recognition from frontal view image measurements

This paper deals with a novel vehicle manufacturer and model recognition scheme, which is enhanced by color recognition for more robust results. A probabilistic neural network is assessed as a classiﬁer and it is demonstrated that relatively simple image processing measurements can be used to obtain high performance vehicle authentication. The proposed system is assisted by a previously developed license plate recognition, a symmetry axis detector and an image phase congruency calculation modules. The reported results indicate a high recognition rate and a fast processing time, making the system suitable for real-time applications.

2011-10-15

Vehicle Detection and Tracking in Car Video Based on Motion Model

Vehicle Detection and Tracking in Car Video Based on Motion Model--This work aims at real-time in-car video analysis to detect and track vehicles ahead for safety, auto-driving, and target tracing. This paper describes a comprehensive approach to localize target vehicles in video under various environmental conditions. The extracted geometry features from the video are projected onto a 1D profile continuously and are tracked constantly. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We model the motion in the field of view probabilistically according to the scene characteristic and vehicle motion model. The Hidden Markov Model is used for separating target vehicles from background, and tracking them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination, and that real time processing becomes possible for vehicle borne cameras.

2011-10-15

Projection and Least Square Fitting

Projection and Least Square Fitting with Perpendicular Offsets based Vehicle License Plate Tilt Correction

2011-10-15

An Algorithm for License Plate Recognition Applied to ITS

An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow re- moval method, which is based on the improved Bernsen algorithm combined with the Gaussian ﬁlter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for im- age tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16%and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions

2011-10-15

A Review of Computer Vision Techniques for the Analysis of Urban Traffic

Automatic video analysis from urban surveillance cameras is a fast-emerging ﬁeld based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for trafﬁc video with a critical analysis and an outlook to future research directions. This ﬁeld is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing de- ployment of cameras have opened a wide application ﬁeld for video analytics. Several monitoring objectives such as congestion, trafﬁc rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human oper- ators. Systems for the detection and classiﬁcation of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to trafﬁc density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct com- parison of the proposed algorithms difﬁcult. In addition, evalu- ation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classiﬁers for all road users, with a focus on realistic conditions during evaluation.

2011-10-15

On Improving the Efficiency of Tensor Voting

This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.

2011-10-11

Selecting Critical Patterns Based on Local Geometrical

Pattern selection methods have been traditionally developed with a dependency on a specific classifier. In contrast, this paper presents a method that selects critical patterns deemed to carry essential information applicable to train those types of classifiers which require spatial information of the training data set. Critical patterns include those edge patterns that define the boundary and those border patterns that separate classes. The proposed method selects patterns from a new perspective, primarily based on their location in input space. It determines class edge patterns with the assistance of the approximated tangent hyperplane of a class surface. It also identifies border patterns between classes using local probability. The proposed method is evaluated on benchmark problems using popular classifiers, including multilayer perceptrons, radial basis functions, support vector machines, and nearest neighbors. The proposed approach is also compared with four state-of-the-art approaches and it is shown to provide similar but more consistent accuracy from a reduced data set. Experimental results demonstrate that it selects patterns sufficient to represent class boundary and to preserve the decision surface.

2011-10-11

Fast LOG Filtering Using Recursive Filters

Marr and Hildreth's theory of LoG filtering with multiple scales has been extensively elaborated. One problem with LoG filtering is that it is very time-consuming, especially with a large size of filters. This paper presents a recursive convolution scheme for LoG filtering and a fast algorithm to extract zero-crossings. It has a constant computational complexity per pixel and is independent of the size of the filter. A line buffer is used to determine the locations of zero-crossings along with filtering hence avoiding the need for an additional convolution and extra memory units. Various images have been tested

2011-10-11

A discrete expression of Canny's criteria for step

Optimal filters for edge detection are usually developed in the continuous domain and then transposed by sampling to the discrete domain. Simpler filters are directly defined in the discrete domain. We define criteria to compare filter performances in the discrete domain. Canny has defined (1983, 1986) three criteria to derive the equation of an optimal filter for step edge detection: good detection, good localization, and low-responses multiplicity. These criteria seem to be good candidates for filter comparison. Unfortunately, they have been developed in the continuous domain, and their analytical expressions cannot be used in the discrete domain. We establish three criteria with the same meaning as Canny's.

2011-10-11

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