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Description: Face-Detector, OpenCV Haar cascade object detection. Matlab interface for running haar-cascade object detectors from the Intel Open Computer Vision Library.
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Author: njustyw |
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Description: In this paper, a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust
Features) is presented. It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descriptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance.-In this paper, a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust
Features) is presented. It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descriptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance.
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Author: yangwei |
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Description: Forward-Backward MHI(前后向运动历史图)是一个快速有效的运动目标检测方法，本程序正是实现2006年Zhaozheng Yin and Robert Collins的“Moving Object Localization in Thermal Imagery
by Forward-backward MHI”文献中的方法。此程序可以很方便的对该方法的优缺点作出评判。给运动目标检测器的选择提供了除“帧间差”和“背景相减”外的又一方法。
本程序基于OpenCV。-Forward-Backward MHI (before and after the historical figure to the movement) is a fast and efficient moving target detection methods, this program is to achieve in 2006 Zhaozheng Yin and Robert Collins of the " Moving Object Localization in Thermal Imagery by Forward-backward MHI" in the literature method. This procedure can easily judge the advantages and disadvantages of the method. To the moving target detector choose to provide that except " bad frames" and the " background subtraction" another way outside. The program is based on OpenCV.
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Author: huangjian |
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Description: canny edge detection for find edge in image processing.is the best edge detector for object segmentation
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Author: Delphi_CAT |
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Description: musicdsp source code archive-Analysis
Beat Detector Class
Coefficients for Daubechies wavelets 1-38
DFT
Envelope detector
Envelope Detector class (C++)
Envelope follower with different attack and release
Fast in-place Walsh-Hadamard Transform
FFT
FFT classes in C++ and Object Pascal
Frequency response from biquad coefficients
Java FFT
Look ahead limiting
LPC analysis (autocorrelation+ Levinson-Durbin recursion)
Magnitude and phase plot of arbitrary IIR function, up to 5th order
Measuring interpollation noise
QFT and DQFT (double precision) classes
Simple peak follower
tone detection with Goertzel
Tone detection with Goertzel (x86 ASM)
Effects
2 Wave shaping things
Alien Wah
Band Limited PWM Generator
Bit quantization/reduction effect
Class for waveguide/delay effects
Compressor
Decimator
Delay time calculation for reverberation
DIRAC- Free C/C++ Library for Time and Pitch Manipulation of Audio Based on Time-Frequency Transforms
dynamic convolution
Early echo s with image-mirror techn
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Author: Alan Tang |
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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.
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Size: 492544 |
Author: 谷川 |
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Description: 运动物体检测，包括背景处理（中值滤波，灰度化，确定匹配中心，得到特征区域，物体轨迹的绘制），运动判定分析（腐蚀膨胀等）-Moving object detection, including background processing (median filter, grayscale, match the center to determine, by feature area, the object trajectory drawing), exercise decision analysis (corrosion expansion, etc.)
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Author: jcq |
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Description: 功能说明：输入一幅图像，能对此图像检测关键点，这些点通常对应图像中的重要对象，可用于对象识别，也可用来作为特征进行图像分类。运行其中的例程Ldx_GoSalScale.m，就可以知道此程序的用法（调试环境matlab7.0）。-Function: input image, this image can detect critical points, these points usually correspond to an important object of the image can be used for object recognition, can also be used as features for image classification. Run one of routine Ldx_GoSalScale.m, you can know the usage of this program (debugging environment matlab7.0).
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Author: Li Daxiang |
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Description: We present an object class detection approach
which fully integrates the complementary strengths offered
by shape matchers. Like an object detector, it can
learn class models directly from images, and can localize
novel instances in the presence of intra-class variations,
clutter, and scale changes. Like a shape matcher,
it finds the boundaries of objects, rather than just their
bounding-boxes. This is achieved by a novel technique
for learning a shape model of an object class given images
of example instances. Furthermore, we also integrate
Hough-style voting with a non-rigid point matching
algorithm to localize the model in cluttered images.
As demonstrated by an extensive evaluation, our
method can localize object boundaries accurately and
does not need segmented examples for training (only
bounding-boxes).
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Author: Daria |
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Description: 基于MATLAB的boosting目标检测!-Detection based on MATLAB boosting goals!
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Author: tiger |
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Description: The files written out by the detection process are used as input for the tracker to infer trajectories of each object. The tracker needs to deal with different scenarios
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Author: ysy |
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Description: This program is to detect object by using Ferns algorithm.
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Author: 金先生 |
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Description: Viola Jones 的目标检测程序，虽然人脸检测已经发展了近十年，但是还有不少学者研究人脸检测。此程序是通过邮件从KTH皇家技术学院的Vahid Kazemi要到的。特意共享出来。-Viola Jones Object Detector ，which can finish adaboost face detection
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Description: 2014年TI杯电子设计大赛B题金属物体探测定位器源代码，基于msp430f5529单片机编写，包含IAR工程文件。通过步进电机与细分器的组合驱动传送带，带动探测器扫描。-2014 Electronic Design Contest B TI Cup title metallic object detection locator source code, based on msp430f5529 MCU writing, including IAR project files. Through a combination of stepper motor and drive belt is broken, driven detector scan.
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Author: xx |
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Description: A matlab version of object detector and tracker to be used as an auxiliary function for various computer vision applications
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Author: yasar |
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Description: 本书是关于雷达目标检测和恒虚警(CFAR)处理理论与方法的一部专著。书中总结了三十多年来，在这一领域国际上的研究进展及大量研究成果。全书由15章组成，主要内容有经典的固定门限检测、均值类CFAR检测器、有序统计类CFAR检测器、采用自动筛选技术的广义有序统计类CFAR检测器、自适应CFAR检测器、韦布尔和对数正态杂波背景中的CFAR检测器、复合高斯分布杂波中的CFAR处理、非参量CFAR处理、杂波图CFAR处理、变换域CFAR处理、距离扩展目标检测、多传感器分布式CFAR处理以及其他CFAR处理方法，最后是本书的回顾、建议与展望-This book is about the radar target detection and constant false alarm rate (CFAR) processing theory and methods of a monograph. The book summarizes the thirty years, research progress in this area and a large number of international research. The book consists of 15 chapters, the main contents of the classic fixed threshold detection, mean class CFAR detector, orderly statistical category CFAR detector, automatic screening generalized order statistics class CFAR detector, adaptive CFAR detector, Wei Boolean and lognormal clutter the CFAR detector, complex Gaussian clutter CFAR processing, non-parametric CFAR processing, clutter map CFAR processing, CFAR processing transform domain the extended target detection, multi-sensor distributed CFAR processing and other CFAR processing method, and finally the review of the book, suggestions and Prospects
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Author: wangjianlu |
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Description: 用VC++和OpenCV编写的一个运动目标检测软件-Moving object detector
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Author: dust |
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Description: 文献Improving an Object Detector and Extracting Regions using Superpixels的配套代码-code for paper: CVPR13_Improving an Object Detector and Extracting Regions using Superpixels
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Author: zili |
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Description: 由一个3D变形人脸模型取自动生成适应的训练样本。由统计视角，tailored训练数据保证了所有的数据变化且由任意的人脸属性丰富训练样本，例如，年龄或体重。更进一步，它可能自动适应到环境约束，例如，来自于监控摄像机的照明或视角约束。我们使用裁剪的（tailor）图象训练一个新的Viola Jones的adaboost 目标检测框架的多核实现。这个新的实现不仅快速的，而且多特征通道的使用成为可能，例如，在训练期间的颜色特征。在我们实验中，我们训练7个依赖视角的人脸检测子并在Face Detection Data Set 和 Benchmark (FDDB)中评估它们。- takes a look
into the automated generation of adaptive training samples
a 3D morphable face model. Using statistical insights,
the tailored training data guarantees full data variability
and is enriched by arbitrary facial attributes such as age
or body weight. Moreover, it can automatically adapt to
environmental constraints, such as illumination or viewing
angle of recorded video footage surveillance cameras.
We use the tailored imagery to train a new many-core implementation of Viola Jones’ AdaBoost object detection framework. The new implementation is not only faster but also
enables the use of multiple feature channels such as color
features at training time.
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Author: 郭继东 |
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Description: 神经网络引入后，检测框架变得更快更准确。然而，大多数检测方法受限于少量物体。检测和训练数据上联合训练物体检测器，用有标签的检测图像来学习精确定位，同时用分类图像来增加词汇和鲁棒性。原YOLO系统上生成YOLOv2检测器；在ImageNet中超过9000类的数据和COCO的检测数据上，合并数据集和联合训练YOLO9-After the neural network is introduced, it is becoming faster and more accurate detection frame. However, most detection methods is limited by the small number of objects. Testing and training on joint training data object detector for detecting an image tag to learn precise positioning, while using image classification to increase vocabulary and robustness. YOLOv2 detector generates the original YOLO system ImageNet more than in the 9000 class of data and test data COCO, the consolidated data sets and joint training YOLO9000
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Author: 安宁 |
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