Description: This paper presents a real-time defect detection
algorithm for high-speed steel bar in coil. Because the target speed is
very high, proposed algorithm should process quickly the large
volumes of image for real-time processing. Therefore, defect detection
algorithm should satisfy two conflicting requirements of reducing the
processing time and improving the efficiency of defect detection. To
enhance performance of detection, edge preserving method is
suggested for noise reduction of target image. Finally, experiment
results show that the proposed algorithm guarantees the condition of
the real-time processing and accuracy of detection.-This paper presents a real-time defect detectionalgorithm for high-speed steel bar in coil. Because the target speed isvery high, proposed algorithm should process quickly the largevolumes of image for real-time processing. Therefore, defect detectionalgorithm should satisfy two conflicting requirements of reducing theprocessing time and improving the efficiency of defect detection. Toenhance performance of detection, edge preserving method issuggested for noise reduction of target image. Finally, experimentresults show that the proposed algorithm guarantees the condition ofthe real-time processing and accuracy of detection. Platform: |
Size: 457728 |
Author:陈平 |
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Description: 该图像处理DEMO实现了用VC++6.O读取BMP图像的类，并在此基础上实现了图像灰度变化、图像增强、图像噪声处理、运动目标检测等算法，实用性非常强，开用于图像处理的二次开发-DEMO implements the image processing using VC++6. O read BMP images of the class, and on this basis to achieve a change in image intensity, image enhancement, image noise processing, moving target detection algorithm, practical, very strong, open for the secondary development of image processing Platform: |
Size: 4797440 |
Author:国国 |
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Description: 运动目标检测是整个视频监控系统的最底层，是目标跟踪、目标分类、目
为理解等的基础，因此运动目标检测是视频序列图像处理的关键环节。根
列图像的背景情况可以将运动目标检测划分为静态背景下运动目标检测
态背景下运动目标检测，本章主要研究静态背景下的运动目标检测算法。
-Moving target detection is the lowest level of video surveillance systems, is the target tracking, target classification, currently the basis for understanding, etc., so video motion detection is a key link in image sequence processing. Root out the background of the image motion detection can be divided into static state of the moving target detection of moving object detection, this chapter of the static background of moving target detection algorithm. Platform: |
Size: 1311744 |
Author:欢 |
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Description: 步态识别论文，对目标检测方法进行了分析，提出了在HSL颜色模型空间中，利用时间域中值滤波算法构建背景模型，采用背景减除法实现人体上肢和下肢关节点的检测，采用闽值分割、形态学滤波和颗粒去除操作对关节点的图像进行二值化处理，为后续相关特征的提取做好了准备。
-Gait identification papers, for target detection methods are analyzed, presented at the HSL color space model, using median filtering algorithm in the time domain to build the background model, to achieve detection of human upper limb and lower limb joints using background subtraction method, using threshold segmentation, morphological filtering and particle removal operation for image joints were binary processing, ready for subsequent extraction of relevant features. Platform: |
Size: 10088448 |
Author:luoli |
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Description: 基于人类视觉将图像分割成若干个有意义的区域是目标检测和模式识别的基础。图像分割属于图像处理中一种重要的图像分析技术。图像分割的基本方法是对灰度图像分割，处理图像的亮度分量，简单快速。本论文介绍了传统的图像分割与K-均值聚类算法分割，然后利用OpenCV函数将其实现，并介绍了OpenCV中图像分割相关的基本函数。-Based on the human visual image is segmented into several meaningful regions is the basis for target detection and pattern recognition. Image segmentation is an important image analysis technique in image processing. The basic method of image segmentation is to segment the gray image, the luminance image processing, simple and fast. This paper introduces the traditional segmentation of image segmentation and K- means clustering algorithm, and then use the OpenCV function to achieve the basic functions, and introduces the segmentation of OpenCV in the relevant image. Platform: |
Size: 5035008 |
Author:潇枫 |
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Description: 计算机视觉中最重要的研究之一就是运动目标检测，其不但在模式识别方面具有相关的研究，而且在图像理解领域也有非凡的意义。运动目标检测是通过通过图像序列帧图像来提取运动目标，通过运用相关的算法一幅图片被划分为前景点和背景点。运动目标检测算法是后续的运动目标分类、运动目标跟踪和分析提供了基础。本论文讲述了几种常用的视频运动目标检测算法，并就背景差分法进行了重点研究，通过两种方法来对比差分法的特点。其中背景差分法算法的主要流程为：视频获取、视频转化为图片序列、图片灰度化处理、去除噪声、差分图片、对图片进行后续处理。此次课题的完成采用的是比较常见的数学软件matlab，其具有强大的工具箱。对处理图像这块具有很广泛的支持。-Computer vision and video image processing is one of important research in the field of video moving object detection, whether in the domains of pattern recognition and image understanding has special significance. Moving target detection is to extract video moving object in successive frames, the image is divided into foreground and background. Moving target detection algorithm for subsequent movement target classification, target tracking and analysis provides the basis. This paper tells the story of several common video moving object detection algorithm, and to the study on the background difference method, through the two methods to contrast the characteristics of the finite difference method. The background difference method algorithm is the main process is: the video capture, video into image sequence and image grayscale processing, remove noise, difference image, for subsequent processing images. The completion of this subject is the more common mathematical software matlab, whi Platform: |
Size: 2048 |
Author:wan |
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Description: In previous work, we presented GMTI detection and geo-location results from the AFRL Gotcha challenge data set, which was collected using a 3-channel, X-band, circular SAR system. These results were compared against GPS truth for a scripted vehicle target. The algorithm used for this analysis is known as ATI/DPCA, which is a hybrid of alongtrack interferometry (ATI) and the displaced phase center antenna (DPCA) technique. In the present paper the use of ATI/DPCA is extended in order to detect and geo-locate all observable moving targets in the Gotcha challenge data,
including both the scripted movers and targets of opportunity. In addition, a computationally efficient SAR imaging technique is presented, appropriate for short integration times, which is used for computing an image of the scene of interest using the same pulses of data used for the GMTI processing. The GMTI detections are then overlaid on the SAR image to produce a simultaneous SAR/GMTI map. Platform: |
Size: 1273856 |
Author:asgary
|
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Description: 在机器视觉应用领域里特征检测和匹配是一个很重要的算法，比如图像配准、跟踪和目标检测。这个例子里，我们用基于特征的方法完成图像拼接。处理的方法是先用图像配准特征点。不同于单图像对配准，这里是多图像对的配准完成图像拼接。(In the field of machine vision application, feature detection and matching is a very important algorithm, such as image registration, tracking and target detection. In this example, we use a feature based method to complete the image stitching. The method of processing is to register the feature points with the image first. Unlike the single image pair registration, this is the registration of the multi image pairs that completes the image stitching.) Platform: |
Size: 620544 |
Author:bbyron |
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Description: 基于离散分数布朗随机场模型的水下图像目标检测方法。该方法根据分形理论和水下图像的特点，以图像中每
个像素点为中心取窗口，计算在该窗口内的分形维数均值，将该均值作为中心像素的分形特征，然后根据分形维
数分布图确定分割阈值，从而实现对水下图像分割，并且通过将目标表面不同尺度下的灰度差分平均值进行归一
化处理，减少了用于表示不同尺度下的平均绝对值灰度差分的数据，从而提高算法检测效率(Underwater target detection method based on discrete fractional Brown random field model. According to the fractal theory and the characteristics of underwater images, the method takes every image in the image.
A pixel is taken as the center to take the window, and the mean value of the fractal dimension in the window is calculated. The mean value is taken as the fractal characteristic of the central pixel, and then according to the fractal dimension.
The number distribution map determines the segmentation threshold, so as to realize underwater image segmentation, and normalizing the average value of the gray scale difference under different scales of the target surface.
Chemical processing reduces the mean absolute value of the gray scale difference data at different scales, thus improving the detection efficiency of the algorithm.) Platform: |
Size: 1537024 |
Author:辉辉AG |
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Search - target detection algorithm in image processing