Description: GPU Accelerating Speeded-Up Robust Features. Many computer vision tasks require interest point detection and description, such as real-time visual navigation. We present a GPU implementation of the recently proposed Speeded-Up Robust Feature extractor, currently the state of the art for
this task. Robust feature descriptors can give vast improvements
in the quality and speed of subsequent steps, but require intensive
computation up front that is well-suited to inexpensive graphics
hardware. We describe the algorithm’s translation to the GPU in
detail, with several novel optimizations, including a new method
of computing multi-dimensional parallel prefix sums. It operates
at over 30 Hz at HD resolutions with thousands of features and
in excess of 70 Hz at SD resolutions. Platform: |
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Author:yangwei |
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Description: Segmentation (image processing)
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In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels) (Also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze
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Size: 25600 |
Author:joko |
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Description: SIFT特征（Scale-invariant feature transform，尺度不变特征转换)是一种电脑视觉的算法用来侦测与描述影像中的局部性特征，它在空间尺度中寻找极值点，并提取出其位置、尺度、旋转不变量，此算法由 David Lowe 在1999年所发表，2004年完善总结。其应用范围包含物体辨识、机器人地图感知与导航、影像缝合、3D模型建立、手势辨识、影像追踪和动作比对。-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving. Platform: |
Size: 8782848 |
Author:张博 |
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Description: Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia.-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia. Platform: |
Size: 90112 |
Author:dhivya |
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Description: SIFT匹配（Scale-invariant feature transform，尺度不变特征转换)是一种电脑视觉的算法用来侦测与描述影像中的局部性特征，它在空间尺度中寻找极值点，并提取出其位置、尺度、旋转不变量，其应用范围包含物体辨识、机器人地图感知与导航、影像缝合、3D模型建立、手势辨识、影像追踪和动作比对。-Matching SIFT (Scale invariant feature transform, Scale invariant feature transform) is a computer vision algorithm is used to detect and describe the local characteristics of image, it seek extreme value point in the space Scale, and extract its location, Scale and rotation invariant, its application areas include perception and object recognition, robot map navigation, image stitching, 3 d modeling, gesture recognition, image tracking and action.
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Size: 10620928 |
Author:陈天华 |
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Description: Our world is three-dimensional, but camera image is two-dimensional, in order to obtain
the three-dimensional information of objective object’s by the two-dimensional images, we
need to rebuild the three-dimensional model of the target so that we can target the quantitative
analysis. The same subject in computer vision and photogrammetry community,the stereo
vision-based three-dimensional reconstruction techniques have been widely used in medical
imaging, robot navigation, virtual reality, terrain exploration and other fields. Platform: |
Size: 14069760 |
Author:杨松 |
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Description: 数字图像处理（Digital Image Processing）是通过计算机对数字图像进行变换、增强、复原、分割及匹配等处理的方法和技术，在航空航天、生物医学工程、工业检测、机器人视觉、公安司法、导航制导、安保监控及文化艺术等诸多领域有着极为广泛的应用。数字图像处理技术的研究和应用离不开程序设计，Visual C++则是最有力、最常用的数字图像处理程序开发工具之一-Digital image processing (Digital Image Processing) is a computer for digital image conversion, enhancement, restoration, segmentation and matching methods and technical processing, aerospace, biomedical engineering, industrial inspection, robot vision, public security and justice, navigation guidance , security monitoring, and arts and culture and other fields has a very wide range of applications. Digital image processing technology research and application inseparable programming, Visual C++ is an image processing program, one of the most powerful development tools, the most common digital Platform: |
Size: 731136 |
Author:zzj |
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Description: 一篇关于光流传感器的论文，光流传感器广泛应用于微型西轴飞行器的室内定位-Robust velocity and position estimation at high
update rates is crucial for mobile robot navigation. In recent
years optical flow sensors based on computer mouse hardware
chips have been shown to perform well on micro air vehicles.
Since they require more light than present in typical indoor
and outdoor low-light conditions, their practical use is limited.
We present an open source and open hardware design 1 of an
optical flow sensor based on a machine vision CMOS image
sensor for indoor and outdoor applications with very high light
sensitivity. Optical flow is estimated on an ARM Cortex M4
microcontroller in real-time at 250 Hz update rate. Angular
rate compensation with a gyroscope and distance scaling using
a ultrasonic sensor are performed onboard. The system is
designed for further extension and adaption and shown in-flight
on a micro air vehicle. Platform: |
Size: 1458176 |
Author:丁杰峰 |
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Description: GPS和INS组合导航程序，非常适合计算机视觉方面的研究使用,具有丰富的参数选项。- GPS and INS navigation program, Very suitable for the study using computer vision, It has a wealth of parameter options. Platform: |
Size: 9216 |
Author:江兴维 |
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Description: 本书介绍Python的计算机视觉编程，用清晰的Python示例，细致讲解对象识别、3D重建、立体图像、增强现实及其他计算机视觉应用技巧，给出了分析图像的工具与方法。内容：机器人导航、医学图像分析；图像映射与变换；多图像的3D重建；用聚类方法基于相似性和内容组织图像；基于视觉内容的图像检索技巧；实现图像内容分类和对象识别的算法；通过Python接口访问常用的OpenCV库。(This book introduces Python's computer vision programming. With clear Python examples, it elaborates on object recognition, 3D reconstruction, stereo image, augmented reality and other computer vision application techniques, and gives tools and methods for image analysis. Contents: Robot navigation, medical image analysis; image mapping and transformation; multi-image 3D reconstruction; image organization based on similarity and content using clustering method; image retrieval techniques based on visual content; algorithm for image content classification and object recognition; access to common OpenCV library through Python interface.) Platform: |
Size: 5047296 |
Author:qhxinshou |
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