Description: 几篇运动目标检测的论文，适合在研究方向入门使用，仅供参考。-few moving target detection paper, the research direction for the use of induction, for information purposes only. Platform: |
Size: 2700288 |
Author:纸飞机 |
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Description: 在人脸检测中用于标记人脸位置的objectmarker.exe，本人花了很大力气才收集到的-Face Detection in for marking the location of human faces objectmarker.exe, I spent a lot only collected Platform: |
Size: 920576 |
Author:王仿 |
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Description: 这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing
images extremely rapidly while achieving high detection rates. There are three
key contributions. The first is the introduction of a new image representation called the
“Integral Image” which allows the features used by our detector to be computed very
quickly. The second is a learning algorithm, based on AdaBoost, which selects a small
number of critical visual features and yields extremely efficient classifiers [4]. The
third contribution is a method for combining classifiers in a “cascade” which allows
background regions of the image to be quickly discarded while spending more computation
on promising object-like regions. A set of experiments in the domain of face
detection are presented. The system yields face detection performance comparable to
the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop,
face detection proceeds at 15 frames per second Platform: |
Size: 784384 |
Author:lai |
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Description: 通过fisher对hog特征降维，并用于物体检测-We present a method for object detection that combines AdaBoost learning with local histogram features. On the side of learning we improve the performance by designing a weak learner for multi-valued features based on Weighted Fisher Linear Discriminant. Platform: |
Size: 917504 |
Author:ljj |
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Description: 一种机器视觉的物体检测算法，是一种先进物体检测算法，全英文描述.讲述一种机器视觉的物体检测算法的实现-his paper describes a visual object detection framework that is capable of processing images extremely
rapidly while achieving high detection rates. There are three key contributions. The fi rst is the introduction
of a new image representation called the “Integral Image” which allows the features used by our detector
to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small
number of critical visual features and yields extremely effi cient classifi ers [6]. The third contribution is a
method for combining classifi ers in a “cascade” which allows background regions of the image to be quickly
discarded while spending more computation on promising object-like regions. A set of experiments in the
domain of face detection are presented. The system yields face detection performace comparable to the best
previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15
frames Platform: |
Size: 402432 |
Author:huyongjin |
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Search - adaboost for object detection