Description: Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by a set of local invariant features, whose motions are observed by a feature correspon-
dence process. A generative model is proposed to depict
the relationship between local feature motions and object
global motion, whose parameters are learned efciently by
an on-line EM algorithm. And the object global motion is estimated in term of maximum likelihood of observations.Then an updating mechanism is employed to adapt object representation. Experiments show that our framework is
exible and robust in dealing with appearance changes,background clutter, illumination changes and occlusion Platform: |
Size: 424705 |
Author:chenjieke |
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Description: source code for light normalisation presented by Ralph Gross in this paper
@inproceedings{RGross_AVBPA_2003,
author = "Ralph Gross and Vladimir Brajovic",
title = "An Image Preprocessing Algorithm for Illumination Invariant Face Recognition",
booktitle = "4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA)",
month = "June",
year = "2003",
publisher = "Springer"
}
-source code for light normalisation presented by Ralph Gross in this paper
@inproceedings{RGross_AVBPA_2003,
author = "Ralph Gross and Vladimir Brajovic",
title = "An Image Preprocessing Algorithm for Illumination Invariant Face Recognition",
booktitle = "4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA)",
month = "June",
year = "2003",
publisher = "Springer"
}
Platform: |
Size: 3072 |
Author:laouer |
Hits:
Description: Detecting, identifying, and recognizing salient regions or feature points
in images is a very important and fundamental problem to the computer vision
and robotics community. Tasks like landmark detection and visual odometry,
but also object recognition benefit from stable and repeatable salient features
that are invariant to a variety of effects like rotation, scale changes, view point
changes, noise, or change in illumination conditions. Recently, two promising new
approaches, SIFT and SURF, have been published. In this paper we compare and
evaluate how well different available implementations of SIFT and SURF perform
in terms of invariancy and runtime efficiency. Platform: |
Size: 869376 |
Author:yangwei |
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Description: SIFT: Scale Invariant Feature Transform
SIFT isn’t just scale invariant. You can change the following, and still get good results:
• Scale (duh)
• Rotation
• Illumination
• Viewpoint
Platform: |
Size: 342016 |
Author:unni |
Hits:
Description: SIFT isn’t just scale invariant. You can change the following, and still get good results:
• Scale (duh)
• Rotation
• Illumination
• Viewpoint
Platform: |
Size: 108544 |
Author:unni |
Hits:
Description: SIFT isn’t just scale invariant. You can change the following, and still get good results:
• Scale (duh)
• Rotation
• Illumination
• Viewpoint
Platform: |
Size: 60416 |
Author:unni |
Hits:
Description: SIFT isn’t just scale invariant. You can change the following, and still get good results:
• Scale (duh)
• Rotation
• Illumination
• Viewpoint
Platform: |
Size: 77824 |
Author:unni |
Hits:
Description: For this project, the shadow removal method used by Finlayson et al. in [1] was implemented.
This report contains an overview of the mathematical background and a detailed
discussion on the experiments performed with the implementation.
This method consists of two independent steps. First, an illumination invariant image
is derived from a color image for which an appropriate color calibration is known. Second,
shadow edges are detected by finding edges in the intensity image that are not in the illumination
invariant image. Gradient information is then erased at those shadow edges, and
a new shadowless color image is formed by solving a Poisson’s equation. Platform: |
Size: 3089408 |
Author:dermeche |
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Description: 基于内容的图像检索技术的关键在于特征提取，是利用图像的颜色、形状、纹理、轮廓、对象的空间关系等客观独立的存在于图像中的基本视觉特征作为图像的索引，计算查询图像和目标图像的相似距离，按相似度匹配进行检索。综合国内外研究现状，可将基于内容的图像检索技术分为如下几种类型:基于颜色特征的检索、基于纹理特征的检索、基于形状及区域的检索、基于空间约束关系的检索。-Based on comparing various affine invariant regional basis, selection of image stabilization exremum area biggest content segmentation and extraction. It has the affine invariants, the neighboring territory, stability and multi-scale characteristics, but also because of regional only by grey value of decision, so the size relations is not sensitive illumination change. In images all the pixel, then through sorting for barrel separated binary tree forest-- set the extreme area all obtained images and construct component tree, finally through the biggest stable delay-independent conditions MSER area is MSER, obtain the area without rules boundary shape of its, in order to facilitate to quantification description, using covariance matrix region neat optimization, made the final output of extreme value for the oval areas regional. Platform: |
Size: 2584576 |
Author:陈利华 |
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Description: 人脸识别工具箱。包含了人脸识别的各种基本函数，用MATLAB编写，非常适合研究者使用。-The INface toolbox for illumination invariant face recognition .The INFace (Illumination Normalization techniques for robust Face recognition)
toolbox v 2.0 is a collection of Matlab functions and scripts intended to helpresearchers working in the fi eld of face recognition. Platform: |
Size: 747520 |
Author:王蕊 |
Hits:
Description: The INFace (Illumination Normalization techniques for robust Face recognition) toolbox v2.0 is a collection of Matlab functions and scripts intended to help researchers working in the field of face recognition. Platform: |
Size: 732160 |
Author: |
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Description: 频域光照归一化的人脸识别
基于NSCT和SQI的光照不变量及人脸识别
张量局部判别投影的人脸识别
基于全局和局部特征集成的人脸识别-Frequency-domain light normalized face recognition
based on the NSCT and SQI s illumination invariant face recognition
Zhang amount Bureau Ministry of discriminant projection of face recognition
based on global and local features integrated face recognition Platform: |
Size: 2361344 |
Author:朱同辉 |
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Description: This paper focuses on the detection of objects with a Lambertian surface under varying
illumination and pose. modeling
the different illuminations from a small number of images in a training set this automaticallyvoids the illumination effects, allowing fast illumination invariant detection, without having to
create a large training set.-This paper focuses on the detection of objects with a Lambertian surface under varying
illumination and pose. modeling
the different illuminations from a small number of images in a training set this automaticallyvoids the illumination effects, allowing fast illumination invariant detection, without having to
create a large training set. Platform: |
Size: 1134592 |
Author:bobobobo |
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Description: 光照不变性特征，主要用于目标跟踪，是比较稳定的一种跟踪方法，应用很广-A tracking method for illumination invariant features, mainly used for target tracking, is relatively stable, a very wide application Platform: |
Size: 3147776 |
Author:胡鹏程 |
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Description: 梯度脸是一种提取光照不变特征的算法，对光照有很好的处理效果-Gradient face is a kind of algorithm to extract the illumination invariant features, and it has a good effect on the illumination. Platform: |
Size: 2003968 |
Author:jisaiping |
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Description: Illumination Invariance through ADMMs.The code of : An Image Preprocessing Algorithm for Illumination Invariant Face Recognition in AVPBA 2003.-Illumination Invariance through ADMMs.The code of : An Image Preprocessing Algorithm for Illumination Invariant Face Recognition in AVPBA 2003. Platform: |
Size: 2048 |
Author:abdou |
Hits:
Search - Illumination invariant