I am doing a background search for suggesting my own thesis topic. I'm searching for papers that kind of introduced the concept of pose graph estimation for slam. I looked up google scholar and my university library but found modifications or specific application based papers on PGO.
What are some papers that are the "base" of post graph optimization?
Related
I am looking for a way of approximating a surface based on a set of 3D data points. For this purpose I would like to use a method based on radial basis functions but I cannot find a free implementation in C++.
I looked in ITK, VTK and open CV but I did not find anything...
Does anyone knows a free implementation of such an algorithm ?
Any suggestion about the reconstruction of a surface based on a set of 3D data points is also more than welcome ! :)
3D surface reconstruction can be challenging. I would first recommend taking a look at PCL. The Point Cloud Library has grown into a nice set of tools for 3D point management and interpretation, and its license and API sound compatible with your needs. The surface reconstruction features of the library appear to be most applicable. In fact, RBF reconstruction is supported.
If PCL doesn't work, there are other options:
MeshLab,
This SO post provides a nice summary, and
of course, Wikipedia provides some links
Finally, you might search CiteSeerX, Google Scholar, etc. for papers like this one. As an example, a search for "3D Surface Reconstruction" at CiteSeerX yields many hits. RBF-based reconstruction is just one of many methods: is your application truly limited to radial basis functions? If not, there are many choices, (i.e. Ball Pivoting Algorithm). See this survey paper for some comparisons.
Im new at OpenCV so please help me out. I want to detect lips edge using OpenCV. So can you give me some links and solution ?
Ive checked out normal procedure of detecting face and mouth using OpenCV but accuracy is not there. Ive used "haarcascade_mcs_mouth" to detect mouth in a picture but result was not so good. And I heard about AAM method , but could not get any documents regarding it. please help me...
Lip recognition is a problem in Computer Vision that it is not completely solved. The haar-like classifiers that you've been using (included in OpenCV) perform well in face recognition but for lip recognition better techniques have been developed. You will have to build different algorithms and choose the better one for your purpose. The fact that you are developing for iOS makes the task harder because of additional constraints (memory footprint, CPU, etc.). I have condensed for you a brief overview of the state of the art in lip recognition so you can research further:
Methods for recognising lips can be classified in three big categories:
Image based techniques: These are based on the hypotesis that skin and lips have different color. The paper [2] is an example of this kind of approach applied for sign language recognition. Color clustering has also been explored by [3]. It assumes that there are two pixel classes in the image: skin and lips. This method is not appropriate if the person has a beard, or is showing his/her teeth, for example.
Model based techniques: These methods are more robust than the previous ones because they use prior information about the lip shape. However, they are more expensive computationally so they may not be suitable for an implementation on mobile devices. AAM (Active Appearance Models) belong to this group and learn the shape of lips from manually annotated data. In the "External links" section of the Wikipedia article you can see some open source implementations and libraries that can be ported to C++/OpenCV.
Hybrid techniques: These methods are a combination of image based methods and model based methods. Typically, a color based technique is first applied to the image in order to estimate the lip region position and size; then, a model based technique (like AAM) is applied to the region of interest to extract lip contours. [4] is an example of this technique.
[2] U. Canzler and T. Dziurzyk, "Extraction of Non Manual Features for Videobased Sign Language Recognition". ;In Proceedings of MVA. 2002, 318-321
[3] Leung, Shu-Hung, Shi-Lin Wang, and Wing-Hong Lau. "Lip image segmentation using fuzzy clustering incorporating an elliptic shape function." Image Processing, IEEE Transactions on 13.1 (2004): 51-62.
[4] Bouvier, Christian, P-Y. Coulon, and Xavier Maldague. "Unsupervised lips segmentation based on ROI optimisation and parametric model." Image Processing, 2007. ICIP 2007. IEEE International Conference on. Vol. 4. IEEE, 2007.
I am new to computer vision and struggling to understand the concept of splat,blur and slice technique for accelerating the gaussian filtering technique. I have been following this dissertation http://books.google.com/books?id=ihe0nfm97eIC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false.
However, I am a very beginner. So can anyone point me to some basics that could help me understand this technique?
If you need to start from the very basics, there are many good introductory texts on image processing. Personally I liked the Gonzalez-Wintz a lot, but it may be hard to find these days. The Petrou one gets good reviews.
I have implemented foreground subtraction to detect moving cars and the results look pretty good. The only issue is in removing the shadows , which form a part of the foreground.
I searched online to find a way to fix this and found links to many papers :
1) Moving Shadow Detection with Low- and Mid-Level Reasoning
2)J.-F. Lalonde, A. A. Efros, and S. G. Narasimhan. Detecting Ground Shadows
in Outdoor Consumer Photographs. in European Conference on Computer Vision,
2010.
Please watch the video
for a better idea of what I am looking for. Though the papers make for great learning, they are beyond my level of comprehension at this point. Could someone point me to some open source code which could help me understand and implement shadow removal?
See the following paper, which contains links to the source code for various shadow detection/removal algorithms:
A. Sanin, C. Sanderson, B.C. Lovell. "Shadow Detection: A Survey and Comparative Evaluation of Recent Methods", Pattern Recognition, Vol. 45, No. 4, pp. 1684-1695, 2012.
Official version at:
http://dx.doi.org/10.1016/j.patcog.2011.10.001
There is also a pre-print of the above paper on the Arxiv server: http://arxiv.org/abs/1304.1233
I want to track objects of simple shapes and rather homogeneous colors. I would like to do it more or less by myself, with minimum copy-paste. Does anyone know any good book/course/set of tutorials to computer vision?
Preferred platform is .net, but if materials are general enough it is fine.
Thanks,
Andrey
The fundamentals of the application you're interested in developing can be found in any introductory computer vision book.
You can take a look at Richard Szeliski's book which is available online and is very good: http://szeliski.org/Book/
Computer Vision: A Modern Approach is another introductory book to computer vision.