I would like to improve vigra. First of all I would like to propose an idea and then if you will not like this idea I would like to ask for sugestions regarding the image feature extraction project.
The proposed idea is an obstacle detection based on elevation map:
detection is a an important issue to all vision systems. In the
automotive field it can be used to detect road obstacles that would
further need to be classified, reducing the number of hypothesis and
improving the speed of the alorithm. In the industial inspection field,
object can be detected on the manufacturing line.
method of obstacle detection is based on elevation map. After computing
the BM ( I will also implement a robust BM algorithm), the elevation
map is computed grouping the points that have a certain hight. If we get
more points in a neighbourhood we consider than point an obstacle and
find his boundaries. This found object can be offerd for further
processing (to a classifier for example, in order to classifie the
object in certain classes, however the classification is not in the
scope of this project. There are many methods that do this task of
classification on the internet).
proposed method is invariant to ground issues, unlike other methods( i
want to say here that methods like UV disparity are prone to errors, for
example if there is a bump in the road or a whole it can detect it as
an obstacle, wheras the method proposed is invariant to such issues).
algorithm will run in real time. ( > 10 fps). I really believe that
the method proposed is a must to any system.
The second topic is regarding the image features for machine learning. There are many features that can be extracted from images and furthe be used in computation.
HOG, HOPE, SIFT,HARIS, LBP, Chain Codes, bag of features/ words, features like the mean and variation of the image after applying filters(gray scale filter, laplacian) etc.
I have worked with all the above mentioned filters, using them either from a framework or implementing them from scrach.
My question in this topic would be what type of feature descriptors would you desire ? How many ?
I hooe to hear from you soon!