Open garaemon opened 9 years ago
Sorry for late response. I can't understand several things.
Define mask image in stochastic representation (defined in probability density function)
this part is realized by attention_clipper node and roi_clipper node in jsk_pcl_ros package.
Extract keypoints as usual
extract by surf or sift..etc
sample points like RANSAC based detection but sample them according to pre-defined stochastic mask image. cf. sample with replacement
I can't understand this part. How to sample points? Sample points (like particle filter) only in the region of stochastic mask image? (Is it correct?) And if so, how to resample?
do not compute feature vectors if probability is under threshold (= mask image)
Each coordinate's probability is given by calculating probability density destribution using sampled points?
this part is realized by attention_clipper node and roi_clipper node in jsk_pcl_ros package.
It's a binary representation. If object is out of ROI, we cannot recover from it.
I can't understand this part. How to sample points? Sample points (like particle filter) only in the region of stochastic mask image? (Is it correct?) And if so, how to resample?
RANSAC sample points based on purely random sampling. We can add bias to sampling according to a priori probability.
do not compute feature vectors if probability is under threshold (= mask image)
Each coordinate's probability is given by calculating probability density destribution using sampled points?
These two lines do not correspond.
this part is realized by attention_clipper node and roi_clipper node in jsk_pcl_ros package. It's a binary representation. If object is out of ROI, we cannot recover from it.
actually I don't follow all your discussions, but how do you get the first ROI? What @garaemon means with Stochastic mask image based detection is like below?
Just an idea cc: @h-kamada