1. A computer-implemented method for tracking one or more catheter objects in a sequence of images, the method comprising: determining, by a computer, a foreground portion of the first image comprising portions of the first image corresponding to one or more catheter electrode locations; determining, by the computer, a background portion of the first image which excludes the foreground portion; applying, by the computer, a steerable filter or a pre-processing method to the background portion of the first image to create a non-catheter structures mask which excludes ridge-like structures in the background portion of the first image; generating, by the computer, a dictionary based on catheter object locations in the first image, wherein sparse coding is used to represent the non-catheter structures mask as a plurality of basis vectors in the dictionary; identifying, by the computer, one or more catheter object landmark candidates in the sequence of images; generating, by the computer, a plurality of tracking hypothesis for the catheter object landmark candidates; generating, by the computer, a voting score for the catheter object landmark candidates based on a voting contribution of each of a plurality of image patches used to localize the catheter object locations in the first image; and selecting, by the computer, a first tracking hypothesis from the plurality of tracking hypothesis based on the dictionary and the voting score.
1. A computer-implemented method for tracking one or more catheter objects in a sequence of images, the method comprising: determining, by a computer, a foreground portion of the first image comprising portions of the first image corresponding to one or more catheter electrode locations; determining, by the computer, a background portion of the first image which excludes the foreground portion; applying, by the computer, a steerable filter or a pre-processing method to the background portion of the first image to create a non-catheter structures mask which excludes ridge-like structures in the background portion of the first image; generating, by the computer, a dictionary based on catheter object locations in the first image, wherein sparse coding is used to represent the non-catheter structures mask as a plurality of basis vectors in the dictionary; identifying, by the computer, one or more catheter object landmark candidates in the sequence of images; generating, by the computer, a plurality of tracking hypothesis for the catheter object landmark candidates; generating, by the computer, a voting score for the catheter object landmark candidates based on a voting contribution of each of a plurality of image patches used to localize the catheter object locations in the first image; and selecting, by the computer, a first tracking hypothesis from the plurality of tracking hypothesis based on the dictionary and the voting score. 6. The method of claim 1 , wherein the identifying, by the computer, the one or more catheter object landmark candidates in the sequence of images comprises: identifying, by the computer, a first object landmark candidate corresponding to a first object type using one or more first classifiers trained for the first object type; and identifying, by the computer, a second object landmark candidate corresponding to a second object type using one or more second classifiers trained for the second object type.