Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based M-estimator. Unlike other methods in the RANSAC family of techniques, where performance depends on a user supplied scale parameter, in the pbM-estimator technique this scale parameter is estimated automatically from the data using kernel smoothing density estimation. In this work  improve the performance of the pbM-estimator by changing its cost function. Replacing the cost function of the pbM-estimator with the changed one yields the modified pbM-estimator. The cost function of the modified pbM-estimator is more stable relative to the scale parameter and is also a better classifier. Thus  get a more robust and effective technique. A new general method to estimate the runtime of robust regression algorithms is proposed. Using it  show, that the modified pbM-estimator runs 2 -3 times faster than the pbM-estimator. Experimental results of fundamental matrix estimation are presented demonstrating the correctness of the proposed analysis method and the advantages of the modified pbM-estimator.
['Stas Rozenfeld', 'Ilan Shimshoni']
I am writing a research paper on a idea The modified pbM-estimator method and a runtime analysis technique for the RANSAC family
[{"from": "human", "value": "I am writing a research paper on a idea The modified pbM-estimator method and a runtime analysis technique for the RANSAC family"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Robust regression techniques are used today in many computer vision algorithms. Chen and Meer recently presented a new robust regression technique named the projection based M-estimator. Unlike other methods in the RANSAC family of techniques, where performance depends on a user supplied scale parameter, in the pbM-estimator technique this scale parameter is estimated automatically from the data using kernel smoothing density estimation. In this work  improve the performance of the pbM-estimator by changing its cost function. Replacing the cost function of the pbM-estimator with the changed one yields the modified pbM-estimator. The cost function of the modified pbM-estimator is more stable relative to the scale parameter and is also a better classifier. Thus  get a more robust and effective technique. A new general method to estimate the runtime of robust regression algorithms is proposed. Using it  show, that the modified pbM-estimator runs 2 -3 times faster than the pbM-estimator. Experimental results of fundamental matrix estimation are presented demonstrating the correctness of the proposed analysis method and the advantages of the modified pbM-estimator."}]