Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probability model. In contrast to the recent semi- supervised methods  do not utilise interest point detectors, but apply a supervised approach where local image features (landmarks) are annotated in a training set and therefore their appearance and spatial variation can be learnt. Our method enables working in purely probabilistic search spaces providing a MAP estimate of object location, and in contrast to the recent methods, no background class needs to be formed. Using the training set  can estimate pdfs for both spatial constellation and local feature appearance. By applying an inference bias that the largest pdf mode has probability one,  are able to combine prior information (spatial configuration of the features) and observations (image feature appearance) into posterior distribution which can be generatively sampled, e.g. using MCMC techniques. The MCMC methods are sensitive to initialisation, but as a solution,  also propose a very efficient and accurate RANSAC-based method for finding good initial hypotheses of object poses. The complete method can robustly and accurately detect and localise objects under any homography.
['Joni-Kristian Kamarainen', 'Miroslav Hamouz', 'Josef Kittler', 'Pekka Paalanen', 'Jarmo Ilonen', 'Alexander Drobchenko']
I am writing a research paper on a idea Object Localisation Using Generative Probability Model for Spatial Constellation and Local Image Features
[{"from": "human", "value": "I am writing a research paper on a idea Object Localisation Using Generative Probability Model for Spatial Constellation and Local Image Features"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probability model. In contrast to the recent semi- supervised methods  do not utilise interest point detectors, but apply a supervised approach where local image features (landmarks) are annotated in a training set and therefore their appearance and spatial variation can be learnt. Our method enables working in purely probabilistic search spaces providing a MAP estimate of object location, and in contrast to the recent methods, no background class needs to be formed. Using the training set  can estimate pdfs for both spatial constellation and local feature appearance. By applying an inference bias that the largest pdf mode has probability one,  are able to combine prior information (spatial configuration of the features) and observations (image feature appearance) into posterior distribution which can be generatively sampled, e.g. using MCMC techniques. The MCMC methods are sensitive to initialisation, but as a solution,  also propose a very efficient and accurate RANSAC-based method for finding good initial hypotheses of object poses. The complete method can robustly and accurately detect and localise objects under any homography."}]