@techreport{oai:ipsj.ixsq.nii.ac.jp:00082565,
 author = {北川, 正理 and 清水, 郁子 and Masamichi, Kitagawa and Ikuko, Shimizu},
 issue = {4},
 month = {May},
 note = {画像の特徴抽出手法のひとつであるFerns Descriptorは，インプリメントが容易でありマッチングが高速な手法であるが，前処理として行う学習に非常に計算時間がかかることが知られている．そこで，本研究では，Ferns Descriptorの学習時間をGPGPUを利用して高速化する．このとき，メモリアクセスやデータ転送が遅いことが問題になる．そこで，使用メモリを削減し，並列化を効率よく行うために，キーポイント数，Ferns数，クラスが含むパッチ数の3通りで並列化を行い，学習で扱うデータによってGPU内の処理をモジュール化し，特徴量を圧縮してメモリの効率化を図る．, Ferns descriptor is one of the feature extraction methods. It is easy to implement and its matching is very fast. However, the computational time for its learning is very high. In this paper, a method for fast learning of Ferns descriptor by GPGPU is proposed. Data transfer between GPU and CPU and memory access is very slow. To reduce the memory usage, three parallelization are tested by number of keypoints, by number of Ferns, and by number of patches. To reduce the data transfer between GPU and CPU by introducing the one dimensional indices for the image coordinate and controlling the device memory in GPU by dividing learning step of the Ferns descriptor into small steps.},
 title = {GPGPUによるFerns Descriptorの学習の高速化},
 year = {2012}
}