量子化ニューロンチップ(QNC)を用いた時系列パターン認識ネットワーク : 物体形状の時系列データ変換(φ-S変換)による形状認識への適用 : 視聴覚,画像処理・コンピュータビジョン・マルチメディアおよび一般 : 視聴覚技術 : 画像処理・コンピュータビジョン

概要

論文の詳細を見る
We have developed a temporal pattern recognition networks with newly developed quantizer neuron chip(ONC) and applied it to the object recognition system. One of the biggest issues of an object recognition is the recognition with rotation invariance under a fluctuating noisy environment. The shape of the object is converted to a series of angles as a function of the circumference of the shape (φ-s data) and can be treated as a series of temporal patterns. The networks consist of a Multi Functional Layered Network(MFLN) with QNC and a layer of neurons with self feedback (self feedback layer). The self feedback layer unifies the temporal recognition results of networks with QNC during a certain period defined by the time constant of self feedback and this function can realize the function of selective attention to certain areas of a series of temporal patterns. As a result, the system realizes rotation invariance in recognition and we obtained 100% recognition accuracy of 50 trials with fluctuating noise taken by CCD camera.
社団法人映像情報メディア学会の論文
1993-10-18