@techreport{oai:ipsj.ixsq.nii.ac.jp:00238536,
 author = {原田, 晋平 and 原, 惇也 and 東, 広志 and 田中, 雄一 and Shimpei, Harada and Junya, Hara and Hiroshi, Higashi and Yuichi, Tanaka},
 issue = {1},
 month = {Aug},
 note = {イベントカメラは，各センサが独立して非同期に輝度の変化を検出することで，3 次元（2 次元座標 + 時間）のストリームデータを出力するカメラである．イベントカメラは，高時間分解能，低遅延，低電力消費，高ダイナミックレンジといった望ましい特性を持ち，様々な分野で利用されている．一方で，その感度の高さから，イベントストリームは多くのノイズを含む．本報告では，グラフ周波数特徴を用いたイベントカメラのノイズ除去手法を提案する．提案手法では，実際のイベント（リアルイベント）はノイズ由来のイベント（ノイズイベント）に比べて 3 次元空間上で密に分布するという知見に基づき，リアルイベントとノイズイベントを分類する．最初に，各イベントを頂点とするグラフを構築する．次に，冪乗法を用いて，グラフ作用素から Fiedler ベクトルを導出する．提案する冪乗法は，固有値分解に比べて少ない計算量で Fiedler ベクトルを得ることが出来る．最後に，Fiedler ベクトルに対して閾値処理を行うことでノイズ除去を行う．実験において，既存手法と比較して提案手法が優れたノイズ除去性能を示した．, Neuromorphic cameras, also known as event-based cameras, can detect changes in the environmental brightness asynchronously and independently for each pixel. They output the changes, i.e., events, as 3-D (2-D pixel coordinates + time) streaming data. While event-based cameras are used in many applications because of their desirable characteristics, such as high temporal resolution, low latency, low power consumption, and high dynamic range, their measurements contain considerable noise due to their high sensitivity. In this paper, we propose a simple yet effective denoising method for event-based cameras based on graph spectral features. We utilize the fact that the real events captured are often densely distributed in the streaming data while the noise events are spatiotemporally sparse. In the proposed method, we first construct a graph where nodes represent events and edges represent the spatiotemporal distance between events. Next, we calculate the Fiedler vector, which is the eigenvector of the graph operator associated with the second smallest eigenvalue. We extract real events based on the magnitudes of the obtained Fiedler vector. In the calculation of the Fiedler vector, we leverage a power method instead of the naive eigenvalue decomposition and thereby reduce its computational complexity. In experiments, we demonstrate that the proposed method effectively removes noise events from the raw events compared to conventional methods.},
 title = {グラフ周波数特徴を用いたイベントカメラのノイズ除去},
 year = {2024}
}