CAJ | 학술논문

三维图像分类能有效克服二维彩色图像分类易受光照变化、阴影、物体遮挡以及环境变化等因素的干扰。利用压缩感知的方法研究 Kinect 相机获取的带深度信息的 RGB-D 图像分类问题。该方法首先利用下采样和 PCA 的方法分别对 RGB 图像和深度图像进行特征提取；再将所提取的特征信息融合；然后利用压缩感知方法对融合后的特征信息进行稀疏分解并分类。最后，利用该方法对6类蔬菜、7类水果及文件夹和相机等共15类 RGB-D 图像进行分类实验，比较了压缩感知方法和 SVM分类器的 RGB-D 图像分类精度，并对比分析了深度信息对图像分类精度的影响。实验表明，压缩感知方法对 RGB-D 图像分类精度高于 SVM的精度，且加入深度信息的 RGB-D 图像分类精度高于 RGB 图像的精度。
삼유도상분류능유효극복이유채색도상분류역수광조변화、음영、물체차당이급배경변화등인소적간우。이용압축감지적방법연구 Kinect 상궤획취적대심도신식적 RGB-D 도상분류문제。해방법수선이용하채양화 PCA 적방법분별대 RGB 도상화심도도상진행특정제취；재장소제취적특정신식융합；연후이용압축감지방법대융합후적특정신식진행희소분해병분류。최후，이용해방법대6류소채、7류수과급문건협화상궤등공15류 RGB-D 도상진행분류실험，비교료압축감지방법화 SVM분류기적 RGB-D 도상분류정도，병대비분석료심도신식대도상분류정도적영향。실험표명，압축감지방법대 RGB-D 도상분류정도고우 SVM적정도，차가입심도신식적 RGB-D 도상분류정도고우 RGB 도상적정도。
3D images classification can effectively overcome the disadvantage of 2D colour images classification such as susceptible to the interferences of illumination changes,shadows,objects occlusion,environmental changes and other factors.In this paper,compressed sensingis used to study the classification of RGB-D images containing deep information gained by Kinect camera.First,the method uses downsampling and PCA to extract the features from RGB images and depth images separately.Secondly,the extracted features are fused.And then the sparse decomposition of the fused features is conducted by using compressed sensing and followed by classification.Finally,this method is used to make classification experiments on images in RGB-D dataset,including 6 kinds of vegetables,7 kinds of fruits,and the binder,the camera,counted 15 classes in total.The precision of RGB-D images classification using compressed sensing is compared with that of SVMclassifier;and the impact of depth information on image classification precision is compared and analysed as well.Experiments show that the classification precision of RGB-D images is higher by using compressed sensing than using SVM,and the classification precision of RGB-D images with depth information added is higher than that of RGB images.