基于颜色和深度信息融合的目标识别方法  

Object recognition method by combining color and depth information

作　　者：吴鑫[1,2] 王桂英[1] 丛杨[2] 

机构地区：[1]沈阳农业大学信息与电气工程学院,沈阳110866 [2]中国科学院沈阳自动化研究所机器人学国家重点实验室,沈阳110016

出　　处：《农业工程学报》2013年第A01期96-100,共5页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金青年基金(611105013)资助

摘　　要：传统的机器视觉采用二维RGB图像,难以满足三维视觉检测的要求,深度图像能直接反映物体表面的三维特征,正逐渐受到重视。该文提出的方案将RGB和深度信息相结合,分割出物体所在区域,并利用梯度方向直方图(HOG,histograms of oriented gradients)分别提取RGB图像和深度图像特征信息。在分类算法上,该文采用k最邻近节点算法(k-NN)对特征进行筛选,识别出目标物体。试验结果表明,综合利用深度信息和RGB信息,识别准确率很高,此方案能够对物体和手势进行很好识别。The traditional machine vision with RGB image doesn＇t meet the requirements of the 3D visual inspection. The Range Image can reflect the 3D characteristics of the object surface directly, and is attracting much more attentions gradually. How to use the RGB and depth information for object recognition is the core issue, which would be studied in this paper. Firstly, based on the kinect color and depth information, the object recognition system was put forward in this paper. The kinect sensor was used to acquire the color and depth information of the target object and its background in recognition system. The information can be used to segment the object from the background. Then HOG feature descriptor was used to extract the target sample’s characteristics and establish the characteristic model. In the actual process of object recognition, the most similar templates category with k-NN algorithm was selected to achieve the goal of classification and recognition. In this paper’s scheme, depth and RGB image was comprehensively used. The target objects was segmented by Canny edge detection operator, and the depth image’s advantage that can reflect the object’s contour directly was made full use of. In terms of feature extraction, histograms of oriented gradients（HOG） had a good geometrical and optical invariance. The HOG descriptor was used to describe the objects’ features. The HOG descriptor was done some deformation to under the premise of keeping its core algorithm unchanged. And then the HOG was used to extract the image features from target object of any size. In the descriptor, the image was divided into 2 × 2 sub-images. Count of the original image itself, there were 5 sub-images together. Each sub-image was one block, and it can be divided into 2 × 2 cells. Two different quantization levels were used in each cell. The gradient distribution space of unsigned value （0 ° to 180 °） was divided into 9 parts, and the gradient distribution space of symbol value （0 ° to 360 °） was divide

关 键 词：物体识别 图像处理 图像分割 深度图像 RGB图像 K最邻近节点算法(k-NN) 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]