近年来,智能机器人技术逐步成熟,以目标识别为代表的机器视觉技术是其核心。现有目标识别算法通常先根据颜色信息分割场景,再提取特征以识别目标。但对于颜色信息比较复杂的场景,往往存在过分割现象,影响后续目标识别。针对这一问题,提出一种基于局部特征的多目标图像分割算法。该算法使用双目摄像头采集场景图像,对场景图像进行预处理,同时通过立体匹配得到场景的深度信息;结合深度信息确定目标区域;设计动态阈值的尺度不变特征变换(SIFT)算法以提取目标区域的局部特征,将局部特征转化为特征约束;基于区域约束、特征约束和空间信息组成的特征向量进行聚类分割,得到最终分割结果,同时实现对每个目标区域的识别。实验结果表明,在颜色特征复杂的场景中,本文算法的整体误差率小于10%,与已有算法相比降低了15%以上。

Intelligent robot has recently matured in industry, whose core technology is machine vision, especially object recognition. In existing object recognition methods, scenes are segmented based on color, and features are then extracted to recognize objects. However, over segmentation exists for scenes with complex color features, which influences subsequent object recognition process. To deal with the over segmentation problem, a multi-object image segmentation algorithm based on local features is proposed, which uses binocular camera to collect scene images. Firstly, the scene image is preprocessed. The depth information of the scene is then obtained by stereo matching, and is used to determine the target area. Secondly, the local features of the target region are extracted by a scale-invariant feature transform (SIFT) algorithm with dynamic threshold, and the local features are then transformed into feature constraints. Finally, the feature vectors, which are based on region constraint, feature constraint and spatial information, are used for clustering segmentation to obtain the final segmentation result. Simultaneously, each object region is recognized. The experiment results show that the overall error rate of the proposed algorithm is less than 10% for a scene with complex color features, and is reduced by 15% compared with those of the existing algorithms.