欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对难以获取完备的非可通行区域样本问题,为提高算法在不同场景的适应性,首次把可通行性检测看作单类分类问题,提出了基于one-class SVM的可通行区域检测算法.提出一种改进的融合颜色和纹理的特征提取方法,对各颜色分量进行离散余弦变换（DCT）变换,对DCT系数进行金字塔分解,用每个分解的均值和方差描述特征窗口.利用one-class SVM进行训练生成可通行区域的模式.实验表明,方法对新数据具有很好的识别能力,具有较高的检测精度和较低的误检率.
英文摘要：
For the difficulty in obtaining the complete non-traversable region samples,a traversable region detection method based on one-class SVM（support vector machine） is proposed to improve the adaptability of algorithms in different scenes.This article formulates traversability detection as a one-class classification problem for the first time.An improved feature extraction method is proposed with the fusion of color and texture.Image data of every color channels are transformed by discrete cosine transform（DCT）,then the DCT coefficients are decomposed using pyramid decomposition.Mean and variance in each decomposition are used to describe characteristic window.Traversable region pattern is generated by training the traversable samples using one-class SVM.Experiments show that the algorithm recognizes new data well,and performs with high detection accuracy and low abused detection rate.
同期刊论文项目
同项目期刊论文
Maximum inter-class and marginal discriminant embedding (MIMDE) for feature extraction and classific
Incremental embedding and learning in the local discriminant subspace with application to face recog
Reconstructive discriminant analysis: A feature extraction method induced from linear regression cla
Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain
A computational and theoretical analysis of local null space discriminant method for pattern classif
Improving the discriminant ability of local margin based learning method by incorporating the global
A visual-attention model using earth mover';s distance-based saliency measurement and nonlinear feat
期刊信息
位置：