This paper presents an approach of model-oriented road detection based on trapezoidal model proposed by H. Jeong, et al and fuzzy Support Vector Machine (SVM). Firstly, the frames ex-tracted from the video are preprocessed by Pulse Coupled Neural Network (PCNN), and then handled by Kalman filter and Expectation Maximization (EM) algorithms. Next, according to the road's dif-ferent feathers, using fuzzy algorithm chooses a corresponding SVM for further lane detection, and then using morphological filters obtains the final detecting result. For different types of roads, this method uses fuzzy algorithm to choose different SVMs. Furthermore, in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations. Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong, et al..
图像压缩的视觉有效性直接与其所保留的视觉重要信息有关。心理生理学实验结果显示,人眼对图像的边缘、平滑、纹理区域的敏感度不同,同时对小波分解后不同子带部分小波系数的敏感度也不同。本文据此提出了一种新的低比特率小波图像压缩方法。该算法对图像不同区域小波系数进行不同的量化,同时对图像小波分解不同子带系数进行视觉重要性加权,保证优先传输视觉上最重要的系数。实验表明,本文算法适合于低比特率的图像压缩,将其嵌入EZW编码算法中,与传统的EZW算法相比,重构图像的主观视觉质量评价指标VIF(visual information fidelity)值更高,具有更清晰的视觉效果。
A novel feature fusion method is proposed for the edge detection of color images. Except for the typical features used in edge detection, the color contrast similarity and the orientation consistency are also selected as the features. The four features are combined together as a parameter to detect the edges of color images. Experimental results show that the method can inhibit noisy edges and facilitate the detection for weak edges. It has a better performance than conventional methods in noisy environments.
