RGB Vector Space Research Articles

Registration of Visible and Infrared Images Based on Gradient Information

Multi-modality image registration is very challenging due to disparate imaging theory of sensors. Extraction of similar features and evaluation of algorithms have been two key difficulties. In this paper, gradient of RGB vector space, as a similar image feature, is shown the viability of using for the registration of infrared and visible still stereo pairs. Based on adaptive support-window algorithm, a novel method, which formulates the registration problem as correspondences between gradients of RGB vector space and obtains the best matching by minimizing gradient difference in sliding correspondence support-windows, is proposed. Evaluation experiments demonstrate high rates of successful registration by yielding qualitative and quantitative results.

SURF and LA with RGB Vector Space Based Detection and Monitoring of Manholes with an Application to Tri-Rotor UAS Images

SURF and LA with RGB Vector Space Based Detection and Monitoring of Manholes with an Application to Tri-Rotor UAS Images

Local Stereo Matching Based on Information Entropy of Image

Adaptive support-window algorithm is one of the simplest local algorithms for stereo matching. An important problem for adaptive support-window algorithm is to determine the appropriate support-window size, which is always hard to do and limits the validity of adaptive support-window algorithm. An appropriate support-window size must be selected adaptively based on image features. In this paper, information entropy of image is defined for stereo matching in the RGB vector space. Based on adaptive support-window, a new support-window selection algorithm, which uses information entropy of image to quantify image features such as illumination color and number of object contained in an image, is proposed. Experimental results evaluated on the Middlebury stereo benchmark show that our algorithm outperforms the conventional adaptive support-window algorithms.

Stereo matching algorithm based on illumination normal similarity and adaptive support weight

For the purpose of representing the feature of the gray image, illumination normal of pixels in a two-dimensional gray image plane is proposed, which can reflect the high-frequency information of the gray image. In order to get an accurate dense disparity map based on the adaptive support weight (ASW) approach in RGB vector space, a matching algorithm is proposed that combines the illumination normal similarity, gradient similarity, color similarity, and Euclidean distance similarity to compute the corresponding support weights and dissimilarity measurements. After testing by the Middlebury stereo benchmark, the result of the proposed algorithm shows more accurate disparity than many state-of-the-art stereo matching algorithms.

Stereo matching based on adaptive support-weight approach in RGB vector space

Gradient similarity is a simple, yet powerful, data descriptor which shows robustness in stereo matching. In this paper, a RGB vector space is defined for stereo matching. Based on the adaptive support-weight approach, a matching algorithm, which uses the pixel gradient similarity, color similarity, and proximity in RGB vector space to compute the corresponding support-weights and dissimilarity measurements, is proposed. The experimental results are evaluated on the Middlebury stereo benchmark, showing that our algorithm outperforms other stereo matching algorithms and the algorithm with gradient similarity can achieve better results in stereo matching.

Curve path detection of unstructured roads for the outdoor robot navigation

Path planning is a crucial problem in the application of mobile robot and autonomous vehicle. Recently most of methods only achieve reliable results in some particular structured environments. In this paper, we describe a new navigation path detection algorithm in an unstructured environment. Natural frames are analyzed in RGB vector space to research the feasibility of curve path detection of unstructured roads. Perceptual color clustering and morphological image processing have been used as pre-processing to obtain the segmented path. The pixels of the segmented image are analyzed from right to left line-by-line to find the middle points of the path. In order to obtain the optimal navigation path, the least-squares curve fitting method has been used in our research. At last a new method of obtaining the navigation parameters is given. The experimental result shows that our algorithm has high speed and remarkable precision. The navigation parameters in our algorithm can control the robot movement well.

Image retrieval based on the texton co-occurrence matrix

This paper put forward a new method of co-occurrence matrix to describe image features. This method can express the spatial correlation of textons. During the course of feature extracting, we have quantized the original images into 256 colors and computed color gradient from the RGB vector space, and then calculated the statistical information of textons to describe image features. Image retrieval experimental results have shown that our proposed method has the discrimination power of color, texture and shape features, the performances are better than that of GLCM and CCG.