Jongin Son

Real-time illumination invariant lane detection for lane departure warning system

Invariant property of lane color under various illuminations is utilized for lane detection.Computational complexity is reduced using vanishing point detection and adaptive ROI.Datasets for evaluation include various environments from several devices.Simulation demo demonstrate fast and powerful performance for real-time applications. Lane detection is an important element in improving driving safety. In this paper, we propose a real-time and illumination invariant lane detection method for lane departure warning system. The proposed method works well in various illumination conditions such as in bad weather conditions and at night time. It includes three major components: First, we detect a vanishing point based on a voting map and define an adaptive region of interest (ROI) to reduce computational complexity. Second, we utilize the distinct property of lane colors to achieve illumination invariant lane marker candidate detection. Finally, we find the main lane using a clustering method from the lane marker candidates. In case of lane departure situation, our system sends driver alarm signal. Experimental results show satisfactory performance with an average detection rate of 93% under various illumination conditions. Moreover, the overall process takes only 33ms per frame.

Illumination robust road detection using geometric information

Road detection is an important task in intelligent transportation system (ITS). Over the past few decades, several vision-based approaches for road detection have been proposed and most of them are based on color information. However, color information may result in false road detection under variation of illumination conditions. To deal with illumination problems, we propose an illumination invariant road detection method using geometric information. By incorporating geometric information with a color-based road probability map, the proposed method robustly detect road regions on real scene containing variation of illumination such as shadow and mixed artificial lights. Experimental results show that the proposed method outperforms the conventional methods.

A multi-vision sensor-based fast localization system with image matching for challenging outdoor environments

Robust feature point extraction to provide viewpoint invariances with virtual viewpoint.Robust feature descriptor to provide illumination invariances with modified binary pattern.Efficient computational model to reduce time complexity of localization system.Extensive experiments and simulation demos for objective evaluations. A sensor-based vision localization system is one of the most essential technologies in computer vision applications like an autonomous navigation, surveillance, and many others. Conventionally, sensor-based vision localization systems have three inherent limitations, These include, sensitivity to illumination variations, viewpoint variations, and high computational complexity. To overcome these problems, we propose a robust image matching method to provide invariance to the illumination and viewpoint variations by focusing on how to solve these limitations and incorporate this scheme into the vision-based localization system. Based on the proposed image matching method, we design a robust localization system that provides satisfactory localization performance with low computational complexity. Specifically, in order to solve the problem of illumination and viewpoint, we extract a key point using a virtual view from a query image and the descriptor based on the local average patch difference, similar to HC-LBP. Moreover, we propose a key frame selection method and a simple tree scheme for fast image search. Experimental results show that the proposed localization system is four times faster than existing systems, and exhibits better matching performance compared to existing algorithms in challenging environments with difficult illumination and viewpoint conditions.

Illumination invariant road detection based on learning method

Road detection is an essential and important component in intelligent transportation system (ITS). Generally, most road detection methods are sensitive to variation of illumination which results in increasing false detection rate. In this paper, we propose an illumination invariant road detection method to deal with variation of illumination. We adopt learning method to estimate illumination invariant direction which is specified to road surface. Once this direction is estimated, we can classify image pixel as road or not. Incorporating scene layout of road image, we reduce false positive detection rate outside the road. Experimental results on real road scenes show that the effectiveness of the proposed method.

Real-time rear obstacle detection using reliable disparity for driver assistance

HightlightsUtilizing three features such as disparity, super pixel segments and pixel-wise gradient.Computing the reliability of disparity from super pixel segments and pixel-wise gradient.Developing voting map to reduce time complexity of initial obstacle region.Superior performance with erroneous disparity information and in complex environments. A vision based real-time rear obstacle detection system is one of the most essential technologies, which can be used in many applications such as a parking assistance systems and intelligent vehicles. Although disparity is a useful feature for detecting obstacles, estimating a correct disparity map is a hard problem due to the matching ambiguity and noise sensitivity, especially in homogeneous regions. To overcome these problems, we leverage reliable disparities only for obstacle detection. A reliability factor is introduced to measure an inhomogeneity of the regions quantitatively. It is computed at each superpixel to consider the noise sensitivity of pixel-wise gradients and to assign similar reliability value within a same object. It includes two major components: firstly, In a feature extraction and combining stage, we extract three features from stereo images such as disparity, superpixel segments and pixel-wise gradient and compute the reliability of disparity from superpixel segments and the pixel-wise gradient. Secondly, In an obstacle detection stage, a disparity feature with reliability votes for localizing obstacles and dominant candidates in voting map are selected as initial obstacle region. The initial obstacle regions are expanded into their neighbor superpixels based on CIELAB color similarity and distance similarity between superpixels. Experimental results show satisfactory performance under various real parking environments. Its detection rate is at least 4% higher than those of other existing methods, and its false detection rate is more than 10% lower and thus, can be used for parking assistance system.

Fast affine-invariant image matching based on global Bhattacharyya measure with adaptive tree

Establishing visual correspondence is one of the most fundamental tasks in many applications of computer vision fields. In this paper we propose a robust image matching to address the affine variation problems between two images taken under different viewpoints. Unlike the conventional approach finding the correspondence with local feature matching on fully affine transformed-images, which provides many outliers with a time consuming scheme, our approach is to find only one global correspondence and then utilizes the local feature matching to estimate the most reliable inliers between two images. In order to estimate a global image correspondence very fast as varying affine transformation in affine space of reference and query images, we employ a Bhattacharyya similarity measure between two images. Furthermore, an adaptive tree with affine transformation model is employed to dramatically reduce the computational complexity. Our approach represents the satisfactory results for severe affine transformed-images while providing a very low computational time. Experimental results show that the proposed affine-invariant image matching is twice faster than the state-of-the-art methods at least, and provides better correspondence performance under viewpoint change conditions.

EMCCD color correction based on spectral sensitivity analysis

The sensor response function of a color camera is very essential to understand an overall camera imaging pipeline and to process captured images. It is also true when we characterize underlying imaging behaviors of the electron multiplying charge coupled device (EMCCD) camera, which was recently proposed to acquire color images in low-light-level conditions. Unlike existing CCD cameras, the EMCCD camera contains partially uni-modal spectral sensitivity functions (SSFs), some of which consists of two base (bi-modal) functions, thus often leading to a serious color distortion in existing characterization techniques. To address this problem, we propose a novel method that corrects output colors of the EMCCD camera by analyzing its partially uni-modal characteristics. Specifically, our goal is to correct such color distortion by adjusting bi-modal SSFs to be uni-modal. We remove the secondary region of bi-modal channels using a pre-calculated spectral sensitivity of the EMCCD camera. Experimental results demonstrate that the proposed method reduces the color distortion as well as enlarges a color gamut, which is crucial to color reproduction.

Image Color Registration for Illumination-Robust Pattern Recognition

Image-based pattern recognition techniques have attracted much attention in vision-based applications. Color-based methods have shown several benefits. However, due to color variations resulting from illumination changes, many color-based techniques have yet to demonstrate stable performance. For illumination-robust pattern recognition, we propose an image color registration method based on an image acquisition model. Since the image acquisition model is created using the variables related to an illumination condition, camera characteristics, and an object’s surface reflectance, the proposed method normalizes the image’s color by taking into account both the illumination and camera characteristics. To evaluate the performance of the proposed method in terms of illumination-robust pattern recognition, we perform both an image similarity test and a feature similarity test between images acquired under different illumination conditions. Through the experiments, the superiority and the usefulness of the proposed method was validated.