Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Vitronectin modulates human mesenchymal stem cell response to insulin-like growth factor-I and transforming growth factor beta 1 in a serum-free environment

The use of animal sera for the culture of therapeutically important cells impedes the clinical use of the cells. We sought to characterize the functional response of human mesenchymal stem cells (hMSCs) to specific proteins known to exist in bone tissue with a view to eliminating the requirement of animal sera. Insulin-like growth factor-I (IGF-I), via IGF binding protein-3 or -5 (IGFBP-3 or -5) and transforming growth factor-beta 1 (TGF-beta(1)) are known to associate with the extracellular matrix (ECM) protein vitronectin (VN) and elicit functional responses in a range of cell types in vitro. We found that specific combinations of VN, IGFBP-3 or -5, and IGF-I or TGF-beta(1) could stimulate initial functional responses in hMSCs and that IGF-I or TGF-beta(1) induced hMSC aggregation, but VN concentration modulated this effect. We speculated that the aggregation effect may be due to endogenous protease activity, although we found that neither IGF-I nor TGF-beta(1) affected the functional expression of matrix metalloprotease-2 or -9, two common proteases expressed by hMSCs. In summary, combinations of the ECM and growth factors described herein may form the basis of defined cell culture media supplements, although the effect of endogenous protease expression on the function of such proteins requires investigation.

Facial feature detection for in-car environment

Acoustically, vehicles are extremely noisy environments and as a consequence audio-only in-car voice recognition systems perform very poorly. Seeing that the visual modality is immune to acoustic noise, using the visual lip information from the driver is seen as a viable strategy in circumventing this problem. However, implementing such an approach requires a system being able to accurately locate and track the driver’s face and facial features in real-time. In this paper we present such an approach using the Viola-Jones algorithm. Using this system, we present our results which show that using the Viola-Jones approach is a suitable method of locating and tracking the driver’s lips despite the visual variability of illumination and head pose.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.