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Lane-mark detection is one of the most important parts of the intelligent transportation system (ITS). Cameras mounted in front of automobiles are used to capture the road scene to detect lane-marks. This paper proposes a new lane-mark extraction algorithm that consists of six parts. We first determine the regions of interest (ROIs) of captured images and investigate their boundaries. Then, we divide the boundary image into subimages to calculate the local edge-orientation of each block and remove edges with abnormal orientations. After that, the multi-adaptive thresholding method is employed for each subimage so that the system can work efficiently for an image that has different contrasts to indicate sub-regions for lane-marks. We then verify these candidate lane-mark edges and fit the lane-marks using either straight or curved line models. A Kalman filter is also applied to track them. The proposed method is evaluated in several situations, including in bad weather conditions and in the presence of shadow effects, obstacles such as wipers, or road signs on the road. The results show that the proposed method can detect the lane-marks in real time for various complex environments.

High demand 3-D scenes on embedded systems draw the developers’ attention to use the whole resources of current low-power processors and add dedicated hardware as a graphic accelerator unit to deal with real-time realistic scene rendering. Photon mapping, as one of the most powerful techniques to render highly realistic 3-D images by high amounts of floating-point operations, is very time-consuming. To use the advantages of multiprocessor systems to make 3-D scenes, parallel photon-mapping rendering on a homogeneous multiprocessor SoC (MPSoC) platform along with a mesh NoC by an adaptive wormhole routing method to communicate packets among cores is proposed in this paper. To make efficient use of the MPSoC platform to carry out photon-mapping rendering, many methods concerning the increase of load balancing, the efficient use of memory, and the decrease of communication cost to achieve a scalable application are explored in this paper. The resulting MPSoC platform is verified and evaluated by cycle-accurate simulations for different sizes of the mesh NoC. As expected, the proposed methods can obtain excellent load balancing and achieve a maximum of 44.3 times faster on an 8-by-8 MPSoC platform than on a single-core MPSoC platform.

A new method for detecting text in video images is proposed in this article. Variations in background complexity, font size and color, make detecting text regions in video images a difficult task. A pyramidal scheme is utilized to solve these problems. First, two downsized images are generated by bilinear interpolation from the original image. Then, the gradient difference of each pixel is calculated for three differently sized images, including the original one. Next, three K-means clustering procedures are applied to separate all the pixels of the three gradient difference images into two clusters: text and non-text, separately. The K-means clustering results are then combined to form the text regions. Thereafter, projection profile analysis is applied to the Sobel edge map of each text region to determine the boundaries of candidate text regions. Finally, we identify text candidates through two verification phases. In the first verification phase, we verify the geometrical properties and texture of each text candidate. In the second verification phase, statistical characteristics of the text candidate are computed using a discrete wavelet transform, and then the principal component analysis is further used to reduce the number of dimensions of these features. Next, the optimal decision function of the support vector machine, obtained by sequential minimal optimization, is applied to determine whether the text candidates contain texts or not.