Earticle

With the aggravation of terrorist activities and frequent occuring of all kinds of car bomb attacks, social and public safety issues have become the focus of attention of the whole world. The vehicle chassis foreign body detection system developed in this paper is mainly used in all kinds of important occasions, to achieve real-time and safe detection of vehicles which is helpful to protect the lives and property of citizens, and to block the invasion and transimission of drugs and other harmful substances. Traditional vehicle chassis detection is carried out by security personnel with dedicated portable underbody detection tool, which is time-consuming, laborious and ineffective. In this paper, we construct a intelligent vehicle chassis image detection system. However, there is always distortion in the real time vehicle chassis image captured by multi-line CCD array which therefore needs to be corrected. This paper presents an image correction method for multi linear CCD. Firstly, Sobel differential operator is used to detect the binarized image of the edge of the vehicle chassis on horizontal and vertical directions. Secondly, we use Radon transform to detect angle of inclination of traveling distorted image captured by line scan cameras based on the results of edge detection, using shear transformation to correct binarized chassis image. Finally, a standard chassis image can be obtained based on image interpolation, the characteristic of Sobel operator and recapture the image. Experimental results show that this proposed method is simple and insensitive of stains and light. In addition, a standard chassis image can be obtained after completing the distortion correction.

The pre-processed remote sensing images are often polluted by Gaussian and salt-pepper noises. In order to solve this problem, a Sobel-TV based hybrid model is proposed to de-noise the pre-processed remote sensing images. It uses TV model to de-noise and uses Sobel algorithm to control smoothness of the image’ edge. This proposed method will not only efficiently remove image noise but also simultaneously reserves detail information such as edge and texture. Experimental results show the proposed algorithm achieves better SNR and SSIM compared with other methods. In terms of visual quality, the proposed algorithm can remove the noise of the images and preserve more details, which is important value to preprocess remote sensing image.

As a successor to H.264/AVC, High Efficiency Video Coding (HEVC) standard has an outstanding performance than before. To make the coding efficiency higher, one introduced new coding feature in HEVC is adding intra prediction modes up to 35 instead of 9 in H.264, but it increases calculation complexity. To save the coding time, this paper proposes a fast intra mode decision algorithm based on edge detection technology using sobel operator. Because of the principle that sobel operator can find the texture edge directly, the proposed algorithm can save large numbers of coding time with only a little BD-rate rising. Experimental results show that the proposed fast intra mode decision algorithm can save 38.8% computational complexity on average with a 1.6% BD-rate rise.

Aiming at the low accuracy of stereo matching algorithm caused by the larger gray change, an improved algorithm is proposed. Sobel operator is used to compute the gradient of pixels. Based on the gradient histogram, the adaptive thresholds are derived and the support window is determined automatically. The neighbor pixels are compared with the average of all pixels in the window instead of the center pixel to complete the census transform. The disparity image is abtained through searching the best match point in the left and right images. The hardware implementation with parallel procssing is elaborately designed to improve the capability of the large data processing and computational efficiency based on FPGA. The experimental results show that the improved scheme and hardware structure can obtain disparity map with higher accuracy and stronger robustness.

We propose a fast algorithm using Extreme Contour Point (ECP) to detect the angle of rotated images, is implemented by rotation feature of one covered frame image that can be applied to correct the rotated images like in image processing for real time applications, while CORDIC is inefficient to calculate various points like high definition image since it is only possible to detect rotated angle between one point and the other point. The two advantages of this algorithm, namely compatibility to images in preprocessing by using Sobel edge process for pattern recognition. While the other one is its simplicity for rotated angle detection with cyclic shift of two $1{\times}n$ matrix set without complexity in calculation compared with CORDIC algorithm. In ECP, the edge features of the sample image of gray scale were determined using the Sobel Edge Process. Then, it was subjected to binary code conversion of 0 or 1 with circular boundary to constitute the rotation in invariant conditions. The results were extracted to extreme points of the binary image. Its components expressed not just only the features of angle ${\theta}$ but also the square of radius $r^2$ from the origin of the image. The detected angle of this algorithm is limited only to an angle below 10 degrees but it is appropriate for real time application because it can process a 200 degree with an assumption 20 frames per second. ECP algorithm has an O ($n^2$) in Big O notation that improves the execution time about 7 times the performance if CORDIC algorithm is used.

특징점 추출 기반의 지문인식 알고리즘에서 방향정보는 이진화 과정 및 지문 분류, 인증에 쓰이므로 매우 중요하다. 모델기반의 방향정보를 추정하는 방법은 영상 전체에 있는 노이즈에 대하여 매우 강건하지만, 상처와 같이 국부적으로 존재하는 노이즈 정보의 추출에는 취약하다. Sobel Operator의 경우에는 국부적인 노이즈 요소에는 민감한 특정을 보인다. 따라서 본 논문에서는 모델링된 방향정보와 Sobel Operator 의 방향 정보를 백터에 의한 상호 결합하여 영상의 방향 정보를 개선하고 노이즈 블록을 추정하는 방법을 제안한다.