• Journal of Korea Multimedia Society
• /
• v.19 no.6
• /
• pp.979-989
• /
• 2016

Traversable region detection is an essential step for advanced driver assistance systems and self-driving car systems, and it has been conducted by detecting lanes from input images. The performance can be unreliable, however, when the light condition is poor or there exist no lanes on the roads. To solve this problem, this paper proposes an algorithm which utilizes the information about the number of lanes and texture analysis. The proposed algorithm first specifies road region candidates by utilizing the number of lanes information. Among road region candidates, the road region is determined as the region in which texture is homogeneous and texture discontinuities occur around its boundaries. Traversable region is finally detected by dividing the estimated road region with the number of lanes information. This paper combines the proposed algorithm with a lane detection-based method to construct a system, and simulation results show that the system detects traversable region even on the road with poor light conditions or no lanes.

• Journal of Advanced Navigation Technology
• /
• v.6 no.1
• /
• pp.27-36
• /
• 2002

As the number of autonomous vehicles is increased, drivers are trending toward constant interests in detecting distance between vehicles in close-range and maintaining the distance between forward and backward vehicles for drivers' safety. In this paper, we propose a method of detecting the distance between two vehicles by computing the disparity of the close-rang vehicle using stereo vision. The boundary of the vehicle is obtained by using the modified wavelet transform which has multi-resolution characteristics. Then the disparity between left and right images is computed using coarse-to-fine method and histogram matching. Here we transform the left-right stereo images through 3-steps using the modified wavelet for maintaining the original resolution. An experimental result showed that the proposed method had 4.65% in total error rate.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1287-1292
• /
• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• ETRI Journal
• /
• v.39 no.2
• /
• pp.151-162
• /
• 2017

With the remarkable growth in rich media in recent years, people are increasingly exposed to visual information from the environment. Visual information continues to play a vital role in rich media because people's real interests lie in dynamic information. This paper proposes a novel discrete dynamic swarm optimization (DDSO) algorithm for video object tracking using invariant features. The proposed approach is designed to track objects more robustly than other traditional algorithms in terms of illumination changes, background noise, and occlusions. DDSO is integrated with a matching procedure to eliminate inappropriate feature points geographically. The proposed novel fitness function can aid in excluding the influence of some noisy mismatched feature points. The test results showed that our approach can overcome changes in illumination, background noise, and occlusions more effectively than other traditional methods, including color-tracking and invariant feature-tracking methods.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.2
• /
• pp.435-442
• /
• 2018

This study implemented an FPGA-based system to extract PCB defect patterns. The FPGA-based system can perform pattern matching at high speed for vision automation. An image processing library that is used to extract defect patterns was also implemented in IPs to optimize the system. The IPs implemented are Camera Link IP, Histogram IP, VGA IP, Horizontal Projection IP and Vertical Projection IP. In terms of hardware, the FPGA chip from the Vertex-5 of Xilinx was used to receive and handle images that are sent from a digital camera. This system uses MicroBlaze CPU. The image results are sent to PC and displayed on a 7inch TFT-LCD and monitor.

• Proceedings of the Korean Information Science Society Conference
• /
• 1998.10c
• /
• pp.384-386
• /
• 1998

The use of hand gesture provides an attractive alternative to cumbersome interface devices for human-computer interaction(HCI). Many methods hand gesture recognition using visual analysis have been proposed such as syntactical analysis, neural network(NN), Hidden Markov Model(HMM) and so on. In our research, a HMMs is proposed for alphabetical hand gesture recognition. In the preprocessing stage, the proposed approach consists of three different procedures for hand localization, hand tracking and gesture spotting. The hand location procedure detects the candidated regions on the basis of skin-color and motion in an image by using a color histogram matching and time-varying edge difference techniques. The hand tracking algorithm finds the centroid of a moving hand region, connect those centroids, and thus, produces a trajectory. The spotting a feature database, the proposed approach use the mesh feature code for codebook of HMM. In our experiments, 1300 alphabetical and 1300 untrained gestures are used for training and testing, respectively. Those experimental results demonstrate that the proposed approach yields a higher and satisfying recognition rate for the images with different sizes, shapes and skew angles.

• The Journal of the Korea Contents Association
• /
• v.6 no.12
• /
• pp.21-28
• /
• 2006

In a face detection system based on template matching, histogram equalization or log transform is applied to an input image for the intensity normalization and the image improvement. It is known that they are noneffective in improving an image with intensity distortion by illumination variation. In this paper, we propose an efficient image improvement method using a simple regression analysis combined with a bilateral symmetry average for images with intensity distortion by illumination variation. Experimental results show that our method delivers the detection performance better than previous methods and also remarkably reduces the number of face candidates.

• Journal of Broadcast Engineering
• /
• v.1 no.1
• /
• pp.74-83
• /
• 1996

In this paper, an effective color image retrieval algorithm is proposed based on the graph representation. Also we propose a color constancy algorithm to remove the effect of illumination change. Illumination condition of an image can be transformed to that of reference image using the proposed color constancy algorithm, so that the effect of dirrerent lighting is significantly alleviated. Then, we represent a color image as a graph with several nodes and edges in the histogram space, and finally two images are matched by compared two graphs representing them. The simulation results show that the proposed 3-step algorithm performs well for various conditions, including different lighting, translation, rotation, and scaling of the object in the image. In addition, the proposed algorithm is very fast compared to the geometry-based matching technique.

• Journal of Broadcast Engineering
• /
• v.2 no.1
• /
• pp.74-83
• /
• 1997

We have proposed a composite feature measure which combines the color and shape features of an image for image retrieval. We improved the performance of retrieval based on the efficient color quantization using the Lloyd-Max quanizer and on the Histogram matrix matching method which considers the spatial correlation of quantized color group. We also supplemented the color information using shape information with the Improved Moment Invarlants. We have tested our technique on Image database consisting of 200 actual trademark images. Our experimental results showed that our approach improved the performance compared to the previous method under the various situations such as rotation images, translation images, noise added images, gamma corrected images and so on. The efficiency of retrieval is found to be very high and experimental results are

• Journal of Biomedical Engineering Research
• /
• v.11 no.2
• /
• pp.217-226
• /
• 1990

This paper presents a new algorithm that extracted lung region in X-ray and enhanced the region. With a lung region that was extracted by histogram threshold value, it was diffi cult to detect perfect lung boundary. Therefore we presented perfect lung boundary detection method using apex detection and apex region restoration. Also, by applying modified equalization algorithm and presented function to inside of lung region, we want to give help to automatic diagnosis In X-ray chest image. Presented main line trace algorithm gave good result in detection of lung boundary And, as apex detection method using lung row and column gray level average value found more correct place of lung than the rpethod of prior algorithm, we succeeded perfect lung region detection, Also, presented function that had lung region's gray level distribution characteristic was very effective to image enhancement.