• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.5
• /
• pp.550-557
• /
• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.4
• /
• pp.333-337
• /
• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.6
• /
• pp.617-625
• /
• 2017

In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

• Journal of Digital Convergence
• /
• v.14 no.6
• /
• pp.133-142
• /
• 2016

The purpose of this study is to test the effects of brand image and product similarity with the original on the attitude toward the counterfeit of prestige brand. Especially this study is focused on the moderating effect of perceived bland globalness (PBG) and the influence of the original attitude on the counterfeit one. The results of this study are as follows 1) brand image has a positive impact on the counterfeit attitude as well as the original one. And symbolic image is more positive than functional image on the both of them. 2)The moderating effect of PBG appeared between brand image and attitude. Namely, there is no statistical difference according to PBG in the effect of brand image on the original attitude. But the effect of brand image on the counterfeit attitude is higher in case of high PBG. 3) Product similarity of the counterfeit with the original has a positive impact on only the counterfeit attitude. And the similarity of perceived quality is more positive than appearance similarity on the counterfeit attitude. 4) The original attitude has a positive impact on the counterfeit one.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.21 no.6
• /
• pp.959-969
• /
• 1997

This research is intended to help the development of new products and marketing strategies studying consumers' knowledge level and attitude towards original brand and the extension attitude related to the brand image. The detailed purposes of this study are as follow: First, it is to measure consumers' knowledge and attitude towards original brand. Second, it is to clarify the dimensions of the image about the original brand and extended brand product which is perceived by the consumer. Third, it is to meassure image consistency and product similarity between the original brand and extended product. The sample group consisted of female college students 393 in Seoul. Stratified sampling, based on major and grade of sturients and the structure of the college they were attending was used as sampling method. Questionnaires, which were selected from literature and proceeding researches published in Korea and abroad, were modified for this study, SAS Package was used for data analysis. The results observed in this study were as follow: 1. Consumers' knowledge level about original brand showed high among students majoring in clothing-related subjects and their general attitude proved to be positive. 2. Image factors of original brand were classified to dignity factor, personality/modernity factor, femininity factor, and ornamentation factor. Lipstick is considered to have high image consistency and product similarity. 3. Comparing the attitude before and after extension, extension to lipstick which showed high image consistency and product similarity received more positive reactions than one to bed cover sheet. 4. It was founded that although knowledge and attitude towards original brand with image consistency had no influence on the extension attitude, knowledge and attitude without image consistency influence the extension attitude.

• Journal of KIISE
• /
• v.44 no.9
• /
• pp.992-1004
• /
• 2017

To assess image quality accurately, an image quality assessment (IQA) metric is required to reflect the human visual system (HVS) properly. In other words, the structure, color, and contrast ratio of the image should be evaluated in consideration of various factors. In addition, as mobile embedded devices such as smartphone become popular, a fast computing speed is important. In this paper, the proposed IQA metric combines color similarity, gradient similarity, and phase similarity synergistically to satisfy the HVS and is designed by using optimized pooling and quantization for fast computation. The proposed IQA metric is compared against existing 13 methods using 4 kinds of evaluation methods. The experimental results show that the proposed IQA metric ranks the first on 3 evaluation methods and the first on the remaining method, next to VSI which is the most remarkable IQA metric. Its computing speed is on average about 20% faster than VSI's. In addition, we find that the proposed IQA metric has a bigger amount of correlation with the HVS than existing IQA metrics.

• ETRI Journal
• /
• v.24 no.1
• /
• pp.23-30
• /
• 2002

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.111-115
• /
• 2020

In this paper, I proposed an effective technique for accurately predicting pixel values using edge components. Adjacent pixel values are similar to each other. That is, generally, similarity exists between adjacent pixels in an image. In the proposed algorithm, edge components are detected using the surrounding pixels in the first step, and pixel values are estimated using the edge components in the second step. Therefore, the prediction accuracy of the pixel value is improved and the prediction error is reduced. Pixel value prediction is a necessary technique for various applications such as image magnification and confidential data concealment. Experimental results show that the proposed method has higher prediction accuracy and fewer prediction error. Therefore, the proposed technique can be effectively used for applications such as image magnification and confidential data concealment.

• Radiation Oncology Journal
• /
• v.35 no.2
• /
• pp.101-111
• /
• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• Journal of the Semiconductor & Display Technology
• /
• v.7 no.4
• /
• pp.13-18
• /
• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.