• International Journal of Contents
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• v.9 no.4
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• pp.16-21
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• 2013

Vision system is an indispensable part of constructing the chess-playing robot. Chessboard detection and pieces localization in the captured image of robot's camera are important steps for processes followed such as pieces recognition, move calculation, and robot controlling. We present a method for detecting the Janggi chessboard and pieces based on the edge and color feature. Hough transform combined with line extraction is used for segmenting the chessboard and warping it to form the rectangle shape in order to detect and interpolate the lines of chessboard. Then we detect the existence of pieces and their side by applying the saliency map and checking the color distribution at piece locations. While other methods either work only with the empty chessboard or do not care about the piece existence, our method could detect sufficiently side and position of pieces as well as lines of the chessboard even if the occlusion happens.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.486-489
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• 2012

본 논문에서는 인간의 주의시각(Human Visual Attention)에 기반하여 영상에서 가장 유용하다고 생각되는 관심 영역(Salient Region)을 새로운 방식으로 탐지해내고 관심-객체를 검출하는 방법을 제안한다. 제안하는 시스템은 인간의 주의시각 특성인 주파수와 명도, 색상 특징을 이용하는데, 먼저 주파수-명도 정보를 이용한 특징 지도(Feature map)와 색상 정보를 이용한 특징 지도를 각각 생성 한 후 영상의 특징 점(Saliency Point)을 추출한다. 이렇게 생성된 특징 지도와 특징 점을 이용하여 집중 윈도우의 위치와 크기를 결정하고 집중 윈도우 내에 특징 지도를 결합하여 관심 영역을 탐지하고 해당하는 영역에 대해 관심-객체를 추출한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.406-409
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• 2012

컬러 영상의 흑백화 기술은 영상 처리를 이용하는 다양한 분야에서 널리 활용되고 있는 기술이다. 하지만 기존의 일반적으로 사용되는 흑백화 기술은 컬러 영상에서의 색차 정보를 잃어버리는 문제점을 가지고 있다. 이러한 색차 정보 손실의 단점을 개선하기 위해 여러 방법들이 제안되었지만 대부분의 경우 최적의 결과를 얻기 위해서는 특정한 파라미터가 필요하다. 본 논문에서는 이러한 문제점을 해결하기 위해 영상의 Saliency map을 이용한 자동 흑백화 기술을 제안한다. 제안한 방법은 영상의 중요 정보를 통해 획득된 대표색상 정보를 이용해 기존의 방법과는 달리 특별한 파라미터의 입력이 필요하지 않는 장점을 가지고 있다. 실험결과는 제안한 방법이 기존 방법에 비해 매우 효율적임을 보여준다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.392-393
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• 2012

영상에서의 화질 평가는 학술적으로나 산업적으로나 중요한 문제이지만 지금까지 주로 쓰여온 PSNR 과 같은 방법은 실제 사람이 인식하는 평가에 잘 부합하지 못하는 문제점이 있었다. 본 논문에서는 모든 밝기 영역에서 영상을 평가할 수 있는 HDR-VDP2 와 주목도 지도를 결합하여 범용적으로 사용이 가능하며 성능도 뛰어난 화질 측정 방법을 제시하고, 다양한 주목도 지도에 대하여 그 성능과 화질 평가 성능 사이의 관계를 살펴본다. 구현에는 주목도 지도를 가중치로 사용함으로써 간단하게 더 좋은 성능의 화질 평가 시스템을 만들었고 이를 실험으로 보였다. 또한 주목도 지도의 성능과 화질 평가 시스템의 성능 사이에는 약한 양의 상관관계가 있는 것으로 나타났는데 주목도 지도와 함께 구조적 특징점들의 정보를 성능 평가 시스템에 포함시키면 더 좋은 결과를 얻을 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.158-168
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• 2024

There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.

• Journal of the Korea Computer Graphics Society
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• v.14 no.3
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• pp.1-9
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• 2008

This paper presents a novel optimization-based saliency-preserving method for converting color images to grayscale in a manner consistent with conventional approaches of black-and-white photographers. In black-and-white photography, a colored filter called a contrast filter has been commonly employed on a camera to lighten or darken selected colors. In addition, local exposure controls such as dodging and burning techniques are typically employed in the darkroom process to change the exposure of local areas within the print without affecting the overall exposure. Our method seeks a digital version of a conventional contrast filter to preserve visually-important image features. Furthermore, conventional burning and dodging techniques are addressed, together with image similarity weights, to give edge-aware local exposure control over the image space. Our method can be efficiently optimized on GPU. According to the experiments, CUDA implementation enables 1 megapixel color images to be converted to grayscale at interactive frames rates.

• Journal of the Ergonomics Society of Korea
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• v.31 no.6
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• pp.705-713
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• 2012

Objective: The aim of this study is to predict human visual target search using ACT-R cognitive architecture in real scene images. Background: Human uses both the method of bottom-up and top-down process at the same time using characteristics of image itself and knowledge about images. Modeling of human visual search also needs to include both processes. Method: In this study, visual target object search performance in real scene images was analyzed comparing experimental data and result of ACT-R model. 10 students participated in this experiment and the model was simulated ten times. This experiment was conducted in two conditions, indoor images and outdoor images. The ACT-R model considering the first saccade region through calculating the saliency map and spatial layout was established. Proposed model in this study used the guide of visual search and adopted visual search strategies according to the guide. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion: The proposed ACT-R model is able to predict the human visual search process in real scene images using salience map and spatial layout. Application: This study is useful in conducting model-based evaluation in visual search, particularly in real images. Also, this study is able to adopt in diverse image processing program such as helper of the visually impaired.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5563-5582
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• 2016

Parametric min-cuts is an object proposal algorithm, which can be used for accurate image segmentation. In parametric min-cuts, foreground seeds generation plays an important role since the number and quality of foreground seeds have great effect on its efficiency and accuracy. To improve the performance of parametric min-cuts, this paper proposes a new framework for foreground seeds generation. First, to increase the odds of finding objects, saliency detection at multiple scales is used to generate a large set of diverse candidate seeds. Second, to further select good-quality seeds, a two-stage cascaded ranking classifier is used to filter and rank the candidates based on their appearance features. Experimental results show that parametric min-cuts using our seeding strategy can obtain a relative small pool of proposals with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.769-786
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• 2016

Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.