• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.912-919
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• 2009

A novel algorithm using 2D tensor voting and edge-based approach is proposed for text detection in natural scene images. The tensor voting is used based on the fact that characters in a text line are usually close together on a smooth curve and therefore the tokens corresponding to centers of these characters have high curve saliency values. First, a suitable edge-based method is used to find all possible text regions. Since the false positive rate of text detection result generated from the edge-based method is high, 2D tensor voting is applied to remove false positives and find only text regions. The experimental results show that our method successfully detects text regions in many complex natural scene images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.773-781
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• 2013

The Hough transform for line detection is widely used in many machine vision applications due to its robustness against data loss and distortion. However, it is not appropriate for real-time embedded vision systems, because it has inefficient computation structure and demands a large number of memory accesses. Thus, this paper proposes an improved voting scheme of the Hough transform, and then applies this scheme to a Hough transform hardware architecture so that it can provide real-time performance with less hardware resource. The proposed voting scheme reduces computation overhead of the voting procedure using correlation between adjacent pixels, and improves computational efficiency by increasing reusability of vote values. The proposed hardware architecture, which adopts this improved scheme, maximizes its throughput by computing and storing vote values for many adjacent pixels in parallel. This parallelization for throughput improvement is accomplished with little hardware overhead compared with sequential computation.

• Smart Media Journal
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• v.1 no.2
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• pp.23-33
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• 2012

In this paper, the characteristics of tensor voting, which are used extensively in image processing and computer vision, have been surveyed. In general, tensor voting can infer the structural features like junctions, curves, regions and surfaces from n-dimensional data given as points, curve elements or surface patch elements. Currently various perceptual grouping methods based on such structural inference are studied and are used for diverse applications on images or scenes. Tensor voting provides robustness to noises and demonstrates itself efficient in many applications.

• Smart Media Journal
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• v.4 no.4
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• pp.18-23
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• 2015

In this paper, we propose a method for detecting the number of clusters. This method can improve the performance of a gaussian mixture model function in conventional markov random field method by using the tensor voting. The key point of the proposed method is that extracts the number of the center through the continuity of saliency map of the input data of the tensor voting token. At first, we separate the foreground and background region candidate in a given natural images. After that, we extract the appropriate cluster number for each separate candidate regions by applying the tensor voting. We can make accurate modeling a gaussian mixture model by using a detected number of cluster. We can return the result of natural binary text image by calculating the unary term and the pairwise term of markov random field. After the experiment, we can confirm that the proposed method returns the optimal cluster number and text binarization results are improved.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.138-141
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• 2010

본 논문에서는 고안전성 연산제어 장치의 열차제어시스템에 대한 적용성을 평가하여 그 결과를 분석하였다. 고안전성 연산제어 장치의 적용대상으로 열차제어시스템에서 가장 대표적인 지상 ATP 시스템을 선정하였다. 지상 ATP(Automatic Train Protection) 시스템은 다수의 차상 ATP 시스템과 통신하여 각 열차의 위치를 확인하고, 각 열차마다 안전 운행에 필요한 정보이동허가, 제한 속도 등의 열차정보를 전송하는 열차제어시스템의 하나이다. 적용대상 열차제어시스템(지상 ATP)의 고안전성 연산처리 장치의 평가항목으로 입력처리시간, 보팅 성공률, 보팅 용량, 최대 입력처리 개수를 정하였으며, DSV보드 LVDS 전송성능, DSV 메모리 공유 및 보팅성능, 최대 입력처리성능 및 보팅성공률을 시험하여 고안전성 연산처리장치의 적용성을 평가였다.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.236-241
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• 1999

산업이 발전해 가며 공장 자동화를 하는 회사는 나날이 늘어가고 있으며 현재 산업 전반에 걸쳐 자동화 시스템을 채택하지 않는 곳을 찾아보기 어렵고 이 시스템의 시험 및 운영자 교육용으로 시뮬레이터에 대한 관심은 높아 가고 있는 실정이다. 여기에서는 한국전력에서 사용하고 있는 터빈 제어 삼중화 시스템이 어떠한 보팅 기법들을 적용하여 설계되었으며 그 기능이 어떻게 구현되었는지를 실제 제품의 사례분석을 통해 검토해 보고자 한다. 검토 대상은 삼중화 제어기에 대해서만 시행할 예정이다. 이는 실제 현장이 경제성을 수반한 제어기의 신뢰, 이용, 안정, 수행, 지속, 시험, 확실성 등을 원하고 있으며 또한 시뮬레이터를 설계하는 사람들에게 삼중화 주제어기에 대한 비교(MARK-V, Triconex-TMR, GE-Fanuc, Woodward-Micronet) 소개함으로서 더욱더 현장과 가까운 시뮬레이터를 설계할 수 있도록 하기 위함이다.

• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.10-17
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• 2012

In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. In this paper, we propose a method for creating 3D virtual scenes based on 2D image that is completely automatic and requires only a single scene as input data. The proposed method is similar to the creation of a pop-up illustration in a children's book. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting to an image segmentation. The tensor voting is used based on the fact that homogeneous region in an image is usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. And then, our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. The experimental results show that our method successfully segments coarse regions in many complex natural scene images and can create a 3D pop-up model to infer the structure information based on the segmented region information.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.404-404
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• 2012