• Journal of Broadcast Engineering
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• v.21 no.5
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• pp.782-791
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• 2016

The object segmentation algorithm from the background could be used for object recognition and tracking, and many applications. To segment objects, this paper proposes a method that refer to several initial frames with real-time processing at fixed camera. First we suggest the probability model to segment object and background and we enhance the performance of algorithm analyzing the color consistency and focus characteristic of camera for several initial frames. We compensate the segmentation result by using human skeleton characteristic among extracted objects. Last the proposed method has the applicability for various mobile application as we minimize computing complexity for real-time video processing.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.12-20
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• 2011

In this paper, we propose an efficient object detection and classification algorithm for video surveillance applications. Previous researches mainly concentrated either on object detection or classification using particular type of feature e.g., Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) etc. In this paper we propose an algorithm that mutually performs object detection and classification. We combinedly use heterogeneous types of features such as texture and color distribution from local patches to increase object detection and classification rates. We perform object detection using spatial clustering on interest points, and use Bag of Words model and Naive Bayes classifier respectively for image representation and classification. Experimental results show that our combined feature is better than the individual local descriptor in object classification rate.

• Journal of the Korean Graphic Arts Communication Society
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• v.21 no.2
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• pp.43-53
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• 2003

As information-communication network has been developed rapidly, internet users' circumstances also have been changed for the better, in result, more information can be applied than before. At this moment, there are many differences between real color and reappeared color on the CRT. When we observe a material object, our eyes perceive the multiplied form of light sources and nature spectral reflection. However, when the photographed signal is reappeared, illumination at that time of photographing and spectral reflection of a material object are converted into signal, and this converted RGB signal is observed on the CRT under another illumination. At this time, RGB signal is the reflected result of illumination at that time of photographing Therefore, this signal is influenced by the illumination at present, so it can be perceived another color. To accord the colro reflections of another color source, the study has been reported by S.C.Ahn$^{[1]}$, which study is about the color reapperarance system using neuron network. Furthermore, color reappearing method become independent of its circumstances has been reported by Y.Miyake$^{[2]}$. This method can make the same illuminations even if the observe circumstances are changed. To assume the light sources of observe circumstances, the study about color reappearing system using CCD sensor also have been studied by S.C.Ahn$^{[3]}$. In these studies, a population is fixed, first, on ab coordinates of CIE L${\ast}$a${\ast}$b${\ast}$. Then, color reappearing can be possible using every population and existing digital camera. However, the color is changed curvedly, not straightly, according to value's changes on the ab coordinates of CIE L${\ast}$a${\ast}$b. To solve these problems in this study, first of all, Labeling techniques are introduced. Next, basis color-it is based on Munsell color system-is divided into 10 color fields. And then, 4 special color- skin color, grass color, sky color, and gray-are added to the basis color. Finally, 14 color fields are fixed. After analyzing of the principle elements of new-defined-color fields' population, utility value and propriety value are going to be examined in 3-Band system from now on.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.48-54
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• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.65-72
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• 2015

In this paper, we proposed the color matching application based on smart phone which can help color blind people. For color blind people, the existing methods and applications supported color matching application which based on mobile. However, because the most research only showed the color value and color name through capture image of mobile camera, those cannot compare with capture image color of mobile camera and color of real object in real-time. To solve those problem, we proposed the color matching algorithm and developed the color matching application that can compare with color of mobile camera's capture image and color of real object in real-time, because the proposed application divides screen of smart phone into two parts and it show one part as capture image of smart phone camera and the other part as real-time camera image of smart phone. Color matching algorithm calculate cosine similarity using Red, Green, Blue, and Hue value of each image for real-time comparing and show matching result according to similarity value in real-time. To evaluate the performance of the proposed application, we tested a color matching experiment using the proposed application and the matching result was 98% success rate. Therefore, the proposed application will be a useful application which can help color blind people.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.71-79
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• 2017

Recently, Fire watching and dangerous substances monitoring system has been being developed to enhance various fire related security. It is generally assumed that fire flame extraction plays a very important role on this monitoring system. In this study, we propose the fire flame extraction method of Non-Residential Facilities based on core object extraction in image. A core object is defined as a comparatively large object at center of the image. First of all, an input image and its decreased resolution image are segmented. Segmented regions are classified as the outer or the inner region. The outer region is adjacent to boundaries of the image and the rest is not. Then core object regions and core background regions are selected from the inner region and the outer region, respectively. Core object regions are the representative regions for the object and are selected by using the information about the region size and location. Each inner region is classified into foreground or background region by comparing its values of a color histogram intersection of the inner region against the core object region and the core background region. Finally, the extracted core object region is determined as fire flame object in the image. Through experiments, we find that to provide a basic measures can respond effectively and quickly to fire in non-residential facilities.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.57-64
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• 2006

Tn this paper, We propose a user friendly object-based multimedia retrieval system using the HCNN(HippoCampus Neural Network. Most existing approaches to content-based retrieval rely on query by example or user based low-level features such as color, shape, texture. In this paper we perform a scene change detection and key frame extraction for the compressed video stream that is video compression standard such as MPEG. We propose a method for automatic color object extraction and ACE(Adaptive Circular filter and Edge) of content-based multimedia retrieval system. And we compose multimedia retrieval system after learned by the HCNN such extracted features. Proposed HCNN makes an adaptive real-time content-based multimedia retrieval system using excitatory teaming method that forwards important features to long-term memories and inhibitory learning method that forwards unimportant features to short-term memories controlled by impression.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.63-72
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• 2007

The traffic sign on the road should be easy to distinguishable even from far, and should be recognized in a short time. As traffic sign is a very important object which provides important information for the drivers to enhance safety, it has to attract human's attention among any other objects on the road. This paper proposes a new method of detecting the area of traffic sign, which uses attention module on the assumption that we attention our gaze on the traffic sign at first among other objects when we drive a car. In this paper, we analyze the previous studies of psycophysical and physiological results to get what kind of features are used in the process of human's object recognition, especially color processing, and with these results we detected the area of traffic sign. Various kinds of traffic sign images were tested, and the results showed good quality(average 97.8% success).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.375-387
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• 2012

This paper proposes a new real-time object tracking algorithm using particle filters with color and texture features in moving CCD camera images. If the user selects an initial object, this region is declared as a target particle and an initial state is modeled. Then, N particles are generated based on random distribution and CS-LBP (Centre Symmetric Local Binary Patterns) for texture model and weighted color distribution is modeled from each particle. For observation likelihoods estimation, Bhattacharyya distance between particles and their feature models are calculated and this observation likelihoods are used for weights of individual particles. After weights estimation, a new particle which has the maximum weight is selected and new particles are re-sampled using the maximum particle. For performance comparison, we tested a few combinations of features and particle filters. The proposed algorithm showed best object tracking performance when we used color and texture model simultaneously for likelihood estimation.