• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.47-52
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• 2006

This paper proposes a method of content-based image retrieval using region feature vector in order to overcome disadvantages of existing color histogram methods. The color histogram methods have a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV space and quantize hue factor being purecolor information and calculate histogram and then use thus for retrieval feature that is robust in brightness, movement, and rotation. Also we solve an insufficient part that is the most serious problem in color histogram methods by dividing an image into sixteen regions and then comparing each region. We improve accuracy by edge and DC of DCT transformation. As a result of experimenting with 1,000 color images, the proposed method has showed better precision than the existing methods.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.176-188
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• 2022

In this paper, we propose a compact method for anthropomorphism that uses Deep Convolutional Neural Networks (DCNN) to detect the similarities between a human face and an animal face. We also apply texture feature-based morphing between them. We propose a basic texture feature-based morphing system for morphing between human faces only. The entire anthropomorphism process starts with the creation of an animal face classifier using a parallel DCNN that determines the most similar animal face to a given human face. The significance of our network is that it contains four sets of convolutional functions that run in parallel, allowing it to extract more features than a linear DCNN network. Our employed texture feature algorithm-based automatic morphing system recognizes the facial features of the human face and takes the Control Points automatically, rather than the traditional human aiding manual morphing system, once the similarity was established. The simulation results show that our suggested DCNN surpasses its competitors with a 92.0% accuracy rate. It also ensures that the most similar animal classes are found, and the texture-based morphing technology automatically completes the morphing process, ensuring a smooth transition from one image to another.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2393-2396
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• 2005

We present a method to detect on-road vehicle using geometric invariant of feature points on side planes of the vehicle. The vehicles are assumed into a set of planes and the invariant from motion information of features on the plane segments the plane from the theory that a geometric invariant value defined by five points on a plane is preserved under a projective transform. Harris corners as a salient image point are used to give motion information with the normalized correlation centered at these points. We define a probabilistic criterion to test the similarity of invariant values between sequential frames. Experimental results using images of real road scenes are presented.

• Journal of Soil and Groundwater Environment
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• v.27 no.1
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• pp.50-59
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• 2022

There are several methods for separating the baseflow from the hydrograph, and graphical methods (GM) have mostly been used. GMs are those that separate the baseflow from the direct flow simply by connecting rising point with inflection point or points related to some duration from a hydrograph. Environmental tracer method (ETM) is another tool researched and developed under several conditions to estimate the groundwater recharge. The goal of this study is to separate the baseflow component from a storm hydrograph by applying various GMs and ETM, and to compare their results. The baseflow component estimated by ETM was different from the results by GMs in terms of their shapes of fluctuation and flow rates. Another important feature is that the form of the baseflow to which ETM is applied is similar to that of a storm hydrograph. This similarity is presumed to be due to the selection of tracer that respond quickly to rainfall.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.73-83
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• 2009

In this paper we propose an object similarity matching method based on shape characteristics of an object in an image. The proposed method extracts edge points from edges of objects and generates a log polar histogram with respect to each edge point to represent the relative placement of extracted points. It performs the matching in such a way that it compares polar histograms of two edge points sequentially along with edges of objects, and uses a well-known k-NN(nearest neighbor) approach to retrieve similar objects from a database. To verify the proposed method, we've compared it to an existing Shape-Context method. Experimental results reveal that our method is more accurate in object matching than the existing method, showing that when k=5, the precision of our method is 0.75-0.90 while that of the existing one is 0.37, and when k=10, the precision of our method is 0.61-0.80 while that of the existing one is 0.31. In the experiment of rotational transformation, our method is also more robust compared to the existing one, showing that the precision of our method is 0.69 while that of the existing one is 0.30.

• The Journal of the Korea Contents Association
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• v.6 no.10
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• pp.134-142
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• 2006

In this paper, we investigate a Hausdorff distance, which is used for the measurement of image similarity, to see whether it is also effective for document retrieval. The proposed method uses a local crowdedness and a Hausdorff distance to locate text images by determining whether a pair of images scanned at different time comes from the same text or not. To reduce the processing time, which is one of the disadvantages of a Hausdorff distance algorithm, we adopt a local crowdedness for feature point extraction. We apply the proposed method to 190 pairs of the same class and 190 pairs of the different class collected from postal envelop images. The results show that the modified Hausdorff distance proposed in this paper performed well in locating the tort region and calculating the degree of similarity between two images. An improvement of accuracy by 2.7% and 9.0% has been obtained, compared to a binary correlation method and the original Hausdorff distance method, respectively.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.800-808
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• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.177-186
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• 2005

In this Paper. we propose vision-based camera localization technique using 3D information which is created by mapping of DEM and mountain image. Typically, image features for localization have drawbacks, it is variable to camera viewpoint and after time information quantify increases . In this paper, we extract invariance features of geometry which is irrelevant to camera viewpoint and estimate camera extrinsic Parameter through accurate corresponding Points matching by Proposed similarity evaluation function and Graham search method we also propose 3D information creation method by using graphic theory and visual clues, The Proposed method has the three following stages; point features invariance vector extraction, 3D information creation, camera extrinsic Parameter estimation. In the experiments, we compare and analyse the proposed method with existing methods to demonstrate the superiority of the proposed methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.77-85
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• 2009

In this paper, we propose data format for smell, taste, touch with speech and vision which can be transmitted and implement a floral scent detection and recognition system. We provide representation method of data of smell, taste, and touch. Also, proposed floral scent recognition system consists of three module such as floral scent acquisition module using Metal Oxide Semiconductor (MOS) sensor array, entropy-based floral scent detection module, and floral scent recognition module using correlation coefficients. The proposed system calculates correlation coefficients of the individual sensor between feature vector(16 sensors) from floral scent input point until the stable region and 12 types of reference models. Then, this system selects the floral scent with the maximum similarity to the calculated average of individual correlation coefficients. To evaluate the floral scent recognition system using correlation coefficients, we implemented an individual floral scent recognition system using K-NN with PCA and LDA that are generally used in conventional electronic noses. In the experimental results, the proposed system performs approximately 95.7% average recognition rate.