• Journal of the Optical Society of Korea
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• v.15 no.1
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• pp.15-21
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• 2011

This paper proposes a new approach to palmprint verification based on the gradient, in which a palm image is considered to be a three-dimensional terrain. Principal lines and wrinkles make deep and shallow valleys on a palm landscape. Then the steepest slope direction in each local area is first computed using the Kirsch operator, after which an orientation map is created that represents the dominant slope direction of each pixel. In this study, three orientation maps were made with different scales to represent local and global gradient information. Next, feature matching based on pixel-unit comparison was performed. The experimental results showed that the proposed method is superior to several state-of-the-art methods. In addition, the verification could be greatly improved by fusing orientation maps with different scales.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05a
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• pp.106-109
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

• Journal of Biosystems Engineering
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• v.20 no.1
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• pp.95-103
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• 1995

A series of study has been conducted to develop a multi-purpose fruit grader using a black ＆ white image processing system equipped with a 550 nm interference filter. A device and high performance algorithms were developed for sizing and color grading of Fuji apple in the previous study. In this study an emphasis was put on finding correlations between weights of several kinds of fruits and their area fractions(AF), and on compensating the blurring effect upon sizing and color grading by conveying speed of fruit. Also, the effect of orientation and direction of fruit on conveyor during image forming was analyzed to identify any difficulty (or utilizing an automatic fruit feeder. The results are summarized as follows. 1. The correlation coefficients(r) between the weights of fruits and their image sizes were 0.984~0.996 for apples, 0.983~0.990 for peachs, 0.995 for tomato, 0.986 for sweet persimmon and 0.970~0.993 for pears. 2. It was possible to grade fruits by color with the area weighted mean gray values(AWMGV) based on the mean gray valves of direct image and the compensated values of reflected image of a fruit, and also possible to sort fruits by size with AF. Accuracies in sizing and color grading ranged over 81.0% ~95.0% and 82.0% ~89.7% respectively as compared with results from sizing by electronic weight scale and grading by expert. 3. The blurring effect on the sizing and color grading depending on conveying speed was identified and regression equations were derived. 4. It was found that errors in sizing and coloring grading due to the change in direction and orientation of Fuji apple on the conveyor were not significant as far as the stem end of apple keeping upward.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.693-701
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• 2011

Image registration is an essential process to analyze the time series of satellite images for the purpose of image fusion and change detection. The Mutual Information (MI) is commonly used as similarity measure for image registration because of its robustness to noise. Due to the radiometric differences, it is not easy to apply MI to multi-temporal satellite images using directly the pixel intensity. Image features for MI are more abundantly obtained by employing a Gabor filter which varies adaptively with the filter characteristics such as filter size, frequency and orientation for each pixel. In this paper we employed Bidirectional Gabor Filter Energy (BGFE) defined by Gabor filter features and applied the BGFE to similarity measure calculation as an image feature for MI. The experiment results show that the proposed method is more robust than the conventional MI method combined with intensity or gradient magnitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2075-2092
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• 2017

Person re-identification is to match pedestrians observed from non-overlapping camera views. It has important applications in video surveillance such as person retrieval, person tracking, and activity analysis. However, it is a very challenging problem due to illumination, pose and viewpoint variations between non-overlapping camera views. In this work, we propose a viewpoint invariant method for matching pedestrian images using orientation of pedestrian. First, the proposed method divides a pedestrian image into patches and assigns angle to a patch using the orientation of the pedestrian under the assumption that a person body has the cylindrical shape. The difference between angles are then used to compute the similarity between patches. We applied the proposed method to real-time global multi-object tracking across multiple disjoint cameras with non-overlapping field of views. Re-identification algorithm makes global trajectories by connecting local trajectories obtained by different local trackers. The effectiveness of the viewpoint invariant method for person re-identification was validated on the VIPeR dataset. In addition, we demonstrated the effectiveness of the proposed approach for the inter-camera multiple object tracking on the MCT dataset with ground truth data for local tracking.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.177-182
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• 1995

Since one property(i.e. coarseness, orientation, regularity, granularity etc.) of ultrasound liver images was not sufficiently enough to classify the characteristics of livers, we used the multi-feature vectors from ultrasound images to diagnose the liver disease. The proposed classifier, which uses the multi-feature vectors and Bayes decision rule, performed well for the classification of normal, fat and cirrhosis liver. In our simulation, we used the Battacharyya distance and Hotelling Trace Criterion to select the best multi-feature vectors for the classifier and obtained less classification errors than other methods using single feature vector.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.941-951
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• 2018

Utilization and demand of UAV (unmanned aerial vehicle) for the generation of 3D city model are increasing. In this study, we performed an experiment to adjustment position/orientation of UAV with incomplete attitude information and to extract point cloud data. In order to correct the attitude of the UAV, the rotation angle was calculated by using the continuous position information of UAV movements. Based on this, the corrected position/orientation information was obtained by applying IBA (Incremental Bundle Adjustment) based on photogrammetry. Each pair was transformed into an epipolar image, and the MDR (Multi-Dimensional Relaxation) technique was applied to obtain high precision DSM. Each extracted pair is aggregated and output in the form of a single point cloud or DSM. Using the DJI inspire1 and Phantom4 images, we can confirm that the point cloud can be extracted which expresses the railing of the building clearly. In the future, research will be conducted on improving the matching performance and establishing sensor models of oblique images. After that, we will continue the image processing technology for the generation of the 3D city model through the study of the extraction of 3D cloud It should be developed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.1
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• pp.35-45
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• 1991

Multi layer neural network using backpropagation learning algorithm is used to achieve identification and orientation estimation of different classes of aircraft in the variety of 3-D orientations. In-plane distortion invarient$(L,\;{\Phi})$ feature was extracted from each aircraft image to be used for training neural network aircraft classifier. For aircraft identification the optimum structure of the neural network classifier is studied to obtain high classification performance. Effective reductioin of learning time was achieved by using modified backpropagation learning algorithm and varying, learning parameters.

• ETRI Journal
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• v.34 no.4
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• pp.572-582
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• 2012

The discrete Gaussian-Hermite moment (DGHM) is a global feature representation method that can be applied to square images. We propose a modified DGHM (MDGHM) method and an MDGHM-based scale-invariant feature transform (MDGHM-SIFT) descriptor. In the MDGHM, we devise a movable mask to represent the local features of a non-square image. The complete set of non-square image features are then represented by the summation of all MDGHMs. We also propose to apply an accumulated MDGHM using multi-order derivatives to obtain distinguishable feature information in the third stage of the SIFT. Finally, we calculate an MDGHM-based magnitude and an MDGHM-based orientation using the accumulated MDGHM. We carry out experiments using the proposed method with six kinds of deformations. The results show that the proposed method can be applied to non-square images without any image truncation and that it significantly outperforms the matching accuracy of other SIFT algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1114-1119
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• 1995

This paper presents the development of estimation model and control method based on the new computer vision. This proposed control method is accomplished using a sequential estimation scheme that permits placement of the rigid body in each of the two-dimensional image planes of monitoring cameras. Estimation model with six parameters is developed based on a model that generalizes known 4-axis scara robot kinematics to accommodate unknown relative camera position and orientation, etc. Based on the estimated parameters,depending on each camers the joint angle of robot is estimated by the iteration method. The method is tested experimentally in two ways, the estimation model test and a three-dimensional rigid body placement task. Three results show that control scheme used is precise and robust. This feature can open the door to a range of application of multi-axis robot such as assembly and welding.