• Journal of Advanced Navigation Technology
• /
• v.16 no.2
• /
• pp.394-400
• /
• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.3
• /
• pp.670-678
• /
• 2014

This paper presents a method to extract objects in radiographic images where all the allowable combinations of segmented regions are compared to a target object using Fourier descriptor. In the object extraction for usual images, a main problem is occlusion. In radiographic images, there is an advantage that the shape of an object is not occluded by other objects. It is because radiographic images represent the amount of radiation penetrated through objects. Considering the property of no occlusion in radiographic images, the shape based descriptors can be very effective to find objects. After all, the proposed object extraction method consists of three steps of segmenting regions, finding all the combinations of the segmented regions, and matching the combinations to the shape of the target object. In finding the combinations, we reduce a lot of computations to remove unnecessary combinations before matching. In matching, we employ Fourier descriptor so that the proposed method is rotation and shift invariant. Additionally, shape normalization is adopted to be scale invariant. By experiments, we verify that the proposed method works well in extracting objects.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.537-546
• /
• 2019

The paper actualizes the twin camera module system that is portable and very useful for the production of 3D contents. The suggested twin camera module system is a system to be able to display the 3D image after converting the inputted image from 2D stereo camera. To evaluate the performance of the twin camera module suggested in this paper, I assessed the correction of Rotation and Tilt created depending on the visual difference between the left and right stereoscopic image shot by the left and right lenses by using the Test Platform. In addition, I verified the efficiency of the twin camera module system through verifying Depth Error of 3D stereoscopic image by means of Scale Invariant Feature Transform(SIFT) algorithm. I think that if the user utilizes the suggested twin camera module system in displaying the image to the external after converting the shot image into the 3D stereoscopic image and the preparation image, it is possible to display the image in a matched way with an output device fit respectively for different 3D image production methods and if the user utilizes the system in displaying the created image in the form of the 3D stereoscopic image and the preparation image via different channels, it is possible to produce 3D image contents easily and conveniently with applying to lots of products.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.8
• /
• pp.88-100
• /
• 1999

Detecting human face and facial landmarks automatically in an image is as essential step to a fully automatic face recognition system. In this paper, we present a new approach to detect automatically face and its eyes of input image with scale and rotation variations of faces by using an intensity based template matching with a single log-polar face template. In a template-based matching it is necessary to normalize the scale changes and rotations of an input image to a template ones. The log-polar mapping which simulates space-variant human visual system converts scale changes and rotations of input image into constant horizontal and cyclic vertical shifts in the output plane. Intelligent use of this property allows us to shift of the candidate log-polar faces mapped at various fixation points of an input image to be matched to a template over the log-polar plane. Thus, the proposed method eliminates the need of adapting multitemplate and multiresolution schemes, which inevitably give rise to intensive computation involved to cope with scale and rotation variations of faces. Through this scale and rotation involved to cope with scale and method can lead to detecting face and its eyes simultaneously. Experimental results on a database of 795 images show over 98% detection rate.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.4
• /
• pp.116-128
• /
• 2011

In this paper, we propose the structure of a high-performance face-detection engine that responds well to facial rotating changes using rotation transformation which minimize the required memory usage compared to the previous face-detection engine. The validity of the proposed structure has been verified through the implementation of FPGA. For high performance face detection, the MCT (Modified Census Transform) method, which is robust against lighting change, was used. The Adaboost learning algorithm was used for creating optimized learning data. And the rotation transformation method was added to maintain effectiveness against face rotating changes. The proposed hardware structure was composed of Color Space Converter, Noise Filter, Memory Controller Interface, Image Rotator, Image Scaler, MCT(Modified Census Transform), Candidate Detector / Confidence Mapper, Position Resizer, Data Grouper, Overlay Processor / Color Overlay Processor. The face detection engine was tested using a Virtex5 LX330 FPGA board, a QVGA grade CMOS camera, and an LCD Display. It was verified that the engine demonstrated excellent performance in diverse real life environments and in a face detection standard database. As a result, a high performance real time face detection engine that can conduct real time processing at speeds of at least 60 frames per second, which is effective against lighting changes and face rotating changes and can detect 32 faces in diverse sizes simultaneously, was developed.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.5
• /
• pp.1639-1644
• /
• 2014

SAR satellite can acquire clear imagery regardless of weather and the images are widely used for land management, natural hazard monitoring and many other applications. Automatic image matching technique is necessary for management of a huge amount of SAR data. Nevertheless, it is difficult to assure the accuracy of image matching due to the difference of image-capturing attitude and time. In this paper, we compared performances of MI method, FMT method and SIFT method by applying arbitrary displacement and rotation to TerraSAR-X images and changing resolution of the images. As a result, when the features having specific intensity were distributed well in SAR imagery, MI method could assure 0~2 pixels accuracy even if the images were captured in different geometry. But the accuracy of FMT method was significantly poor for the images having different spatial resolutions and the error was represented by tens or hundreds pixels. Moreover, the ratio of corresponding matching points for SIFT method was only 0~17% and it was difficult for SIFT method to apply to SAR images captured in different geometry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.05a
• /
• pp.185-191
• /
• 1999

Mathematical morphology based on set theory has been applied to various areas in image processing. Pitas proposed a object recognition algorithm using Morphological Shape Decomposition(MSD), and a new representation scheme called Morphological Shape Representation(MSR). The Pitas's algorithm is a simple and adequate approach to recognize objects that are rotated 45 degree-units with respect to the model object. However, this recognition scheme fails in case of random rotation. This disadvantage may be compensated by defining small angle increments. However, this solution may greatly increase computational complexity because the smaller the step makes more number of rotations to be necessary. In this paper, we propose a new method for object recognition based on MSD. The first step of our method decomposes a binary shape into a union of simple binary shapes, and then a new tree structure is constructed which ran represent the relations of binary shapes in an object. finally, we obtain the feature informations invariant to the rotation, translation, and scaling from the tree and calculate matching scores using efficient matching measure. Because our method does not need to rotate the object to be tested, it could be more efficient than Pitas's one. MSR has an intricate structure so that it might be difficult to calculate matching scores even for a little complex object. But our tree has simpler structure than MSR, and easier to calculated the matchng score. We experimented 20 test images scaled, rotated, and translated versions of five kinds of automobile images. The simulation result using octagonal structure elements shows 95% correct recognition rate. The experimental results using approximated circular structure elements are examined. Also, the effect of noise on MSR scheme is considered.

• Journal of the Institute of Convergence Signal Processing
• /
• v.6 no.1
• /
• pp.15-22
• /
• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.32-35
• /
• 2003

In this paper, we describe how to represent lower case hand-written English alphabets by a sequence of two to seven fuzzy sets. Each fuzzy set represents an arc segment of the character and each arc segment is assumed to be a part of an ellipse. The part of an ellipse is defined by five quantities: its short and long radii, its orientation angle, whether it is a part of the lower half or the upper half and whether it is the full half or a part of a half. Hence, we use the Cartesian product of five fuzzy sets to represent each arc segment. We show that this representation is a translation, rotation, and scaling invariant and that it can be used to generate the hand-written English alphabets. The representation we describe is different from the one proposed earlier by the author and when compared with the previous representation, the one described in this paper simulates more closely the behavior of how one writes English characters.

• Journal of Korea Society of Industrial Information Systems
• /
• v.7 no.3
• /
• pp.23-31
• /
• 2002

In this paper we propose a new feature vector for recognition of the facial expression based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are invariant under translation rotation, and scale of an facial expression imege. The Algorithm for recognition of a facial expression contains two parts： the extraction of feature vector and the recognition process. The extraction of feature vector are comprised of modified 2-D conditional moments based on estimated Gibbs distribution for an facial image. In the facial expression recognition phase, we use discrete left-right HMM which is widely used in pattern recognition. In order to evaluate the performance of the proposed scheme, experiments for recognition of four universal expression (anger, fear, happiness, surprise) was conducted with facial image sequences on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 95%.