• The Transactions of the Korea Information Processing Society
• /
• v.7 no.8
• /
• pp.2400-2410
• /
• 2000

Many researches on recognition of Korean characters have been undertaken. But while the majority are done on Korean character recognition, tasks for developing document recognition system have seldom been challenged. In this paper, I designed a recognizer of Korean courtesy amounts to improve error correction in recognized character string. From the very first step of Korean character recognition, we face the enormous scale of data. We have 2350 characters in Korean. Almost the previous researches tried to recognize about 1000 frequently-used characters, but the recognition rates show under 80%. Therefore using these kinds of recognizers is not efficient, so we designed a statistical multiple recognizer which recognize 16 Korean characters used in courtesy amounts. By using multiple recognizer, we can prevent an increase of errors. For the Postprocessor of Korean courtesy amounts, we use the properties of Korean character strings. There are syntactic rules in character strings of Korean courtesy amounts. By using this property, we can correct errors in Korean courtesy amounts. This kind of error correction is restricted only to the Korean characters representing the unit of the amounts. The first candidate of Korean character recognizer show !!i.49% of recognition rate and up to the fourth candidate show 99.72%. For Korean character string which is postprocessed, recognizer of Korean courtesy amounts show 96.42% of reliability. In this paper, we suggest a method to improve the reliability of Korean courtesy amounts recognition by using the Korean character recognizer which recognize limited numbers of characters and the postprocessor which correct the errors in Korean character strings.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.1
• /
• pp.35-43
• /
• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.271-278
• /
• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.423-430
• /
• 2013

In typical face recognition systems, the frontal view of face is preferred to reduce the complexity of the recognition. Thus individuals may be required to stare into the camera, or the camera should be located so that the frontal images are acquired easily. However these constraints severely restrict the adoption of face recognition to wide applications. To alleviate this problem, in this paper, we address the problem of tracking and recognizing faces in video captured with no environmental control. The face tracker extracts a sequence of the angle/size normalized face images using IVT (Incremental Visual Tracking) algorithm that is known to be robust to changes in appearance. Since no constraints have been imposed between the face direction and the video camera, there will be various poses in face images. Thus the pose is identified using a PCA (Principal Component Analysis)-based pose classifier, and only the pose-matched face images are used to identify person against the pre-built face DB with 5-poses. For face recognition, PCA, (2D)PCA, and $(2D)^2PCA$ algorithms have been tested to compute the recognition rate and the execution time.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.4
• /
• pp.97-107
• /
• 2009

In image-based gait recognition systems, physical factors, such as the camera angle and the lens distortion, and environmental factors such as illumination determines the performance of recognition. In this paper we present a robust gait recognition method by compensating various types of image distortions. The proposed method is compared with existing gait recognition algorithm with consideration of both physical and environmental distortion factors in the input image. More specifically, we first present an efficient compensation algorithm of image distortion by using the projective transform, and test the feasibility of the proposed algorithm by comparing the recognition performances with and without the compensation process. Proposed method gives universal gait data which is invariant to both distance and environment. Gained data improved gait recognition rate about 41.5% in indoor image and about 55.5% in outdoor image. Proposed method can be used effectively in database(DB) construction, searching and tracking of specific objects.

• Journal of IKEEE
• /
• v.21 no.4
• /
• pp.412-415
• /
• 2017

In this paper, we propose a multi license plate recognition system using high resolution $360^{\circ}$ omnidirectional IP camera. The proposed system consists of a planar division part of $360^{\circ}$ circular image and a multi license plate recognition part. The planar division part of the $360^{\circ}$ circular image are divided into a planar image with enhanced image quality through processes such as circular image acquisition, circular image segmentation, conversion to plane image, pixel correction using color interpolation, color correction and edge correction in a high resolution $360^{\circ}$ omnidirectional IP Camera. Multi license plate recognition part is through the multi-plate extraction candidate region, a multi-plate candidate area normalized and restore, multiple license plate number, character recognition using a neural network in the process of recognizing a multi-planar imaging plates. In order to evaluate the multi license plate recognition system using the proposed high resolution $360^{\circ}$ omnidirectional IP camera, we experimented with a specialist in the operation of intelligent parking control system, and 97.8% of high plate recognition rate was confirmed.

• Science of Emotion and Sensibility
• /
• v.17 no.3
• /
• pp.107-116
• /
• 2014

In the current study, it is suggested that when two emotions are presented through cross-modality, such as auditory and visual, incongruence will influence arousal, recognition, and recall of subjects. The first hypothesis is that incongruent cross-modality does not only increase arousal more than the congruent, but it also increases recall and recognition more than congruent. The second hypothesis is that arousal modulates recall and recognition of subjects. To demonstrate the two hypotheses, our experiment's conditions were manipulated to be congruent and incongruent by presenting positive or negative emotions, visually and acoustically. For dependent variables, we measured recall rate and recognition rates. and arousal was measured by PAD (pleasure-arousal-dominance) scales. After eight days, only recognition was measured repeatedly online. As a result, our behavioral experiment showed that there was a significant difference between arousal before watching a movie clip and after (p<.001), but no difference between the congruent condition and incongruent condition. Also, there was no significant difference between recognition performance in the congruent condition and incongruent condition, but there was a main effect of the clips' emotions. Interestingly after analyzing recognition rates separately depending on clips' emotions, there was a significant difference between congruent and incongruent conditions in the only negative clip (p= .044), not in the positive clip. In a detailed result, recognition in the incongruent condition is more than in the congruent condition. Furthermore, in the case of recall performance, there was a significant interaction between the clips' emotions shown in the clips and congruent conditions (p=.039). Through these results, the effect of incongruence with negative emotion was demonstrated, but an incongruent effect by arousal could not be demonstrated. In conclusion, in our study, we tried to determine the impact of one method to convey a story dramatically and have an effect on memory. These effects are influenced by the subjects' perceived emotions (valence and arousal).

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.4
• /
• pp.19-27
• /
• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.11
• /
• pp.2276-2290
• /
• 1994

In this paper, we propose an algorithm for character recognition using the subdivided type and the stable structure information. The subdivided type of character is acquired from the stable structure information of character which is extracted from an input character. Firstly, the character is obtained from a scanner and classified into on of 6 types by using directional density vector. And then, the stable structure information is extracted from each character and the character is subdivided into on of 26 types. Finally, the classified character is recognized by using neural network which is inputted the directional density vector equivalent to JASO area or recognized direct. Aa a result of experiment with KS C 5601 2350 printed Hangeul characters, we obtain the recognition rate of 94%.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.3
• /
• pp.248-259
• /
• 2001

This paper proposes a human action recognition scheme to recognize nonverbal human communications automatically. Based on the principle that a human body action can be defined as a combination of multiple articulation movements, we use the method of inferencing stochastic grammars to understand each human actions. We measure and quantize each human action in 3D world-coordinate, and make two sets of 4-chain-code for xy and zy projection plane. Based on the fact that the neighboring information among articulations is an essential element to distinguish actions, we designed a new stochastic inference procedure to apply the neighboring information of hands. Our proposed scheme shows better recognition rate than that of other general stochastic inference procedures. ures.