• Journal of Digital Contents Society
• /
• v.14 no.1
• /
• pp.117-124
• /
• 2013

This paper suggests the robust method of extraction for moving objects in illumination variation by using image sequence from an immovable camera. The most difficult part of the implication is the effect by illumination and noise. The object area is hardly estimated when the dusky area occurs in illumination variation by time change. This thesis describes the extraction of moving objects employed by Gaussian mixture model which is noise robust measure. Also, the report suggests the elimination method of illumination part in input image by the representative illumination image which is defined to minimize the illumination influence.

• Journal of Information Processing Systems
• /
• v.14 no.1
• /
• pp.191-204
• /
• 2018

To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.

• Korean Journal of Optics and Photonics
• /
• v.24 no.1
• /
• pp.29-38
• /
• 2013

In this paper, the illumination performances of three freeform lenses optimally designed for a point source were investigated for several LEDs with different source sizes, and also the tolerance characteristics of the lenses were analyzed. For comparison, two lenses with different sizes were designed with a divergent illumination model, and the last one was done with an overlapped illumination model. As the LED source size increased, the illuminance uniformity decreased more strongly, and the influence of a source misalignment on illumination performance became insignificant. However, the variation of LED radiation characteristics had strong effect on the illumination performance, irrespective of LED source size. Even though the lens based on a divergent illumination model showed superior performance compared to the lens based on an overlapped illumination model, the latter was less sensitive to the variation of LED radiation characteristics.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.140-145
• /
• 2009

Significant attention has recently been drawn to human robot interaction system that uses face detection technology. The most conventional face detection methods have applied under pixel domain. These pixel based face detection methods require high computational power. Hence, the conventional methods do not satisfy the robot environment that requires robot to operate in a limited computing process and saving space. Also, compensating the variation of illumination is important and necessary for reliable face detection. In this paper, we propose the illumination invariant face detection that is performed under the compressed domain. The proposed method uses color balancing module to compensate illumination variation. Experiments show that the proposed face detection method can effectively increase the face detection rate under existing illumination.

• Journal of Game and Entertainment
• /
• v.2 no.2
• /
• pp.45-50
• /
• 2006

In a face detection system based on template matching, histogram equalization or log transform is applied to an input image for the intensity normalization and the image improvement. It is known that they are noneffective in improving an image with intensity distortion by illumination variation. In this paper, we propose an efficient image improvement method called as a bilateral symmetry average for images with intensity distortion by illumination variation. Experimental results show that our method delivers the detection performance better than previous methods and also remarkably reduces the number of face candidates.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.7
• /
• pp.102-111
• /
• 1997

In this paper, a patern recognition method of surface mount device(SMD) IC using wavelet transform and neural network is proposed. We chose the feature parameter according to the characteristics of coefficient matrix which is obtained from four level discrete wavelet transform (DWT). These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Experimental results show that when the same form of feature pattern, as is used for learning, is put into neural network and gained 100% rate ofrecognition irrespective of SMD IC kinds, location and variation of illumination. In the case of unused feature pattern for learning, the recognition rate is 85.9% under the similar surroundings, where as an average recognition rate is 96.87% for the case of reregulated value of illumination. Proosed method is relatively simple compared with the traditional space domain method in extracting the feature parameter and is also well suited for recognizing the pattern's class, position and existence. It can also shorten the processing tiem better than method extracting feature parameter with the use of discrete cosine transform(DCT) and adapt the surroundings such as variation of illumination, the arrangement and the translation of SMD IC.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.19 no.1
• /
• pp.15-23
• /
• 2005

According to the recent investigation, the 4th grade students' eyesight weakness was occurred on a large scale in primary school. To improve the classroom illumination environment which affects students' eyesight weakness of the 4th grade student, an analysis of illumination environment and an eyesight acuity for the whole 4th year students was held. In four out of the whole classrooms, each improvement work in illumination has done using different types of luminaires. Re-test of eyesight was held for students who studied under the improved illumination environment for 10 months and students under the environment not improved. Comparative analysis of the results which was obtained from re-test and measurements was carried out. Consequently, influence on the eyesight variation by classroom illumination was analysed and the improvement of classroom illumination was researched.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.10
• /
• pp.129-136
• /
• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.6
• /
• pp.780-785
• /
• 2010

This paper introduces GP(Genetic Programming) based color detection model for an object detection and tracking. Existing color detection methods have used linear/nonlinear transformatin of RGB color-model and improved color model for illumination variation by optimization or learning techniques. However, most of cases have difficulties to classify various of colors because of interference of among color channels and are not robust for illumination variation. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various colors and images with different lighting conditions.

• Journal of KIISE:Software and Applications
• /
• v.34 no.1
• /
• pp.70-78
• /
• 2007

The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.