• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.443-451
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• 2015

In this paper we suggest a new two-wheelers detection algorithm using uniform local binary pattern weighting value for projection vectors. The first, we calculate feature vectors using projection method which has robustness for rotation invariant and reducing dimensionality for each cell from origin image. The second, we applied new weighting values which are calculated by the modified local binary pattern showing the fast compute and simple to implement. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Journal of Multimedia Information System
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• v.1 no.2
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1302-1312
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• 2015

Recent research has been devoted and focused on detecting pedestrian and vehicle in intelligent vehicles except for the vulnerable road user(VRUS). In this paper suggest a new projection method which has robustness for rotation invariant and reducing dimensionality for each cell from original image to detect two-wheeler. We applied new weighting values which are calculated by maximum curvature containing very important object shape features and uniform local binary pattern to remove the noise. This paper considered the Adaboost algorithm to make a strong classification from weak classification. Experiment results show that the new approach gives higher detection accuracy than of the conventional method.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.79-82
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• 2017

Smoke and fire have different shapes and colours. This article suggests a fully connected system which is used two features using Adaboost algorithm for constructing a strong classifier as linear combination. We calculate the local histogram feature by gradient and bin, local binary pattern value, and projection vectors for each cell. According to the histogram magnitude, this paper applied adapted weighting value to improve the recognition rate. To preserve the local region and shape feature which has edge intensity, this paper processed the normalization sequence. For the extracted features, this paper Adaboost algorithm which makes strong classification to classify the objects. Our smoke detection system based on the proposed approach leads to higher detection accuracy than other system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2365-2373
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• 2011

This paper propose the hardware IP of real-time face detection system for mobile devices and digital cameras required for high speed, smaller size and lower power. The proposed face detection system is robust against illumination changes, face size, and various face angles as the main cause of the face detection performance. Input image is transformed to LBP(Local Binary Pattern) image to obtain face characteristics robust against illumination changes, and detected the face using face feature data that was adopted to learn and generate in the various face angles using the Adaboost algorithm. The proposed face detection system can be detected maximum 36 faces at the input image size of QVGA($320{\times}240$), and designed by Verilog-HDL. Also, it was verified hardware implementation by using Virtex5 XC5VLX330 FPGA board and HD CMOS image sensor(CIS) for FPGA verification.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1014-1023
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• 2016

In this study, we presented a method for non-square Differential LBP operation that can well describe the micro pattern in the horizontal and vertical component. We proposed a way to represent a LBP operation with various direction components as well as the diagonal component. In order to verify the validity of the proposed operation, Differential LBP was investigated with respect to accuracy, sensitivity, and specificity for the classification of facial expression. In accuracy comparison proposed LBP operation obtains better results than Square LBP and LBP-CS operations. Also, Proposed Differential LBP gets better results than previous two methods in the sensitivity and specificity indicators 'Neutral', 'Happiness', 'Surprise', and 'Anger' and excellence Differential LBP was confirmed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.10
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• pp.67-71
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• 2011

Existing face detection systems, which are used for digital door locks, digital cameras, video surveillance systems, and so on, are software-based implementation for relatively low level resolution images. Therefore, in this case, there are difficulties in detecting faces in a real-time fashion due to the increasing amount of operational processing as well as in allowing the requirements of face detections for HD(High Definition) resolutions. A hardware approach is necessary to efficiently find faces for HD images in real-time embedded systems. This paper proposes and implements a hardware architecture for an LBP(Local Binary Pattern) operation which is a time-consuming part as one of preprocessing steps for face detection. The hardware architecture proposed in this research has been implemented and tested with a FPGA(Field Programmable Gate Array) chip, and shown that the approach guarantees efficient face detection for HD images.