• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.149-160
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• 2012

The relative effects of individual differences in cultural orientation (individualism vs. collectivism) and locus of control (LOC: internal vs. external control beliefs) combination types on global/local processing were compared by manipulating the compound stimulus types (arrows or letters), and the stimulus-stimulus congruence. The results can be summarized as followings. First, consistent with previous research on global/local processing of the compound stimuli, reaction time (RT) for global stimuli than for local stimuli, and that in the stimulus-stimulus congruent condition than in the stimulus-stimulus incongruent condition was faster. Second, faster RT was found in the compound arrows condition than in the compound letters. Third, individual difference in LOC, rather than that in the cultural orientations, appeared to be related to global precedence effect, when the compound letters were presented. These results indicated that the individual's LOC rather than cultural orientation can increase the size of the global precedence effect, which might be involved in the stage of cognitive analysis than that of feature detection.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.965-976
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• 2020

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.778-785
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• 2009

This paper presents a Gabor texture feature extraction method for classification of discolored Metal pad images using GPU(Graphics Processing Unit). The proposed algorithm extracts the texture information using Gabor filters and constructs a pattern map using the extracted information. Finally, the golden pad images are classified by utilizing the feature vectors which are extracted from the constructed pattern map. In order to evaluate the performance of the Gabor texture feature extraction algorithm based on GPU, a sequential processing and parallel processing using OpenMP in CPU of this algorithm were adopted. Also, the proposed algorithm was implemented by using Global memory and Shared memory in GPU. The experimental results were demonstrated that the method using Shared memory in GPU provides the best performance. For evaluating the effectiveness of extracted Gabor texture features, an experimental validation has been conducted on a database of 20 Metal pad images and the experiment has shown no mis-classification.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1098-1102
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• 2014

Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.

• Smart Structures and Systems
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• v.25 no.5
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• pp.631-641
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• 2020

To meet the urgent requirements of safety surveillance from civil engineering management authorities, this study proposes a refined and efficient approach to generate full-field high-resolution panorama of construction sites using camera-amounted UAV (Unmanned Aerial Vehicle). GPS (Global Position System) information extraction for pre-registration, feature points filtering for efficient registration and optimal seaming line seeking for fusion are performed in sequence to form the full-field panorama generation framework. Advantages of the proposed method are as follows. First, GPS information can sort images for pre-registration, avoiding inefficient repeated pairwise calculations and matching. Second, the feature points are filtered according to the characteristics of the construction site images to reduce the amount of calculation. The proposed framework is validated on a road construction site and results demonstrate that it can generate an accurate and high-quality full-site panorama for the safety supervision in a much efficient manner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3594-3607
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• 2017

The traditional feature extraction methods such as principal component analysis (PCA) cannot obtain the local structure of the samples, and locally linear embedding (LLE) cannot obtain the global structure of the samples. However, a common drawback of existing PCA and LLE algorithm is that they cannot deal well with the sparse problem of the samples. Therefore, by integrating the globality of PCA and the locality of LLE with a sparse constraint, we developed an improved and unsupervised difference algorithm called Sparse Difference Embedding (SDE), for dimensionality reduction of high-dimensional data in small sample size problems. Significantly differing from the existing PCA and LLE algorithms, SDE seeks to find a set of perfect projections that can not only impact the locality of intraclass and maximize the globality of interclass, but can also simultaneously use the Lasso regression to obtain a sparse transformation matrix. This characteristic makes SDE more intuitive and more powerful than PCA and LLE. At last, the proposed algorithm was estimated through experiments using the Yale and AR face image databases and the USPS handwriting digital databases. The experimental results show that SDE outperforms PCA LLE and UDP attributed to its sparse discriminating characteristics, which also indicates that the SDE is an effective method for face recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1125-1136
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• 2010

Lately there exist increasing demands for online abnormality monitoring over trajectory stream, which are obtained from moving object tracking devices. This problem is challenging due to the requirement of high speed data processing within limited space cost. In this paper, we present a FADA (Fuzzy Anomaly Detection Algorithm) which constructs normal profile by computing mobility feature information from the GPS (Global Positioning System) logs of mobile devices in MANETs (Mobile Ad-hoc Networks), computes a fuzzy dissimilarity between the current mobility feature information of the mobile device and the mobility feature information in the normal profile, and detects effectively the anomaly behaviors of mobile devices on the basis of the computed fuzzy dissimilarity. The performance of proposed FADA is evaluated through simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.554-559
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• 2009

This paper proposes a new method of weighted template matching fur machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. The experiment compares confusion matrices of the template matching, error back propagation neural network classifier, and the proposed weighted template matching respectively. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.