• Journal of IKEEE
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• v.18 no.4
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• pp.536-543
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• 2014

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.18-23
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• 2016

QFN (Quad Flat No-leads Package) is one of the SMD (Surface Mount Device). Since there is no lead in QFN, there are many defects on solder. Therefore, we propose an efficient mechanism for QFN solder defect detection at this paper. For this, we employ Convolutional Neural Network (CNN) of the Machine Learning algorithm. QFN solder's color multi-layer images are used to train CNN. Since these images are 3-channel color images, they have a problem with applying to CNN. To solve this problem, we used each 1-channel grayscale image (Red, Green, Blue) that was separated from 3-channel color images. We were able to detect QFN solder defects by using this CNN. In this paper, it is shown that the CNN is superior to the conventional multi-layer neural networks in detecting QFN solder defects. Later, further research is needed to detect other QFN.

• Current Optics and Photonics
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• v.6 no.6
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• pp.598-607
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• 2022

In this paper, a simple nondestructive technology is used to investigate unstained biological blood cells in three dimensions (3D). The technology employs a reflective phase-only spatial light modulator (SLM) for displaying the phase hologram of the object being tested, and a Fourier lens for its reconstruction. The phase hologram is generated via superposing a digital random phase on the 2D image of the object. The phase hologram is then displayed by the SLM with 256 grayscale levels, and reconstructed by a Fourier lens to present the object in 3D. Since noise is the main problem in this method, the windowed Fourier filtering (WFF) method is applied to suppress the noise of the reconstructed object. The quality of the reconstructed object is refined and the noise level suppressed by approximately 40%. The technique is applied to objects: the National Institute of Standards (NIS) logo, and a film of unstained peripheral blood. Experimental results show that the proposed technique can be used for rapid investigation of unstained biological blood cells in 3D for disease diagnosis. Moreover, it can be used for viewing unstained white blood cells, which is still challenging with an optical microscope, even at large magnification.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.975-984
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• 2022

Since a linear transducer has an area of interest equal to the length of the transducer, the area of interest can be expanded using the virtual convex function installed in the device.However, it was thought that the change in the direction of the ultrasonic sound velocity according to the change in the visual area of interest would affect the image quality, so this was objectively confirmed. For this study, image evaluation and SNR·CNR of the phantom for ultrasound quality control were measured. As a result, in the phantom image evaluation, both images were able to identify structures in functional resolution, grayscale, and dynamic range. However, it was confirmed that the standard image was excellent in the reproducibility of the size and shape of the structure. As a result of SNR·CNR evaluation, SNR·CNR of most trapezoidal images was low, except for structures at specific locations. In addition, through the statistical analysis graph, it was further confirmed that the SNR and CNR for each depth decreased as the size of the cystic structure decreased. Through this study, it was confirmed that the use of the function has the advantage of providing a wide visual area of interest, but it has an effect on the image quality. Therefore, when using the virtual convex function, it is judged that the examiner should use it in an appropriate situation and conduct various studies to acquire high-quality images and to improve the understanding and proficiency of the equipment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.469-486
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• 2018

We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images in the context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploits only the lowest kind of local information through the global histogram. However, such global Histograms reflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which uses local histograms of the LBP, was one of few tentative to catch higher level textural information. We believe that important local and useful information in between the two levels is just ignored by the two schemas. The newly developed method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as much local information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present in grayscale images extracted by LBP through a complex structure. The used framework is comprised of a multitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. The framework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic forms allow targeting texture directionality, which is a very useful property in texture characterization. In addition, the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared to published works including the very effective DLEP. Results show significant higher or comparable performance of GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.215-224
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• 2011

There have been studying many researches to detect human body and to track one with increasing interest on human and computer interaction. In this paper, we propose the algorithm that automatically extracts joints, linked points of human body, using the ratio of human body under single camera and tracks object. The proposed method gets the difference images of the grayscale images and ones of the hue images between input image and background image. Then the proposed method composes the results, splits background and foreground, and extracts objects. Also we standardize the ratio of human body using face' length and the measurement of human body and automatically extract joints of the object using the ratio and the corner points of the silhouette of object. After then, we tract the joints' movement using block-matching algorithm. The proposed method is applied to test video to be acquired through a camera and the result shows that the proposed method automatically extracts joints and effectively tracks the detected joints.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.539-554
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• 2018

Effective interaction between user and device is considered an important ability of IoT devices. For some applications, it is necessary to recognize human facial expressions in real time and make accurate judgments in order to respond to situations correctly. Therefore, many researches on facial image analysis have been preceded in order to construct a more accurate and faster recognition system. In this study, we constructed an automatic recognition system for facial expressions through two steps - a facial recognition step and a classification step. We compared various models with different sets of data with pixel information, landmark coordinates, Euclidean distances among landmark points, and arctangent angles. We found a fast and efficient prediction model with only 30 principal components of face landmark information. We applied several prediction models, that included linear discriminant analysis (LDA), random forests, support vector machine (SVM), and bagging; consequently, an SVM model gives the best result. The LDA model gives the second best prediction accuracy but it can fit and predict data faster than SVM and other methods. Finally, we compared our method to Microsoft Azure Emotion API and Convolution Neural Network (CNN). Our method gives a very competitive result.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.1
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• pp.37-48
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• 2000

In this paper, we propose a new connected operator Using morphological grayscale reconstruction for region-based coding First, an effective method of reference-image creation lis proposed, which is based on the Size as well as the contrast. This improves the performance of simplification, because It preserves perceptually important components and removes unnecessary components The conventional connected operators are good for removing small regions, but have a serious drawback for low-contrast regions that are larger than the structuring element. That is, when the conventional connected operators are applied to tills region, the simplification becomes less effective or several meaningful regions are merged to one region to avoid this, the conventional geodesic dilation is modified to propose an adaptive operator to reduce the effect of inappropriate propagation, pixels reconstructed to the original values are excluded m the dilation operation Experimental results have shown that the proposed algorithm achieves better performance In terms of the reconstruction of flat zones. The Picture quality has also been improved by about 7dB, compared to the conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.107-114
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• 2005

Edge enhanced image is needed for processing images for special purpose such as a circuit diagram or a design composed of lines. Error diffusion halftoning, among digital halftoning methods to represent a continuous grayscale image for the binary output device such as printers, facsimiles, LCD televisions and etc. also makes edges of objects blurred. This paper proposes the method to enhance the edge of a binary image for the binary output device as well as a circuit diagram or a design. Based on that the human eyes perceive the local average luminance rather than the pixel's luminance itself, the proposed system uses a local activitymeasure (LAM), which is the difference between a pixel luminance and the average of its $3{\times}3$ neighborhood pixels' luminances weighted according to the spatial positioning. The system also usesinformation of edge enhancement(IEE), which is computed from the LAM multiplied by the average luminance. The IEE is added to the quantizer's input pixel and feeds into the halftoning quantizer. The quantizer produces the halftone image having the enhanced edge. The simulation results show that the proposed method produces more fine halftoning images than conventional methods due to the enhanced edges. Also the performance of the proposed method is compared with that of the conventional method by measuring the edge correlation and the local average accordance over a range of viewing distances.