• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1207-1212
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• 2015

In this paper, we propose three-dimensional (3D) visualization and recognition techniques of micro-objects under photon-starved conditions using photon counting integral imaging microscopy. To capture high resolution 2D images with different perspectives in the proposed method, we use Synthetic Aperture Integral Imaging (SAII). Poisson distribution which is mathematical model of photon counting imaging system is used to extract photons from the images. To estimate 3D images with 2D photon counting images, the statistical estimation is used. Therefore, 3D images can be obtained and visualized without any damage under photon-starved conditions. In addition, 3D object recognition can be implemented using nonlinear correlation filters. To prove the usefulness of our technique, we implemented the optical experiment.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1416-1419
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• 2002

This paper describes an implementation method for the people counting system which detects and tracks moving people using a fixed single camera. This system counts the number of moving objects (people) entering the security door. Moreover, the detected objects are tracked by the proposed tracking algorithm before entering the door. The proposed system with In-tel Pentium IV operates at an average rate of 10 frames a second on real world scenes where up to 6 persons come into the view of a vertically mounted camera.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.24-29
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• 2016

One of the main applications of digital image processing is the estimation of the number of certain types of objects (cells, seeds, peoples etc.) in an image. Difficulties of these counting problems depends on various factors including shape and size variation, degree of object clustering, contrast between object and background, object texture and its variation, and so on. In this paper, a new automatic colony counting algorithm is proposed. We focused on the two applications: counting the bacteria colonies on the agar plate and estimating the number of seeds from images captured by smartphone camera. To overcome the shape and size variations of the colonies, we adopted the distance transformation and peak detection approach. To estimate the reference size of the colony robustly, we also used k-means clustering algorithm. Experimental results show that our method works well in real world applications.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.64-69
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• 2008

This paper discusses low resolution face recognition using the photon-counting linear discriminant analysis (LDA). The photon-counting LDA asymptotically realizes the Fisher criterion without dimensionality reduction since it does not suffer from the singularity problem of the fisher LDA. The linear discriminant function for optimal projection is determined in high dimensional space to classify unknown objects, thus, it is more efficient in dealing with low resolution facial images as well as conventional face distortions. The simulation results show that the proposed method is superior to Eigen face and Fisher face in terms of the accuracy and false alarm rates.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.493-505
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• 2016

Among modern computer vision and video surveillance systems, the pedestrian counting system is a one of important systems in terms of security, scheduling and advertising. In the field of, pedestrian counting remains a variety of challenges such as changes in illumination, partial occlusion, overlap and people detection. During pedestrian counting process, the biggest problem is occlusion effect in crowded environment. Occlusion and overlap must be resolved for accurate people counting. In this paper, we propose a novel pedestrian counting system which improves existing pedestrian tracking method. Unlike existing pedestrian tracking method, proposed method shows that average filter tracking method can improve tracking performance. Also proposed method improves tracking performance through frame compensation and outlier removal. At the same time, we keep various information of tracking objects. The proposed method improves counting accuracy and reduces error rate about S6 dataset and S7 dataset. Also our system provides real time detection at the rate of 80 fps.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.22-29
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• 2006

In this paper, we propose a block-based real-time people counting system that can be used in various environments including showing mall entrances, elevators and escalators. The main contributions of this paper are robust background subtraction, the block-based decision method and real-time processing. For robust background subtraction obtained from a number of image sequences, we used a mixture of K Gaussian. The block-based decision method was used to determine the size of the given objects (moving people) in each block. We divided the images into $6{\times}12$ blocks and trained the mean and variance values of the specific objects in each block. This was done in order to provide real-time processing for up to 4 channels. Finally, we analyzed various actions that can occur with moving people in real world environments.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.9-18
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• 2020

In the field of image processing, a method of detecting and counting passengers as moving objects when getting on and off the bus has been studied. Among these technologies, one of the artificial intelligence techniques, the deep learning technique is used. As another method, a method of detecting an object using a stereo vision camera is also used. However, these techniques require expensive hardware equipment because of the computational complexity of used to detect objects. However, most video equipments have a significant decrease in computational processing power, and thus, in order to detect passengers on the bus, there is a need for an image processing technology suitable for various equipment using a relatively low computational technique. Therefore, in this paper, we propose a technique that can efficiently obtain the number of passengers on the bus by detecting the contour of the object through the background subtraction suitable for low-cost equipment. Experiments have shown that passengers were counted with approximately 70% accuracy on lower-end machines than those equipped with stereo vision camera.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.293-300
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• 2018

We propose a moving object extraction and tracking method for improvement of animal identification and tracking technology. First, we propose a method of merging separated moving objects into a moving object by using FCM (Fuzzy C-Means) clustering algorithm to solve the problem of moving object loss caused by moving object extraction process. In addition, we propose a method of extracting data from a moving object and a method of counting moving objects to determine the number of clusters in order to satisfy the conditions for performing FCM clustering algorithm. Then, we propose a method to continuously track merged moving objects. In the proposed method, color histograms are extracted from feature information of each moving object, and the histograms are continuously accumulated so as not to react sensitively to noise or changes, and the average is obtained and stored. Thereafter, when a plurality of moving objects are overlapped and separated, the stored color histogram is compared with each other to correctly recognize each moving object. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Honam Mathematical Journal
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• v.32 no.1
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• pp.113-129
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• 2010

Inclusion and exclusion is used in many papers to count certain objects exactly or asymptotically. Also it is used to derive the Bonferroni inequalities in probabilistic area [6]. Inclusion and exclusion on finitely many types of properties is first used in R. Meyer [7] in probability form and first used in the paper of McKay, Palmer, Read and Robinson [8] as a form of counting version of inclusion and exclusion on two types of properties. In this paper, we provide a proof for inclusion and exclusion on finitely many types of properties in counting version. As an example, the asymptotic number of general cubic graphs via inclusion and exclusion formula is given for this generalization.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.39-46
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• 2024

In this paper, we propose an efficient method for estimating the number of people and their locations using multiple IR-UWB radar sensors. Using three IR-UWB radar sensors in the indoor space, the measured signal from the target is processed to remove the clutter using rejection methods. Then, to further remove the clutter and to determine the presence of the human, the time-frequency image representing the micro-Doppler is obtained and classified by a convolutional neural network. Finally, the system finds the number of human objects and estimates each position in a two-dimensional space. In experiments using the measured data, the system successfully estimated the location and number of individuals with a high accuracy ≈ 88.68 %.