• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.639-641
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• 2012

People detection using HOG linear SVM classification has been successfully applied. Also, HOG combined with LBP, which reflects texture informations, shows improved performance. In this paper, we analyze various gamma correction methods. We also analyze results obtained using HOG+LBP methods.

• Journal of Microbiology and Biotechnology
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• v.25 no.5
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• pp.709-717
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• 2015

Airborne bacteria from hog farms may have detrimental impacts on human health, particularly in terms of antibiotic resistance and pathogen zoonosis. Despite human health risks, very little is known about the composition and diversity of airborne bacteria from hog farms and hog-related spray fields. We used pyrosequencing analysis of 16S rRNA genes to compare airborne bacterial communities in a North Carolina hog farm and lagoon spray field. In addition, we isolated and identified antibiotic-resistant bacteria from both air samples. Based on 16S rRNA gene pyrosequence analysis, Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria were the dominant phyla in airborne bacterial communities from both hog farm and spray field sites. Within the Firmicutes genera, Clostridium spp. were more abundant in the hog farm, whereas Staphylococcus spp. were higher in the spray field. The presence of opportunitic pathogens, including several Staphylococcus species and Propionibacterium acnes, was detected in both bioaerosol communities based on phylogenetic analysis. The isolation and identification of antibiotic-resistant bacteria from air samples also showed similar results with dominance of Actinobacteria and Proteobacteria in both hog farm and spray field air. Thus, the existence of opportunistic pathogens and antibiotic resistant bacteria in airborne communities evidences potential health risks to farmers and other residents from swine bioaerosol exposure.

• Culinary science and hospitality research
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• v.23 no.2
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• pp.126-134
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• 2017

The purpose of this paper was to examine the dynamics of the price discovery function between lean hog futures and spot markets using the vector error correction model (VECM). The researcher also investigated the existence of the long-run equilibrium relationship between the lean hog futures and spot markets. Daily time series data of lean hog futures and spot observed in the Korean market during the period from 5 Jan. 2011 to 28 Dec. 2012 were analyzed. To examine the price discovery, this study employed the Gonzalo and Granger's (1995) information ratio and Hasbrock's (1995) information ratio measurement method. The significant findings of the study are summarized as follows. First, lean hog futures and spot market are significantly correlated. Secondly, the lean hog future market plays a more dominant role in price discovery than the spot market. Finally, price discovery measures based on the VECM suggested that the lean hog future market plays a more dominant role in price discovery than the lean hog spot market. This is the important systematic empirical work to find the relationship between the lean hog future and spot market.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.654-662
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• 2010

In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using second-stage cascade method, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: (i) Histogram of Oriented Gradient (HOG) which includes gradient information and differential magnitude; (ii) Curvature-HOG which is based on four different curvature features per pixel. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using both HOG and curvature-HOG. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. For the recognition-failed image, the other feature and strong classification will be used for the second stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1289-1301
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• 2015

Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.259-266
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• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Proceedings of the Microbiological Society of Korea Conference
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• 2007.05a
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• pp.120-122
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• 2007

All living organisms use numerous signal-transduction pathways to sense and respond to their environments and thereby survive and proliferate in a range of biological niches. Molecular dissection of these signalling networks has increased our understanding of these communication processes and provides a platform for therapeutic intervention when these pathways malfunction in disease states, including infection. Owing to the expanding availability of sequenced genomes, a wealth of genetic and molecular tools and the conservation of signalling networks, members of the fungal kingdom serve as excellent model systems for more complex, multicellular organisms. Here, we employed Cryptococcus neoformans as a model system to understand how fungal-signalling circuits operate at the molecular level to sense and respond to a plethora of environmental stresses, including osmoticshock, UV, high temperature, oxidative stress and toxic drugs/metabolites. The stress-activated p38/Hog1 MAPK pathway is structurally conserved in many organisms as diverse as yeast and mammals, but its regulation is uniquely specialized in a majority of clinical Cryptococcus neoformans serotype A and D strains to control differentiation and virulence factor regulation. C. neoformans Hog1 MAPK is controlled by Pbs2 MAPK kinase (MAPKK). The Pbs2-Hog1 MAPK cascade is controlled by the fungal "two-component" system that is composed of a response regulator, Ssk1, and multiple sensor kinases, including two-component.like (Tco) 1 and Tco2. Tco1 and Tco2 play shared and distinct roles in stress responses and drug sensitivity through the Hog1 MAPK system. Furthermore, each sensor kinase mediates unique cellular functions for virulence and morphological differentiation. We also identified and characterized the Ssk2 MAPKKK upstream of the MAPKK Pbs2 and the MAPK Hog1 in C. neoformans. The SSK2 gene was identified as a potential component responsible for differential Hog1 regulation between the serotype D sibling f1 strains B3501 and B3502 through comparative analysis of their meiotic map with the meiotic segregation of Hog1-dependent sensitivity to the fungicide fludioxonil. Ssk2 is the only polymorphic component in the Hog1 MAPK module, including two coding sequence changes between the SSK2 alleles in B3501 and B3502 strains. To further support this finding, the SSK2 allele exchange completely swapped Hog1-related phenotypes between B3501 and B3502 strains. In the serotype A strain H99, disruption of the SSK2 gene dramatically enhanced capsule biosynthesis and mating efficiency, similar to pbs2 and hog1 mutations. Furthermore, ssk2, pbs2, and hog1 mutants are all hypersensitive to a variety of stresses and completely resistant to fludioxonil. Taken together, these findings indicate that Ssk2 is the critical interface protein connecting the two-component system and the Pbs2-Hog1 pathway in C. neoformans.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.66-72
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• 2014

Histogram of oriented gradient (HOG) feature is widely used in vision-based pedestrian detection. The interpolation is the most important technique in HOG feature calculation to provide high detection rate. In interpolation technique of HOG feature calculation, two nearest orientation bins to gradient orientation for each pixel and the corresponding weights are required. In this paper, therefore, an efficient gradient orientation bin and weight calculation circuit for HOG feature is proposed. In the proposed circuit, pre-calculated values are defined in tables to avoid the operations of tangent function and division, and the size of tables is minimized by utilizing the characteristics of tangent function and weights for each gradient orientation. Pipeline architecture is adopted to the proposed circuit to accelerate the processing speed, and orientation bins and the corresponding weights for each pixel are calculated in two clock cycles by applying efficient coarse and fine search schemes. Since the proposed circuit calculates gradient orientation for each pixel with the interval of $1^{\circ}$ and determines both orientation bins and weights required in interpolation technique, it can be utilized in HOG feature calculation to support interpolation technique to provide high detection rate.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.39-47
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• 2019

In this paper, we proposed extracting modified Histogram of Oriented Gradients (HOG) features using background removal when tracking pedestrians in real time. HOG feature extraction has a problem of slow processing speed due to large computation amount. Background removal has been studied to improve computation reductions and tracking rate. Area removal was carried out using S and V channels in HSV color space to reduce feature extraction in unnecessary areas. The average S and V channels of the video were removed and the input video was totally dark, so that the object tracking may fail. Histogram equalization was performed to prevent this case. HOG features extracted from the removed region are reduced, and processing speed and tracking rates were improved by extracting clear HOG features. In this experiment, we experimented with videos with a large number of pedestrians or one pedestrian, complicated videos with backgrounds, and videos with severe tremors. Compared with the existing HOG-SVM method, the proposed method improved the processing speed by 41.84% and the error rate was reduced by 52.29%.

• Korean Journal of Agricultural Science
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• v.12 no.2
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• pp.341-348
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• 1985

This study aims at analysis hog cycles and seasonal price patterns, and at develop the procedure for price forecasting based on the relative price ratios by farmers. Seasonal price patterns have been a persistent feature of hog markets. Some month have historically high price and other months historically low price. Hog price tend to be high in Feb, May, June, Sept, winter (Nov. to Jan.) and tend to be low in the other months. There have been four price cycles for 12 years, 1972-1984, the length of the hog price cycle has varied from 24 month to 42 months, with the irregular frequency. The increasing period of the price cycle lasted 23 months and the decreasing period of the price cycle lasted 13 months. Tables 2, 3, 4 in this study show average hog price ratios and the number of times price fall, rose for one, two, and three months ahead of each calendar month.