• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.151-160
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• 2002

Cow image processing technique would be useful not only for recognizing an individual but also for establishing the image database and analyzing the shape of cows. A cow (Holstein) has usually the unique speckle pattern. In this study, the individual recognition of cow was carried out using the speckle pattern and the content-based image retrieval technique. Sixty cow images of 16 heads were captured under outdoor illumination, which were complicated images due to shadow, obstacles and walking posture of cow. Sixteen images were selected as the reference image for each cow and 44 query images were used for evaluating the efficiency of individual recognition by matching to each reference image. Run-lengths and positions of runs across speckle area were calculated from 40 horizontal line profiles for ROI (region of interest) in a cow body image after 3 passes of 5$\times$5 median filtering. A similarity measure for recognizing cow individuals was calculated using Euclidean distance of normalized G-frame histogram (GH). normalized speckle run-length (BRL), normalized x and y positions (BRX, BRY) of speckle runs. This study evaluated the efficiency of individual recognition of cow using Recall(Success rate) and AVRR(Average rank of relevant images). Success rate of individual recognition was 100% when GH, BRL, BRX and BRY were used as image query indices. It was concluded that the histogram as global property and the information of speckle runs as local properties were good image features for individual recognition and the developed system of individual recognition was reliable.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.100-106
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• 2015

In this paper, we propose a fast algorithm for low-power image enhancement method for mobile LCD. In the proposed fast algorithm, the spatial resolution of the input image is significantly reduced, and the image characteristics are analyzed on the reduced resolution image to find a dimming rate adaptive to the image content, thereby saving power. The proposed fast adaptive dimming and image enhancement algorithm is implemented as an application that runs on an Android device. Image quality evaluation and running time analysis experiments on the device indicate that the proposed fast algorithm jointly minimizes the quality degradation and power consumption, reducing the required computation load by over 95%.

• ETRI Journal
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• v.32 no.6
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• pp.871-880
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• 2010

Many applications dealing with image management need a technique for removing duplicate images or for grouping related (near-duplicate) images in a database. This paper proposes a concentric circle-based image signature which makes it possible to detect near-duplicates rapidly and accurately. An image is partitioned by radius and angle levels from the center of the image. Feature values are calculated using the average or variation between the partitioned sub-regions. The feature values distributed in sequence are formed into an image signature by hash generation. The hashing facilitates storage space reduction and fast matching. The performance was evaluated through discriminability and robustness tests. Using these tests, the particularity among the different images and the invariability among the modified images are verified, respectively. In addition, we also measured the discriminability and robustness by the distribution analysis of the hashed bits. The proposed method is robust to various modifications, as shown by its average detection rate of 98.99%. The experimental results showed that the proposed method is suitable for near-duplicate detection in large databases.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.265-274
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• 2001

In this paper, we propose a content-based face retrieval system which can retrieve a face based on a facial feature region. Instead of using keyword such as a resident registration number or name for a query, the our system uses a facial image as a visual query. That is, we recognize a face based on a specific feature region including eyes, nose, and mouth. For this, we extract the feature region using the color information based on HSI color model and the edge information from wavelet transformed image, and then recognize the feature region using neural network. The proposed system is implemented on client/server environment based on Oracle DBMS for a large facial image database. In the experiment with 150 various facial images, the proposed method showed about 88.3% recognition rate.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.18-25
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• 2017

Recently, VR HMD (virtual reality head mounted display) has been utilized for virtual training, entertainment, vision therapy, and optometry. In particular, virtual reality contents are increasingly used for vision therapy and optometry. Accordingly, high-quality virtual reality contents such as a natural vision of life is required. Therefore, it is necessary to study the content production according to the optical characteristics of the VR HMD. The purpose of this paper is to suggest a proper FOV (field of view) of contents according to the distortion rate. We produced virtual reality contents and obtained distorted images by virtual camera. The distortion rate is calculated by using the distorted image. It is proved that the optimal FOV of the VR content with the minimum distortion is $90{\sim}100^{\circ}$. The results of this study are expected to be applied to the production of high quality contents.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.71-79
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• 2008

This study proposed a content-based image retrieval system that uses RGB maximum frequency indexing and BW clustering in order to deal with existing retrieval errors using histogram. We split RGB from RGB color images, obtained histogram which was evenly split into 32 bins, calculated and analysed pixels of each area at histogram of R, G, B and obtained the maximum value. We indexed the color information obtained, obtained 100 similar images using the values, operated the final image retrieval system using the total number and distribution rate of clusters. The algorithm proposed in this study used space information using the features obtained from R, G, and B and clusters to obtain effective features, which overcame the disadvantage of existing gray-scale algorithm that perceived different images as same if they have the same frequencies of shade. As a result of measuring the performances using Recall and Precision, this study found that the retrieval rate and priority of the proposed algorithm are more outstanding than those of existing algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.27-33
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• 2014

This paper proposes a gradation image processing method for the development of a Road Sign Recognition Platform (RReP), which aims to facilitate the rapid and accurate management and surveying of approximately 160,000 road signs installed along the highways, national roadways, and local roads in the cities, districts (gun), and provinces (do) of Korea. RReP is based on GPS(Global Positioning System), IMU(Inertial Measurement Unit), INS(Inertial Navigation System), DMI(Distance Measurement Instrument), and lasers, and uses an imagery information collection/classification module to allow the automatic recognition of signs, the collection of shapes, pole locations, and sign-type data, and the creation of road sign registers, by extracting basic data related to the shape and sign content, and automated database design. Image division and merging, which were applied in this study, produce superior results compared with local binarization method in terms of speed. At the results, larger texts area were found in images, the accuracy of text recognition was improved when images had been gradated. Multi-threshold values of natural scene images are used to improve the extraction rate of texts and figures based on pattern recognition.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.

• Journal of Powder Materials
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• v.17 no.1
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• pp.13-22
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• 2010

The unique features of spark plasma sintering process are the possibilities of a very fast heating rate and a short holding time to obtain fully dense materials. $\beta$-SiC powder with 0, 2, 6, 10 wt% of $\alpha$-SiC particles (seeds) and 4 wt% of Al-B-C (sintering aids) were spark plasma sintered at $1700-1850^{\circ}C$ for 10 min. The heating rate, applied pressure and sintering atmosphere were kept at $100^{\circ}C/min$, 40 MPa and a flowing Ar gas (500 CC/min). Microstructural development of SiC as function of seed content and temperature during spark plasma sintering was investigated quantitatively and statistically using image analysis. Quantitative image analyses on the sintered SiC ceramics were conducted on the grain size, aspect ratio and grain size distribution of SiC. The microstructure of SiC sintered up to $1700^{\circ}C$ consisted of equiaxed grains. In contrast, the growth of large elongated SiC grains in small matrix grains was shown in sintered bodies at $1750^{\circ}C$ and the plate-like grains interlocking microstructure had been developed by increasing sintering temperature. The introduction of $\alpha$-SiC seeds into $\beta$-SiC accelerated the grain growth of elongated grains during sintering, resulting in the plate-like grains interlocking microstructure. In the $\alpha$-SiC seeds added in $\beta$-SiC, the rate of grain growth decreased with $\alpha$-SiC seed content, however, bulk density and aspect ratio of grains in sintered body increased.

• ETRI Journal
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• v.38 no.2
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• pp.347-355
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• 2016

In face examinations, gender classification (GC) is one of several fundamental tasks. Recent literature on GC primarily utilizes datasets containing high-resolution images of faces captured in uncontrolled real-world settings. In contrast, there have been few efforts that focus on utilizing low-resolution images of faces in GC. We propose a GC method based on a pixel classifier boosting with modified census transform features. Experiments are conducted using large datasets, such as Labeled Faces in the Wild and The Images of Groups, and standard protocols of GC communities. Experimental results show that, despite using low-resolution facial images that have a 15-pixel inter-ocular distance, the proposed method records a higher classification rate compared to current state-of-the-art GC algorithms.