Hierarchical Recognition of English Calling Card by Using Multiresolution Images and Enhanced Neural Network

Kim, Kwang-Baek; Kim, Sungshin

doi:10.1007/11596448_116

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…If the conjoined area is cut off, a connected area is made by applying smearing operation to broken areas. 11 Lastly, by using the area of the bony thorax as a criterion, this paper extracts the interested inner area not including the area of the bony thorax.…”

Section: Extraction Of An Area Of the Internal Bodymentioning

confidence: 99%

Area Extraction of the Liver and Hepatocellular Carcinoma in CT Scans

Kim

2007

J Digit Imaging

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In Korea, hepatocellular carcinoma is the third frequent cause of cancer death, occupying 17.2% among the whole deaths from cancer, and the rate of death from hepatocellular carcinoma comes to about 21 out of 100,000. This paper proposes an automatic method for the extraction of areas being suspicious as hepatocellular carcinoma from computed tomography (CT) scans and evaluates the availability as an auxiliary tool for the diagnosis of hepatocellular carcinoma. For detecting tumors in the internal of the liver from a CT scan, first, an area of the liver is extracted from about 45-50 CT slices obtained by scanning in 2.5-mm intervals starting from the lower part of the chest. In the extraction of an area of the liver, after the unconcerned areas outside of the bony thorax are removed, areas of the internal organs are segmented by using information on the intensity distribution of each organ, and an area of the liver is extracted among the segmented areas by using information on the position and morphology of the liver. Because hepatocellular carcinoma is a hypervascular tumor, the area corresponding to hepatocellular carcinoma appears more brightly than the surroundings in a CT scan, and also takes a spherical shape if the tumor shows expansile growth pattern. By using these features, areas being brighter than the surroundings and globe-shaped are segmented as candidate areas for hepatocellular carcinoma in the area of the liver, and then, areas appearing at the same position in successive CT slices among the candidates are discriminated as hepatocellular carcinoma. For the performance evaluation of the proposed method, experimental results obtained by applying the proposed method to CT scans were compared with the diagnoses by radiologists. The evaluation results showed that all areas of the liver and hypervascular tumors were extracted exactly and the proposed method has a high availability as an auxiliary diagnosis tool for the discrimination of liver tumors.

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Section: Extraction Of An Area Of the Internal Bodymentioning

confidence: 99%

Area Extraction of the Liver and Hepatocellular Carcinoma in CT Scans

Kim

2007

J Digit Imaging

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“…Passport recognition consists of code recognition and face verification. In code recognition process, code region is extracted and gradient of the region is corrected using Sobel mask, horizontal smearing method [2] and 8-directional edge tracking algorithm [3]. The extracted individual code is binarized by iterative binarization method and recognized by improved FKCN algorithm.…”

Section: Introductionmentioning

confidence: 99%

PCA-Based Face Verification and Passport Code Recognition Using Improved FKCN Algorithm

Kim

Woo

2008

2008 Eighth International Conference on Intelligent Systems Design and Applications

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In this paper, we proposed methods to recognize passport codes and verify face for efficient management of inbound and outbound passengers and swift immigration control. A passport image can be slanted because the image is acquired by scanning. Therefore gradient correction is very important in character extraction, character recognition, and face verification processes. Gradient correction of a scanned passport image is performed based on center points and horizontal line of both side tips of extracted line of codes applying horizontal smearing method to the scanned passport image. Individual code of passport is extracted using 8-directional edge tracking algorithm and then passport codes are recognized by improved FKCN(Fuzzy Kohonen Clustering Networks) using frequency. Normalized gray-level correlation matching algorithm is used to extract face region and then feature vector of face region is extracted using PCA algorithm. Finally we can check whether face of a passport is forgery or not by comparing the extracted feature vector of face region with the face information of a passport holder extracted from passport database using recognized passport codes. From the experiment on face-forged passports and slanted passport images with our proposed methods in order to evaluate performance of the proposed passport code recognition and face verification methods, we could see the proposed methods is more efficient than conventional methods in passport code recognition and face verification.

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The Modified ART1 Network using Multiresolution Mergence : Mixed Character Recognition

Choi¹,

Kim²

2007

The KIPS Transactions:PartB

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Hierarchical Recognition of English Calling Card by Using Multiresolution Images and Enhanced Neural Network

Cited by 3 publications

References 5 publications

Area Extraction of the Liver and Hepatocellular Carcinoma in CT Scans

Area Extraction of the Liver and Hepatocellular Carcinoma in CT Scans

PCA-Based Face Verification and Passport Code Recognition Using Improved FKCN Algorithm

The Modified ART1 Network using Multiresolution Mergence : Mixed Character Recognition

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