In Seop Na

Separation of left and right lungs using 3-dimensional information of sequential computed tomography images and a guided dynamic programming algorithm.

Objective: This article presents a new computerized scheme that aims to accurately and robustly separate left and right lungs on computed tomography (CT) examinations. Methods: We developed and tested a method to separate the left and right lungs using sequential CT information and a guided dynamic programming algorithm using adaptively and automatically selected start point and end point with especially severe and multiple connections. Results: The scheme successfully identified and separated all 827 connections on the total 4034 CT images in an independent testing data set of CT examinations. The proposed scheme separated multiple connections regardless of their locations, and the guided dynamic programming algorithm reduced the computation time to approximately 4.6% in comparison with the traditional dynamic programming and avoided the permeation of the separation boundary into normal lung tissue. Conclusions: The proposed method is able to robustly and accurately disconnect all connections between left and right lungs, and the guided dynamic programming algorithm is able to remove redundant processing.

Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.

Object Recognition by Combining Binary Local Invariant Features and Color Histogram

In this paper, we propose an approach for object recognition using binary local invariant features and color information. In our approach, we use a fast detector for key point detection and binary local features descriptor for key point description. For local feature matching, the Fast library for Approximated Nearest Neighbors (FLANN) is applied to match the query image and reference image in data set. A homography matrix which represents transformation of object in scene image and reference image is estimated from matching pairs by using the Optimized Random Sample Consensus Algorithm (ORSA). Then, we detect object location in the image, and remove background of image. Next, significant color feature is used to calculate global color histogram since it reflects main content of primitive image and also ignores noises. Similarity of query image and reference object image is a linear combination of color histogram correlation and number of feature matches. As a result, the proposed method can overcome drawbacks of object recognition method using only local features or global features. In addition, the use of binary feature makes feature description as well as feature matching faster to meet the requirement of a real time system. For evaluation, we experiment with two well-known and latest local invariant features including the Oriented Fast and Rotated Binary Robust Independent Elementary Features (ORB) and Fast Retina Key point (FREAK) and a planar object data set. According to the result, ORB feature shows that it is powerful as our system obtained the higher accuracy and fast processing time. The experimental results also proved that combination of binary local invariant feature and significant color is effective for planar object recognition.

Lanes Detection in PCR Gel Electrophoresis Images

This study aims at development of methods to track the center of and detect lanes as the first step of automated tool to analyze rose DNA using PCR gel electrophoresis images. Although several research results have been previously reported using projection profiles in a whole image, it is still challenge to track the center of and detect bent lanes using projection profiles. To resolve the problem, we partitioned an input image into small images and found local maxima, potential lane centers, on the vertical projection in each partitioned image. We used 29 PCR gel images including 934 lanes to evaluate the performance of the proposed scheme. Proposed scheme achieved the performance of the sensitivity of 99.0% and the precision of 98.9%.

Automatic extraction of text regions from document images by multilevel thresholding and k-means clustering

Textual data plays an important role in a number of applications such as image database indexing, document understanding, and image-based web searching. The target of automatic real-life text extracting in document images without character recognition module is to identify image regions that contain only text. These textual regions can then be either input of optical character recognition application or highlighted for user focusing. In this paper we propose a method which consists of three stages-preprocessing which improves contrast of grayscale image, multi-level thresholding for separating textual region from non-textual object such as graphics, pictures, and complex background, and heuristic filter, recursive filter for text localizing in textual region. In many of these applications, it is not necessary to identify all the text regions, therefor we emphasize on identifying important text region with relatively large size and high contrast. Experimental results on real-life dataset images demonstrate that the proposed method is effective in identifying textual region with various illuminations, size and font from various types of background.

Separation of Text and Non-text in Document Layout Analysis using a Recursive Filter

A separation of text and non-text elements plays an important role in document layout analysis. A number of approaches have been proposed but the quality of separation result is still limited due to the complex of the document layout. In this paper, we present an efficient method for the classification of text and non-text components in document image. It is the combination of whitespace analysis with multi-layer homogeneous regions which called recursive filter. Firstly, the input binary document is analyzed by connected components analysis and whitespace extraction. Secondly, a heuristic filter is applied to identify non-text components. After that, using statistical method, we implement the recursive filter on multi-layer homogeneous regions to identify all text and non-text elements of the binary image. Finally, all regions will be reshaped and remove noise to get the text document and non-text document. Experimental results on the ICDAR2009 page segmentation competition dataset and other datasets prove the effectiveness and superiority of proposed method.

Staff Line Removal Using Line Adjacency Graph and Staff Line Skeleton for Camera-Based Printed Music Scores

On camera-based music scores, curved and uneven staff-lines tend to incur more frequently, and with the loss in performance of binarization methods, line thickness variation and space variation between lines are inevitable. We propose a novel and effective staff-line removal method based on following 3 main ideas. First, the state-of-the-art staff-line detection method, Stable Path, is used to extract staff-line skeletons of the music score. Second, a line adjacency graph (LAG) model is exploited in a different manner of over segmentation to cluster pixel runs generated from the run-length encoding (RLE) of the image. Third, a two-pass staff-line removal pipeline called filament filtering is applied to remove clusters lying on the staff-line. Our method shows impressive results on music score images captured from cameras, and gives high performance when applied to the ICDAR/GREC 2013 database.

A robust system for document layout analysis using multilevel homogeneity structure

This paper presents a robust system for the document layout analysis.The proposed system is based on multilevel homogeneity structure (MHS).The proposed system is designed to work with many different document languages.Our system is tested on four published datasets with different document languages.The proposed system (MHS) won the RDCL-2015 competition (ICDAR2015). One of the difficulties in the understanding of document images is document layout analysis, which is the first step in document image modeling. In this paper, a robust system for which a multilevel-homogeneity structure is used in accordance with a hybrid methodology is proposed to deal with this problem. Our system consists of the following three main stages: classification, segmentation, and refinement and labeling. Different from other page segmentation methods, the proposed system includes an efficient algorithm to detect table regions in document images. Besides, to create an effective application, the proposed system is designed to work with a variety of document languages. The proposed method was tested with the ICDAR2015 competition (RDCL-2015) and three other published datasets in different languages. The results of these tests show that the accuracy of proposed system is superior to the previous methods.

A mixture model using Random Rotation Bounding Box to detect table region in document image

A robust system for detecting table regions in the document image is presented.The ruling line and non-ruling line table regions are detected by two scanning processes.The Random Rotation Bounding Box is proposed for describing the table region.Our method is tested on three datasets: ICDAR2013 table competition, UNLV, and Diotek. Table detection in the document image is still a challenging problem due to the variety of table structures and the complexity of document layout. In this paper, we propose a novel method for detecting table regions by using a new shape which is called Random Rotation Bounding Box. This shape is used for illustration and description of the table regions. Based on it, our system performs the following three fundamental steps to detect the table zones: classification of the text and non-text elements in the document image, detection of the ruling-line tables, and identification of the non-ruling-line tables. Different from other methods, our approach can detect most kinds of tables with high precision even when it is skewed. Besides, the proposed method is also designed to fit in the document layout analysis system. Our algorithm has been tested on the two well-known, and a commercial datasets: ICDAR2013 table competition, UNLV, and Diotek. Experimental results on these databases show that our method is more robust and efficient than previous systems.

Hybrid page segmentation using multilevel homogeneity structure

This paper presents a hybrid method of page segmentation based on the combination of connected component analysis and classification on multilevel homogeneous regions. This suggests an iterative method. In which, connected component analysis is used to classify the non-text elements at each level of homogeneous region, and multilevel homogeneity structure is used to ensure this classification can identify all non-text elements. The result of this iterative method is the two documents, text document and non-text document. On text document, adaptive mathematical morphology in each text homogeneous region will give us the corresponding text region. On the non-text document, more detailed classification of the non-text components are made to get separators, tables, images, etc. For evaluation, we experiment our method with datasets from ICDAR2009 page segmentation competition. According to the results, our proposed method achieves the higher accuracy compared to other methods. This proves the effectiveness and superiority of our proposed method.