Charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline, such as (i) automated extraction of charts from documents; (ii) processing of multi-panel charts; (iii) automatic image classifiers to collect chart images at scale; (iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes; (v) applications of chart mining; and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.

Chart Mining: A Survey of Methods for Automated Chart Analysis

Massachusetts Institute of Technology Engineering Systems Division

, This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. The use of Fourier phase information of fingerprint images makes possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. Experimental evaluations using two different types of fingerprint image databases demonstrate efficient recognition performance of the proposed algorithm compared with a typical minutiae-based algorithm and the conventional phase-based algorithm.

A fingerprint recognition algorithm combining phase-based image matching and feature-based matching

https://www.eecis.udel.edu/~shatkay/Course/papers/DemnerEtAl2009.pdf

Annotation and retrieval of clinically relevant images.

Journal images represent an important part of the knowledge stored in the medical literature. Figure classification has received much attention as the information of the image types can be used in a variety of contexts to focus image search and filter out unwanted information or ”noise”, for example non–clinical images. A major problem in figure classification is the fact that many figures in the biomedical literature are compound figures and do often contain more than a single figure type. Some journals do separate compound figures into several parts but many do not, thus requiring currently manual separation. In this work, a technique of compound figure separation is proposed and implemented based on systematic detection and analysis of uniform space gaps. The method discussed in this article is evaluated on a dataset of journal figures of the open access literature that was created for the ImageCLEF 2012 benchmark and contains about 3000 compound figures. Automatic tools can easily reach a relatively high accuracy in separating compound figures. To further increase accuracy efforts are needed to improve the detection process as well as to avoid over–separation with powerful analysis strategies. The tools of this article have also been tested on a database of approximately 150’000 compound figures from the biomedical literature, making these images available as separate figures for further image analysis and allowing to filter important information from them.

https://doc.rero.ch/record/203117/files/009.pdf

Separating compound figures in journal articles to allow for subfigure classification

Essential information is often conveyed pictorially (images, illustrations, graphs, charts, etc.) in biomedical
publications. A clinician's decision to access the full text when searching for evidence in support of clinical decision
is frequently based solely on a short bibliographic reference. We seek to automatically augment these references
with images from the article that may assist in finding evidence.
In a previous study, the feasibility of automatically classifying images by usefulness (utility) in finding evidence
was explored using supervised machine learning and achieved 84.3% accuracy using image captions for modality and
76.6% accuracy combining captions and image data for utility on 743 images from articles over 2 years from a
clinical journal. Our results indicated that automatic augmentation of bibliographic references with relevant images
was feasible. Other research in this area has determined improved user experience by showing images in addition to
the short bibliographic reference. Multi-panel images used in our study had to be manually pre-processed for image
analysis, however. Additionally, all image-text on figures was ignored.
In this article, we report on developed methods for automatic multi-panel image segmentation using not only image
features, but also clues from text analysis applied to figure captions. In initial experiments on 516 figure images we
obtained 95.54% accuracy in correctly identifying and segmenting the sub-images. The errors were flagged as
disagreements with automatic parsing of figure caption text allowing for supervised segmentation. For localizing
text and symbols, on a randomly selected test set of 100 single panel images our methods reported, on the average,
precision and recall of 78.42% and 89.38%, respectively, with an accuracy of 72.02%.

Exploring use of images in clinical articles for decision support in evidence-based medicine

Most fingerprint matching algorithms are based on finding correspondences between minutiae in two fingerprints. In this paper we present a modification of minutiae matching method, which utilizes correlation scores between the local neighborhood areas of corresponding minutiae pairs and the edges that connect neighboring matched minutiae pairs. Minutiae based matching approach considers the overall minutiae distribution pattern between the two fingerprints. Neighborhood correlation score represents the local similarity between the matched pair of minutiae. Edge correlation score gives the resemblance of areas that in between the two corresponding minutiae pairs. Thus, both the global minutiae distribution structure and the local matching similarity between the two fingerprints are considered for final decision. The described matching approach has been tested on FVC2002's DB1 and DB3 database. The experimental results confirm the effectiveness of combining matching scores of different origin - minutia matching score and local correlation matching scores.

Verifying Fingerprint Match by Local Correlation Methods

The matching scores in biometric systems are usually calculated using one enrolled (gallery) template and one test (probe, user) template. In this paper we investigate the dependencies existing between scores related to the same enrolled biometric template or to the same user biometric template. We discuss linear score dependency models which are handled by the Z- or T-normalization, and sample statistics based models. We show that different models might better account for score dependence in different matches. The dependency models might also be different for enrollee or for user specific score sets. Finally, we investigate the application of two such models, Z-normalization and second best score model, to construct enrollee specific verification system decision and combination algorithms. The experiments are performed on NIST BSSR1 biometric score dataset.

Enrolled Template Specific Decisions and Combinations in Verification Systems

One of the difficulties for fingerprint matching lies in the non-linear distortion between fingerprint images originating from same finger. In this paper we present a modification of correlation matching method, which uses thin-plate spline (TPS) as a model for non-linear transformations between two fingerprints. The TPS model is constructed first from the results of a traditional minutia matching algorithm, and by using this model one of the fingerprints is deformed to match the other. After the TPS transformation the correlation scores between the local neighborhood areas of corresponding minutiae pairs and the edges that connect neighboring matched minutiae pairs are calculated and used for final matching score. The FVC2002 DB1 database is used to test the proposed approach. Experimental result shows the improvement when combining TPS deformation model with correlation matching method.

/pdf/fingerprint-matching-using-correlation-and-thin-plate-spline-7z7dt77g76.pdf

Fingerprint Matching Using Correlation and Thin-Plate Spline Deformation Model

Minutiae based fingerprint matching algorithms are wildly used in fingerprint identification and verification applications. However, they may suffer from spurious matches because they do not use the rich local image information. In this paper, we extend minutiae based methods to incorporate such local image information. Our method uses local mutual information, a proven similarity measure in various applications, to improve the matching rate. The overall minutiae distribution pattern between two fingerprints is represented by the initial minutiae matching result, while the mutual information measures the similarity between neighborhoods of matched minutiae, thus enhancing the final matching decision. FVC2002 DB1 and DB3 databases are used to test the proposed approach. Experimental result shows the improvement when combining minutiae matching scores with mutual information scores.

Integrating minutiae based fingerprint matching with local mutual information

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