Topic
Visual comparison
About: Visual comparison is a research topic. Over the lifetime, 164 publications have been published within this topic receiving 2804 citations.
Papers published on a yearly basis
Papers
TL;DR: This paper proposes a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined, and provides a survey of work in information visualization related to comparison.
Abstract: Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools.
646 citations
TL;DR: In this article, the authors reviewed the various quality metrics available in the literature, for assessing the quality of fused image, and evaluated the performance of the fused image by two variants such as with reference image and without reference image.
397 citations
Patent•
16 Apr 2008
TL;DR: A graphical user interface on a computer provides for the analysis of location specific data and the presentation of analysis results for visual comparison by a user as discussed by the authors, where results of the analysis are visually presented as icons subdivided into regions and arranged in such a way that the user is able to associate each icon with a data location.
Abstract: A graphical user interface on a computer provides for the analysis of location specific data and the presentation of analysis results for visual comparison by a user. Results of the analysis are visually presented as icons subdivided into regions and arranged in such a way that the user is able to associate each icon with a data location. A visual presentation of results in the icons and regions allows a user to visually compare the analysis results in two or more data sets according to location. The graphical user interface further provides for the definition and adjustment of an analysis through the interaction of a user with a graphical representation of the analysis. In some cases, the visual presentation of results tracks the analysis adjustments so the user can visually observe the effects that the adjustments have on the results. A method of interacting with the interface to define an analysis and represent results and a method of presenting two or more data sets using the interface are described. The interface can be used to analyze and visually compare the results of location specific data from a number of sources and is illustrated in a flow cytometry application.
196 citations
Patent•
9 May 1972
TL;DR: In this article, a method for identifying individuals using selected characteristic body curves which are usually substantially constant characteristic facial curves derived from one or more images of the individual being identified is presented, and the curves may be used for visual comparison with an image of an individual in an identification card format.
Abstract: A method for identifying individuals using selected characteristic body curves which are usually substantially constant characteristic facial curves derived from one or more images of the individual being identified. The curves may be used for visual comparison with an image of the individual in an identification card format, or the curves may be stored in data processing apparatus and reproduced for comparison, or automatically compared, with an image of the individual presented for use in conjunction with the data processing apparatus.
165 citations
1 Jan 2008
TL;DR: In this article, the authors focus on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements, and the inconsistency between the visual evaluations and quantitative analyses in the above three cases demonstrate that the seven quantitative indicators cannot provide reliable measurements for quality assessment of remote sensing images.
Abstract: This paper focuses on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements. The seven indexes are Mean Bias (MB), Variance Difference (VD), Standard Deviation Difference (SDD), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), Relative Dimensionless Global Error (ERGAS), and Q4 Quality Index (Q4), which were also used in the IEEE GRSS 2006 Data Fusion Contest. Four testing images are generated to evaluate the indexes. Visual comparison and digital classification demonstrate that the four testing images have the same quality for remote sensing applications; however, the seven evaluation methods provide different measurements indicating that the four images have varying qualities. The image fusion quality evaluation by Alparone, et al. (2004) and that by the IEEE GRSS 2006 data fusion contest (Alparone, et al., 2007) are also analyzed. Significant discrepancy between the quantitative measurements, visual comparison and final ranking has been found in both evaluations. The inconsistency between the visual evaluations and quantitative analyses in the above three cases demonstrate that the seven quantitative indicators cannot provide reliable measurements for quality assessment of remote sensing images.
112 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 7 |
| 2020 | 15 |
| 2019 | 12 |
| 2018 | 13 |
| 2017 | 12 |
| 2016 | 9 |