File Name: extracting and retargeting color mappings from bitmap images of visualizations .zip
Extracting color features of leaf color images. Transactions of the Chinese Society of Agricultural Engineering 18 4 : , Objective evaluation of ultrasound images by computerized realtime color analysis.
In this paper, Harper and Agrawala proposed a system for deconstructing D3. The technique exploits the fact that one can access both SVG elements and data directly in the web browser. The technique is limited in SVG-based visualizations which is only a small part of visualizations. This paper describes technical details and practical applications of the system they built for recognizing and understanding imaged infographics located in document pages. This technique perform vectorization on images and convert them into a set of lines, arcs in the vector form before graphical symbols are constructed. Then, it applied domain knowledge to to identify graphical symbols representing data for each chart type. ReVision applies computer vision and machine learning techniques to identify the chart type.
Metrics details. This paper presents a high-performance general-purpose no-reference NR image quality assessment IQA method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier SVC for distortion classification and support vector regression SVR are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment ENIQA , can assess the quality of different categories of distorted images, and has a low complexity.
Ilia V. He obtained PhD degree in computer science in At present time, Dr. Ilia Safonov is principal research-scientist at Schlumberger Moscow Research. His interests include image and signal processing, machine learning, measurement systems, computer graphics and vision.
Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate.
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