Evaluating Infrared Thermal Image’s Color Palettes in Hot Tropical Area

The use of Infrared Thermal Scanners proved to be very useful in lots of applications. Using different color palettes, temperatures can be well-represented in the resulting image. However, most color palettes in hot tropical places like the Philippines are unsuitable since the ambient temperature is almost the same as the scanned object or person. This study evaluates twelve (12) known and used color palettes in the market to determine the most suitable for tropical places using the edge/border tracing algorithms Sobel-Feldman and Laplacian. The result shows that color palettes with the most colors produce more noise, making it difficult to distinguish the object scanned from the background. On the other hand, color palettes with three or fewer contrasting colors produce crisp and more detailed results. This study helps developers and researchers efficiently use color combinations suitable for hot weather for an effective thermal scanning and image representation.

Image-guided color mapping for categorical data visualization

Appropriate color mapping for categorical data visualization can significantly facilitate the discovery of underlying data patterns and effectively bring out visual aesthetics.Some systems suggest pre-defined palettes for this task.However,a predefined color mapping is not always optimal,failing to consider users’needs for customization.Given an input cate-gorical data visualization and a reference image,we present an effective method to automatically generate a coloring that resembles the reference while allowing classes to be easily distinguished.We extract a color palette with high perceptual distance between the colors by sampling dominant and discriminable colors from the image’s color space.These colors are assigned to given classes by solving an integer quadratic program to optimize point distinctness of the given chart while preserving the color spatial relations in the source image.We show results on various coloring tasks,with a diverse set of new coloring appearances for the input data.We also compare our approach to state-of-the-art palettes in a controlled user study,which shows that our method achieves comparable performance in class discrimination,while being more similar to the source image.User feedback after using our system verifies its efficiency in automatically generating desirable colorings that meet the user’s expectations when choosing a reference.

Towards natural object-based image recoloring

Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics.Unlike artists who have an accurate grasp of color,ordinary users are inexperienced in color selection and matching,and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results.To address this issue,we introduce a palette-based approach for realistic object-level image recoloring.Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world,and an online recoloring part that first recognizes the object category,and then recommends appropriate realistic candidate colors learned in the offline step for that category.We also provide an intuitive user interface for efficient color manipulation.After color selection,image matting is performed to ensure smoothness of the object boundary.Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.