Color information correction of images using lightweight camera systems

The paper is about the idea of a method, with which lightweight camera systems can be extended to get more accurate color meta-information for still images and for frames gained from streamed videos. This meta-information can give more information about the lighting conditions and about the colors of objects in the picture. By having more accurate colors in the picture, many typical in situ and post process visual tasks can be done with greater reliability. This extension could enhance color identification of images taken by low budget camera systems to measurement devices

Histogram based segmentation of shadowed leaf images

This paper corresponds to the solution of some problems realized during ragweed identification experiments, namely the samples collected on the field by botanical experts did not match the initial conditions expected. Reflections and shadows appeared on the image, which made the segmentation more difficult, therefore also the classification was not efficient in previous study. In this work, unlike those solutions, which try to remove the shadow by restoring the illumination of image parts, the focus is on separating leaf and background points based on chromatic information, basically by examining the histograms of the full image and the border. This proposed solution filters these noises in the subspaces of hue, saturation and value space and their combination. It also describes a qualitative technique to select the appropriate values from the filtered outputs. With this method, the results of segmentation improved a lot

Identification of Shadowed Areas to Improve Ragweed Leaf Segmentation

As part of a project targeting geometrical structure analysis and identification of ragweed leaves, sample images were created. Even though images were taken under near optimal conditions, the samples still contain noise of cast shadow. The proposed method improves chromaticity based primary shape segmentation efficiency by identification and re-classification of the shadowed areas. The primary classification of each point is done generally based on thresholding the Hue channel of Hue/Saturation/Value color space. In this work, the primary classification is enhanced by thresholding an intra-class normalized weight computed from the class specific Value channel. The corrective step is the removal of areas marked as shadow from the object class. The idea is based on the assumption that the image contains a single, flat leaf in front of a homogeneous background, but there are no color and illumination restrictions. Thus, parameters of the imaging system and the light sources have influence on homogeneity of image parts; however vague shadows differ only in intensity, and hard shadows can only be dropped on the background

Multi-Agent Simulation of Pedestrian Activity in Historic District

Multi-agent simulation has received a lot of attention in recent years as an emerging design method. To improve the accuracy of the simulation results, the authors provide an optimization scheme that combines multi-agent simulation and visibility graph analysis. Investigate how to improve forecasting accuracy through model optimization