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

Joint Model and Observation Cues for Single-Image Shadow Detection

Shadows, which are cast by clouds, trees, and buildings, degrade the accuracy of many tasks in remote sensing, such as image classification, change detection, object recognition, etc. In this paper, we address the problem of shadow detection for complex scenes. Unlike traditional methods which only use pixel information, our method joins model and observation cues. Firstly, we improve the bright channel prior (BCP) to model and extract the occlusion map in an image. Then, we combine the model-based result with observation cues (i.e., pixel values, luminance, and chromaticity properties) to refine the shadow mask. Our method is suitable for both natural images and satellite images. We evaluate the proposed approach from both qualitative and quantitative aspects on four datasets. The results demonstrate the power of our method. It shows that the proposed method can achieve almost 85% F-measure accuracy both on natural images and remote sensing images, which is much better than the compared state-of-the-art methods

Joint Model and Observation Cues for Single-Image Shadow Detection

Shadows, which are cast by clouds, trees, and buildings, degrade the accuracy of many tasks in remote sensing, such as image classification, change detection, object recognition, etc. In this paper, we address the problem of shadow detection for complex scenes. Unlike traditional methods which only use pixel information, our method joins model and observation cues. Firstly, we improve the bright channel prior (BCP) to model and extract the occlusion map in an image. Then, we combine the model-based result with observation cues (i.e., pixel values, luminance, and chromaticity properties) to refine the shadow mask. Our method is suitable for both natural images and satellite images. We evaluate the proposed approach from both qualitative and quantitative aspects on four datasets. The results demonstrate the power of our method. It shows that the proposed method can achieve almost 85% F-measure accuracy both on natural images and remote sensing images, which is much better than the compared state-of-the-art methods