We present an image segmentation algorithm that is based on spatially adaptive color and texture features. The proposed algorithm is based on a previously proposed algorithm but introduces a number of new elements. We use a new set of texture features based on a steerable filter decomposition. The steerable filters combined with a new spatial texture segmentation scheme provide a finer and more...

Scene segmentation is widely used in autonomous driving for environmental perception. Semantic scene (3S) has gained considerable attention owing to its rich semantic information. It assigns labels the pixels an image, thereby enabling automatic image labeling. Current approaches are based mainly on convolutional neural networks (CNN), however, they rely numerous labels. Therefore, use of a sma...

Digital image segmentation is a process in which one assigns distinct labels to different objects in a digital image. The MOFS (Multi Object Fuzzy Segmentation) algorithm has been successfully applied to the segmentation of images from several modalities. However, the traditional MOFS algorithm fails when applied to images whose composing objects are characterized by textures whose patterns can...

We show that an adaptive robust filtering based image segmentation model can be used for microvasculature network detection and quantitative characterization in epifluorescence-based high resolution images. Inhomogeneous fluorescence contrast due to the variable binding properties of the lectin marker and leakage hampers accurate vessel segmentation. We use a robust adaptive filtering approach ...