We propose a new objective function for superpixel segmentation. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters

homogeneous region is denoted as a superpixel. In this paper we propose a novel algorithm that segments an image into superpixels employing a new kind of shape centered feature which serve as a seed points for image segmentation, based on Gradient Vector Flow fields (GVF) [14]. The features are located

Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. In this paper we investigate incorporating a priori

We present in this paper a superpixel segmentation algo-rithm called Linear Spectral Clustering (LSC), which pro-duces compact and uniform superpixels with low computa-tional costs. Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a simi-larity metric

the time-series brightness of individual pixels. However, there are limitations with this approach. Pixels are often quite noisy, and can require a lot of memory. This thesis explores the use of superpixels to address these issues. Superpixels, an approach to image segmentation, over-segment a scene

We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries

Abstract — Superpixels aim to group homogenous pixels by a series of characteristics in an image. They decimate redundancy that may be utilized later by more computationally expensive algorithms. The most popular algorithms obtain superpixels based on an energy function on a graph. However

We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Sciencecraft Experiment [1] for operational use onboard the Earth Observing One (EO-1) spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions. 1.

We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Sciencecraft Experiment [1] for operational use onboard the Earth Observing One (EO-1) spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions. 1.

-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate