A hierarchical image segmentation algorithm based on an observation scale

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenszwalb and Huttenlocher. Quantitative and qualitative assessments of the method on Berkeley image database shows efficiency, ease of use and robustness of our method

A study of observation scales based on Felzenswalb-Huttenlocher dissimilarity measure for hierarchical segmentation

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method

Visual-hint Boundary to Segment Algorithm for Image Segmentation

Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.Comment: 45 page

Semantic 3D Occupancy Mapping through Efficient High Order CRFs

Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.Comment: IROS 201

New characterizations of minimum spanning trees and of saliency maps based on quasi-flat zones

We study three representations of hierarchies of partitions: dendrograms (direct representations), saliency maps, and minimum spanning trees. We provide a new bijection between saliency maps and hierarchies based on quasi-flat zones as used in image processing and characterize saliency maps and minimum spanning trees as solutions to constrained minimization problems where the constraint is quasi-flat zones preservation. In practice, these results form a toolkit for new hierarchical methods where one can choose the most convenient representation. They also invite us to process non-image data with morphological hierarchies

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task

Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map

This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations