A persistence lens is a hierarchy of disjoint upper and lower level sets of a continuous luminance image's Reeb graph. The boundary components of a persistence lens's interior components are Jordan curves that serve as a hierarchical segmentation of the image, and may be rendered as vector graphics. A persistence lens determines a varilet basis for the luminance image, in which image simplification is a realized by subspace projection. Image scale space, and image fractal analysis, result from applying a scale measure to each basis function.
Varilet image analysis provides vector representation of hierarchical image segmentation, by vectorizing the Jordan lens facets' boundary components. The luminance value is identical for all Jordan boundaries of the same lens region. Image vectorization methods typically use image segmentation and or edge detectors, e.g. Selinger @cite_57 . @cite_1 merge regions of similar color. @cite_15 use heuristics to group pixels into cells.
@cite_57 @cite_15 @cite_1
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We address the problems of contour detection, bottom-up grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gPb-ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies super pixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art. Abstract We propose a novel label inference approach for segmenting natural images into perceptually meaningful regions. Each pixel is assigned a serial label indicating its category using a Markov Random Field (MRF) model. To this end, we introduce a framework for latent semantic inference of serial labels, called LSI, by integrating local pixel, global region, and scale information of an natural image into a MRF-inspired model. The key difference from traditional MRF based image segmentation methods is that we infer semantic segments in the label space instead of the pixel space. We first design a serial label formation algorithm named Color and Location Density Clustering (CLDC) to capture the local pixel information. Then we propose a label merging strategy to combine global cues of labels in the Cross-Region potential to grasp the contextual information within an image. In addition, to align with the structure of segmentation, a hierarchical label alignment mechanism is designed to formulate the Cross-Scale potential by utilizing the scale information to catch the hierarchy of image at different scales for final segmentation optimization. We evaluate the performance of the proposed approach on the Berkeley Segmentation Dataset and preferable results are achieved. This article presents a hierarchical classification method for high-resolution satellite imagery incorporating extreme value theory EVT-based normalization to calibrate multiple-feature scores. First, a simple linear iterative clustering algorithm is used to over-segment an image to build a superpixel representation of the scene. Then, each superpixel is characterized by three different visual descriptors. Finally, a two-phase classification model is proposed for achieving classification of the scene: 1 in the first phase, a support vector machine SVM with histogram intersection kernel is applied to each feature channel to obtain raw soft probability; and 2 in the second phase, the derived soft outputs are multiplied to build a product space for score-level fusion. The fused scores are subsequently further calibrated using the EVT and fed to an L1-regularized L2-loss SVM to obtain the final result. Experimental analysis on high-resolution satellite scenes shows that the proposed method achieves promising classification results and outperforms other competitive methods. In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (, ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the @math gPb-ucm approach of (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (, TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art.