An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation

An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.National Science Foundation (IIS-9912573, EIA-9809340

A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods

Performance Assessment of Feature Detection Algorithms: A Methodology and Case Study on Corner Detectors

In this paper we describe a generic methodology for evaluating the labeling performance of feature detectors. We describe a method for generating a test set and apply the methodology to the performance assessment of three well-known corner detectors: the Kitchen-Rosenfeld, Paler et al. and Harris-Stephens corner detectors. The labeling deficiencies of each of these detectors is related to their discrimination ability between corners and various of the features which comprise the class of noncorners

Automated detection of extended sources in radio maps: progress from the SCORPIO survey

Automated source extraction and parameterization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parameterization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, including also different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU survey. The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure

A Novel Active Contour Model for Texture Segmentation

Texture is intuitively defined as a repeated arrangement of a basic pattern or object in an image. There is no mathematical definition of a texture though. The human visual system is able to identify and segment different textures in a given image. Automating this task for a computer is far from trivial. There are three major components of any texture segmentation algorithm: (a) The features used to represent a texture, (b) the metric induced on this representation space and (c) the clustering algorithm that runs over these features in order to segment a given image into different textures. In this paper, we propose an active contour based novel unsupervised algorithm for texture segmentation. We use intensity covariance matrices of regions as the defining feature of textures and find regions that have the most inter-region dissimilar covariance matrices using active contours. Since covariance matrices are symmetric positive definite, we use geodesic distance defined on the manifold of symmetric positive definite matrices PD(n) as a measure of dissimlarity between such matrices. We demonstrate performance of our algorithm on both artificial and real texture images

Fast 3D keypoints detector and descriptor for view-based 3D objects recognition

International audienceIn this paper, we propose a new 3D object recognition method that employs a set of 3D keypoints extracted from point cloud representation of 3D views. The method makes use of the 2D organization of range data produced by 3D sensor. Our novel 3D interest points approach relies on surface type classifi-cation and combines the Shape Index (SI) - curvedness(C) map with the Gaus-sian (H) - Mean (K) map. For each extracted keypoint, a local description using the point and its neighbors is computed by joining the Shape Index histogram and the normalized histogram of angles between normals. This new proposed descriptor IndSHOT stems from the descriptor CSHOT (Color Signature of Histograms of OrienTations) which is based on the definition of a local, robust and invariant Reference Frame RF. This surface patch descriptor is used to find the correspondences between query-model view pairs in effective and robust way. Experimental results on Kinect based datasets are presented to validate the proposed approach in view based 3D object recognition