Shape-based invariant features extraction for object recognition

International audienceThe emergence of new technologies enables generating large quantity of digital information including images; this leads to an increasing number of generated digital images. Therefore it appears a necessity for automatic systems for image retrieval. These systems consist of techniques used for query specification and re-trieval of images from an image collection. The most frequent and the most com-mon means for image retrieval is the indexing using textual keywords. But for some special application domains and face to the huge quantity of images, key-words are no more sufficient or unpractical. Moreover, images are rich in content; so in order to overcome these mentioned difficulties, some approaches are pro-posed based on visual features derived directly from the content of the image: these are the content-based image retrieval (CBIR) approaches. They allow users to search the desired image by specifying image queries: a query can be an exam-ple, a sketch or visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring simi-larity between image features. An important property of these features is to be in-variant under various deformations that the observed image could undergo. In this chapter, we will present a number of existing methods for CBIR applica-tions. We will also describe some measures that are usually used for similarity measurement. At the end, and as an application example, we present a specific ap-proach, that we are developing, to illustrate the topic by providing experimental results

Modeshapes recognition using Fourier descriptors: a simple SHM example

The main objective of this study is to develop an alternative criterion for modeshape classification, as the currently available one, MAC (Modal Assurance Criteria), is only a vector correlation representing modeshape similarities. This new method is developed to provide a set of features (Fourier Descriptors) for comparing modeshapes with “local” similarities of higher interest than “global” similarities using nodal lines. These lines are able to characterize modeshapes very easily. So when damage occurs, we are able to track the few descriptors changes to localise the damage. We validated our method on a CFCF plate demonstrating the quality of the damage localisation and possible use in a “mode tracking” application (space structure)

Robotic assembly of complex planar parts: An experimental evaluation

In this paper we present an experimental evaluation of automatic robotic assembly of complex planar parts. The torque-controlled DLR light-weight robot, equipped with an on-board camera (eye-in-hand configuration), is committed with the task of looking for given parts on a table, picking them, and inserting them inside the corresponding holes on a movable plate. Visual servoing techniques are used for fine positioning over the selected part/hole, while insertion is based on active compliance control of the robot and robust assembly planning in order to align the parts automatically with the hole. Execution of the complete task is validated through extensive experiments, and performance of humans and robot are compared in terms of overall execution time

A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.894244Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters (PFs) have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter. The PF also models image statistics such as mean and variance of the given data which can be useful in obtaining proper separation of object and backgroun

Behavior of Normalized Moments under Distortion and Optimization

Various types of moments have been used to recognize planar shapes. The algorithms are mostly based upon extracting moment features and train the machine to match these features with a database of templates. The shape could be represented by a polygon whose vertices lie on the boundary. The computational complexity of algorithm is a function of number of vertices of polygon. In this paper, we first present one such algorithm for hand drawn shapes. Vertices are picked up randomly as the user draws the shape with the help of mouse on a monitor. We have used a database of four templates for training the machine. The robustness of the algorithm based upon the moment features has been exhibited by matching a test shape that is a distortion of one of template stored in the database. In the second part of paper we have proposed an optimization technique that discards most of the redundant vertices of the polygon representing the shapes, thus reducing significantly the complexity. Integral square error norm is used to calculate optimal vertices and nearest – neighbor (NN) classifier for classifying the shapes. Empirical results have been presented for extracting the moment feature vectors

2-D shapes description by using features based on the differential turning angle scalogram

International audienceA 2-D shape description using the turning angle is presented 1 . This descriptor is based on a scalogram obtained from a progressive filtering of a planar closed contour. At a given scale, the differential turning angle function is calculated from which, three essential points are derived: the minimum differential-turning angle (α-points), the maximum differential-turning angle (β-points) and the zero-crossing of the turning angle (γ-points). For a continuum of the scale values in the filtering process, a map (called d-TASS map) is generated. As shown experimentally in a previous study, this map is invariant under rotation, translation and scale change. Moreover, it is shearing and noise resistant. The contribution of the present study is firstly, to prove theoretically that d-TASS is rotation and scale change invariant and secondly to propose a new descriptor extracted from the blocks within the scalogram. When applied to shape retrieval from commonly used image databases like MPEG-7 Core Experiments Shape-1 dataset, Multiview Curve Dataset and marines animals of SQUID dataset, experimental results yield very encouraging efficiency and effectiveness of the new analysis approach and the proposed descriptor

Intrinsic Inference on the Mean Geodesic of Planar Shapes and Tree Discrimination by Leaf Growth

For planar landmark based shapes, taking into account the non-Euclidean geometry of the shape space, a statistical test for a common mean first geodesic principal component (GPC) is devised. It rests on one of two asymptotic scenarios, both of which are identical in a Euclidean geometry. For both scenarios, strong consistency and central limit theorems are established, along with an algorithm for the computation of a Ziezold mean geodesic. In application, this allows to verify the geodesic hypothesis for leaf growth of Canadian black poplars and to discriminate genetically different trees by observations of leaf shape growth over brief time intervals. With a test based on Procrustes tangent space coordinates, not involving the shape space's curvature, neither can be achieved.Comment: 28 pages, 4 figure

Shape description and matching using integral invariants on eccentricity transformed images

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately