Affine Invariant Contour Descriptors Using Independent Component Analysis and Dyadic Wavelet Transform

The paper presents a novel technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The proposed technique first normalizes an input image by removing the affine deformations using independent component analysis which also reduces the noise introduced during contour parameterization. Then four invariant functionals are constructed using the restored object contour, dyadic wavelet transform and conics in the context of wavelets. Experimental results are conducted using three different standard datasets to confirm the validity of the proposed technique. Beside this the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet based invariants. Also the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors

Partial surface matching by using directed footprints

AbstractIn this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem, we are given two objects in 3-space, each represented as a set of points, scattered uniformly along its boundary or inside its volume. The goal is to find a rigid motion of one object which makes a sufficiently large portion of its boundary lying sufficiently close to a corresponding portion of the boundary of the second object. This is an important problem in pattern recognition and in computer vision, with many industrial, medical, and chemical applications. Our algorithm is based on assigning a directed footprint to every point of the two sets, and locating all the pairs of points (one of each set) whose undirected components of the footprints are sufficiently similar. The algorithm then computes for each such pair of points all the rigid transformations that map the first point to the second, while making the respective direction components of their footprints coincide. A voting scheme is employed for computing transformations which map significantly large number of points of the first set to points of the second set. Experimental results on various examples are presented and show the accurate and robust performance of our algorithm

Application of geometric hashing techniques to retrieval of high dimensional objects in scientific databases

An approach to designing very fast algorithms for tackling the problem of approximate object matching in very large databases of high-dimensional objects is proposed. Given are a target object C and a database D containing information about a set of high-dimensional objects each of which is represented as a set of points. Our algorithms have an off-line object preprocessing (shape representation) phase and a recognition phase. The described algorithms determine those objects from D which are the closest to object C, according to delete or insert some points, move and rotation. All of these can be achieved very efficiently with the help of geometric hashing techniques. This scheme has been successfully applied to a real scientific database

Local Geometric Consensus: A General Purpose Point Pattern-Based Tracking Algorithm

Proceedings of ACM ISMAR 2015, Fukuoka, JapanInternational audienceWe present a method which can quickly and robustly match 2D and 3D point patterns based on their sole spatial distribution , but it can also handle other cues if available. This method can be easily adapted to many transformations such as similarity transformations in 2D/3D, and affine and perspective transformations in 2D. It is based on local geometric consensus among several local matchings and a refinement scheme. We provide two implementations of this general scheme, one for the 2D homography case (which can be used for marker or image tracking) and one for the 3D similarity case. We demonstrate the robustness and speed performance of our proposal on both synthetic and real images and show that our method can be used to augment any (textured/textureless) planar objects but also 3D objects

MATCHING REAL AND SYNTHETIC PANORAMIC IMAGES USING A VARIANT OF GEOMETRIC HASHING

This work demonstrates an approach to automatically initialize a visual model-based tracker, and recover from lost tracking, without prior camera pose information. These approaches are commonly referred to as tracking-by-detection. Previous tracking-by-detection techniques used either fiducials (i.e. landmarks or markers) or the object’s texture. The main contribution of this work is the development of a tracking-by-detection algorithm that is based solely on natural geometric features. A variant of geometric hashing, a model-to-image registration algorithm, is proposed that searches for a matching panoramic image from a database of synthetic panoramic images captured in a 3D virtual environment. The approach identifies corresponding features between the matched panoramic images. The corresponding features are to be used in a photogrammetric space resection to estimate the camera pose. The experiments apply this algorithm to initialize a model-based tracker in an indoor environment using the 3D CAD model of the building