Feature extraction for range image interpretation using local topology statistics

This thesis presents an approach for interpreting range images of known subject matter, such as the human face, based on the extraction and matching of local features from the images. In recent years, approaches to interpret two-dimensional (2D) images based on local feature extraction have advanced greatly, for example, systems such as Scale Invariant Feature Transform (SIFT) can detect and describe the local features in the 2D images effectively. With the aid of rapidly advancing three-dimensional (3D) imaging technology, in particular, the advent of commercially available surface scanning systems based on photogrammetry, image representation has been able to extend into the third dimension. Moreover, range images confer a number of advantages over conventional 2D images, for instance, the properties of being invariant to lighting, pose and viewpoint changes. As a result, an attempt has been made in this work to establish how best to represent the local range surface with a feature descriptor, thereby developing a matching system that takes advantages of the third dimension present in the range images and casting this in the framework of an existing scale and rotational invariance recognition technology: SIFT. By exploring the statistical representations of the local variation, it is possible to represent and match range images of human faces. This can be achieved by extracting unique mathematical keys known as feature descriptors, from the various automatically generated stable keypoint locations of the range images, thereby capturing the local information of the distributions of the mixes of surface types and their orientations simultaneously. Keypoints are generated through scale-space approach, where the (x,y) location and the appropriate scale (sigma) are detected. In order to achieve invariance to in-plane viewpoint rotational changes, a consistent canonical orientation is assigned to each keypoint and the sampling patch is rotated to this canonical orientation. The mixes of surface types, derived using the shape index, and the image gradient orientations are extracted from each sampling patch by placing nine overlapping Gaussian sub-regions over the measurement aperture. Each of the nine regions is overlapped by one standard deviation in order to minimise the occurrence of spatial aliasing during the sampling stages and to provide a better continuity within the descriptor. Moreover, surface normals can be computed from each of the keypoint location, allowing the local 3D pose to be estimated and corrected within the feature descriptors since the orientations in which the images were captured are unknown a priori. As a result, the formulated feature descriptors have strong discriminative power and are stable to rotational changes

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

A novel approach to nose-tip and eye corners detection using H-K Curvature Analysis in case of 3D images

In this paper we present a novel method that combines a HK curvature-based approach for three-dimensional (3D) face detection in different poses (X-axis, Y-axis and Z-axis). Salient face features, such as the eyes and nose, are detected through an analysis of the curvature of the entire facial surface. All the experiments have been performed on the FRAV3D Database. After applying the proposed algorithm to the 3D facial surface we have obtained considerably good results i.e. on 752 3D face images our method detected the eye corners for 543 face images, thus giving a 72.20% of eye corners detection and 743 face images for nose-tip detection thus giving a 98.80% of good nose tip localizationComment: 5 page

Discrete spherical means of directional derivatives and Veronese maps

We describe and study geometric properties of discrete circular and spherical means of directional derivatives of functions, as well as discrete approximations of higher order differential operators. For an arbitrary dimension we present a general construction for obtaining discrete spherical means of directional derivatives. The construction is based on using the Minkowski's existence theorem and Veronese maps. Approximating the directional derivatives by appropriate finite differences allows one to obtain finite difference operators with good rotation invariance properties. In particular, we use discrete circular and spherical means to derive discrete approximations of various linear and nonlinear first- and second-order differential operators, including discrete Laplacians. A practical potential of our approach is demonstrated by considering applications to nonlinear filtering of digital images and surface curvature estimation

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Framework for 4D medical data compression

U ovom radu predložen je novi programski okvir za kompresiju četvero-dimenzionalnih (4D) medicinskih podataka. Arhitektura ovog programskog okvira temelji se na različitim procedurama i algoritmima koji detektiraju vremenske i prostorne zalihosti u ulaznim 4D medicinskim podacima. Pokret kroz vrijeme analizira se pomoću vektora pomaka koji predstavljaju ulazne parametre za neuronske mreže koje se koriste za procjenu pokreta. Kombinacijom segmentacije, pronalaženja odgovarajućih blokova i predikcijom vektora pomaka, zajedno s ekspertnim znanjem moguće je optimirati performanse sustava. Frekvencijska svojstva se analiziraju proširenjem wavelet transformacije na tri dimenzije. Za mirne volumetrijske objekte, moguće je konstruirati različite wavelet pakete s različitim filtrima koji omogućavaju širok raspon analiza frekvencijskih zalihosti. Kombinacijom uklanjanja vremenskih i prostornih zalihosti moguće je postići vrlo visoke omjere kompresije.This work presents a novel framework for four-dimensional (4D) medical data compression architecture. This framework is based on different procedures and algorithms that detect time and spatial (frequency) redundancy in recorded 4D medical data. Motion in time is analyzed through the motion fields that produce input parameters for the neural network used for motion estimation. Combination of segmentation, block matching and motion field prediction along with expert knowledge are incorporated to achieve better performance. Frequency analysis is done through an extension of one dimensional wavelet transformation to three dimensions. For still volume objects different wavelet packets with different filter banks can be constructed, providing a wide range of frequency analysis. With combination of removing temporal and spatial redundancies, very high compression ratio is achieved