Object recognition using three-dimensional optical quasi-correlation

SYMMETRY AXIS BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING

This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

Review of three-dimensional holographic imaging by multiple-viewpoint-projection based methods

Methods of generating multiple viewpoint projection holograms of three-dimensional (3-D) realistic objects illuminated by incoherent white light are reviewed in this paper. Using these methods, it is possible to obtain holograms with a simple digital camera, operating in regular light conditions. Thus, most disadvantages characterizing conventional digital holography, namely the need for a powerful, highly coherent laser and extreme stability of the optical system, are avoided. The proposed holographic processes are composed of two stages. In the first stage, regular intensity-based images of the 3-D scene are captured from multiple points of view by a simple digital camera. In the second stage, the acquired projections are digitally processed to yield the complex digital hologram of the 3-D scene, where no interference is involved in the process. For highly reflecting 3-D objects, the resulting hologram is equivalent to an optical hologram of the objects recorded from the central point of view. We first review various methods to acquire the multiple viewpoint projections. These include the use of a microlens array and a macrolens array, as well as digitally generated projections that are not acquired optically. Next, we show how to digitally process the acquired projections to Fourier, Fresnel, and image holograms. Additionally, to obtain certain advantages over the known types of holograms, the proposed hybrid optical-digital process can yield novel types of holograms such as the modified Fresnel hologram and the protected correlation hologram. The prospective goal of these methods is to facilitate the design of a simple and portable digital holographic camera that can be useful for a variety of practical applications, including 3-D video acquisition and various types of biomedical imaging. We review several of these applications to signify the advantages of multiple viewpoint projection holography.

Three-dimensional object recognition using a quasi-correlator invariant to imaging distances

We present a new method for performing electro-optical three-dimensional (3-D) object recognition under incoherent white-light illumination. Perspective projections of the 3-D scene are acquired from multiple points of view and then processed into a single complex two-dimensional modified Fresnel hologram of the scene. This hologram is processed with a single filter which is matched to a single object, so that all identical objects in the scene yield similar correlation peaks in the 3-D space with almost no dependency on the distances of the objects from the acquisition plane. The new method is demonstrated by experiments.

Three-dimensional object detection under arbitrary lighting conditions

A novel method of 3D object recognition independent of lighting conditions is presented. The recognition model is based on a vector space representation using an orthonormal basis generated by the Lambertian reflectance functions obtained with distant light sources. Changing the lighting conditions corresponds to multiplying the elementary images by a constant factor and because of that, all possible lighting views will be elements that belong to that vector space. The recognition method proposed is based on the calculation of the angle between the vector associated with a certain illuminated 3D object and that subspace. We define the angle in terms of linear correlations to get shift and illumination-invariant detection.

Computer-generated holograms of three-dimensional objects synthesized from their multiple angular viewpoints

Synthesizing computer-generated holograms (CGHs) of a general three-dimensional (3D) object is usually a heavy computational task. We propose and demonstrate a new algorithm for computing CGHs of 3D objects. In our scheme, many different angular projections of computer-designed 3D objects are numerically processed to yield a single two-dimensional complex matrix. This matrix is equivalent to the complex amplitude of a wave front on the rear focal plane of a spherical lens when the object is located near the front focal point and illuminated by a plane wave. Therefore the computed matrix can be used as a CGH after it is encoded to a real positive-valued transparency. When such CGH is illuminated by a plane wave, a 3D real image of the objects is constructed. The number of computer operations are equivalent to those of a two-dimensional Fourier CGH. Computer and optical constructions of 3D objects, both of which show the feasibility of the proposed approach, are described.

Scale-invariant recognition of three-dimensional objects by use of a quasi-correlator

A method of scale-invariant recognition of three-dimensional (3-D) objects is presented. Several images of the observed scene are recorded under white-light illumination from several different points of view and compressed into a single complex two-dimensional matrix. After filtering with a single scale-invariant filter, the resultant function is then coded into a computer-generated hologram (CGH). When this CGH is coherently illuminated, a correlation space is reconstructed in which light peaks indicate the existence and location of true targets in the tested 3-D scene. The light peaks are detectable for different sizes of the true objects, as long as they are within the invariance range of the filter. Experimental results in a complete electro-optical system are presented, and comparisons with other systems are investigated by use of computer simulation.