Efficient strategies for reducing the size of search space in three-dimensional object recognition and pose estimation

In this thesis we show how large efficiencies can be achieved in model based 3-D vision by combining the notions of discrete relaxation and bipartite matching. The computational approach we present is empirically interesting and capable of pruning large segments of search space--an indispensable step when the number of objects in the model library is large and when recognition of complex objects with a large number of surfaces is called for. We use bipartite matching for quick wholesale rejection of inapplicable models. We also use bipartite matching for implementing one of the key steps of discrete relaxation: the determination of compatibility of a scene surface with a potential model surface taking into account relational considerations. While we are able to provide the time complexity function associated with those aspects of the procedure that are implemented via bipartie matching, we are not able to do so for the interative elements of the discrete relaxation computations. In defense of our claim regarding computational efficiencies of the method presented here, all we can say is that our algorithms do not take more than a couple of iterations even for objects with more than 30 surfaces. Methods for range data collection and segmentation of such data are essential elements of our 3D robot vision system. In this dissertation, we will also discuss a new type of a structured-light scanner; we call it the Cross Scanning Structured Light Scanner because two crossed laser beams are used for scanning a scene. We will show how surface features used in our recognition scheme can be extracted directly from the data produced by this scanning system

Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification

Sweat pores on the human fingertip have meaningful patterns that enable individual identification. Although conventional automatic fingerprint identification systems (AFIS) have mainly employed the minutiae features to match fingerprints, there has been minimal research that uses sweat pores to match fingerprints. Recently, high-resolution optical sensors and pore-based fingerprint systems have become available, which motivates research on pore analysis. However, most existing pore-based AFIS methods use the minutia-ridge information and image pixel distribution, which limit their applications. In this context, this paper presents a stable pore matching algorithm which effectively removes both the minutia-ridge and fingerprint-device dependencies. Experimental results show that the proposed pore matching algorithm is more accurate for general fingerprint images and robust under noisy conditions compared with existing methods. The proposed method can be used to improve the performance of AFIS combined with the conventional minutiae-based methods. Since sweat pores can also be observed using various systems, removing of the fingerprint-device dependency will make the pore-based AFIS useful for wide applications including forensic science, which matches the latent fingerprint to the fingerprint image in databases

www.elsevier.com/locate/patcog A novel approach to the fast computation of Zernike moments

This paper presents a novel approach to the fast computation of Zernike moments from a digital image. Most existing fast methods for computing Zernike moments have focused on the reduction of the computational complexity of the Zernike 1-D radial polynomials by introducing their recurrence relations. Instead, in our proposed method, we focus on the reduction of the complexity of the computation of the 2-D Zernike basis functions. As Zernike basis functions have specific symmetry or anti-symmetry about the x-axis, the y-axis, the origin, and the straight line y = x, we can generate the Zernike basis functions by only computing one of their octants. As a result, the proposed method makes the computation time eight times faster than existing methods. The proposed method is applicable to the computation of an individual Zernike moment as well as a set of Zernike moments. In addition, when computing a series of Zernike moments, the proposed method can be used with one of the existing fast methods for computing Zernike radial polynomials. This paper also presents an accurate form of Zernike moments for a discrete image function. In the experiments, results show the accuracy of the form for computing discrete Zernike moments and confirm that the proposed method for the fast computation of Zernike moments is much more efficient than existing fast methods in most cases

Continuous Separation of Circulating Tumor Cells from Whole Blood Using a Slanted Weir Microfluidic Device

The separation of circulating tumor cells (CTCs) from the peripheral blood is an important issue that has been highlighted because of their high clinical potential. However, techniques that depend solely on tumor-specific surface molecules or just the larger size of CTCs are limited by tumor heterogeneity. Here, we present a slanted weir microfluidic device that utilizes the size and deformability of CTCs to separate them from the unprocessed whole blood. By testing its ability using a highly invasive breast cancer cell line, our device achieved a 97% separation efficiency, while showing an 8-log depletion of erythrocytes and 5.6-log depletion of leukocytes. We also developed an image analysis tool that was able to characterize the various morphologies and differing deformability of the separating cells. From the results, we believe our system possesses a high potential for liquid biopsy, aiding future cancer research