Partial shape matching using transformation parameter similarity

In this paper, we present a method for non-rigid, partial shape matching in vector graphics. Given a user-specified query region in a 2D shape, similar regions are found, even if they are non-linearly distorted. Furthermore, a non-linear mapping is established between the query regions and these matches, which allows the automatic transfer of editing operations such as texturing. This is achieved by a two-step approach. First, pointwise correspondences between the query region and the whole shape are established. The transformation parameters of these correspondences are registered in an appropriate transformation space. For transformations between similar regions, these parameters form surfaces in transformation space, which are extracted in the second step of our method. The extracted regions may be related to the query region by a non-rigid transform, enabling non-rigid shape matching. In this paper, we present a method for non-rigid, partial shape matching in vector graphics. Given a user-specified query region in a 2D shape, similar regions are found, even if they are non-linearly distorted. Furthermore, a non-linear mapping is established between the query regions and these matches, which allows the automatic transfer of editing operations such as texturing. This is achieved by a two-step approach. First, pointwise correspondences between the query region and the whole shape are established. The transformation parameters of these correspondences are registered in an appropriate transformation space. For transformations between similar regions, these parameters form surfaces in transformation space, which are extracted in the second step of our method. The extracted regions may be related to the query region by a non-rigid transform, enabling non-rigid shape matching

A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%

Robust Estimation of Nonrigid Transformation for Point Set Registration

We present a new point matching algorithm for robust nonrigid registration. The method iteratively recovers the point correspondence and estimates the transformation be-tween two point sets. In the first step of the iteration, fea-ture descriptors such as shape context are used to establish rough correspondence. In the second step, we estimate the transformation using a robust estimator called L2E. This is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspon-dence step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. We apply our method to nonrigid sparse image feature cor-respondence on 2D images and 3D surfaces. Our results quantitatively show that our approach outperforms state-of-the-art methods, particularly when there are a large num-ber of outliers. Moreover, our method of robustly estimating transformations from correspondences is general and has many other applications. 1

Registration of prostate surfaces for image-guided robotic surgery via the da Vinci System

Organ-confined prostate cancer represents a commonly diagnosed cancer among men rendering an early diagnosis and screening a necessity. The prostate laparoscopic surgery using the da Vinci system is a minimally invasive, computer assisted and image-guided surgery application that provides surgeons with (i) navigational assistance by displaying targeting lesions of the intraoperative prostate anatomy onto aligned preoperative high-field magnetic resonance imaging (MRI) scans of the pelvis; and (ii) an effective clinical management of intra-abdominal cancers in real time. Such an image guidance system can improve both functional and oncological outcomes as well as augment the learning curve of the process increasing simultaneously the eligibility of patients for surgical resection. By segmenting MRI scans into 3D models of intraprostatic anatomy preoperatively, and overlaying them onto 3D stereoendoscopic images acquired intraoperatively using the da Vinci surgical system, a graphical representation of intraoperative anatomy can be provided for surgical navigation. The preoperative MRI surfaces are full 3D models and the stereoendoscopic images represent partial 3D views of the prostate due to occlusion. Hence achieving an accurate non-rigid image registration of full prostate surfaces onto occluded ones in real time becomes of critical importance, especially for use intraoperatively with the stereoendoscopic and MRI imaging modalities. This work exploits the registration accuracy that can be achieved from the application of selected state-of-the-art non-rigid registration algorithms and in doing so identifies the most accurate technique(s) for registration of full prostate surfaces onto occluded ones; a series of rigorous computational registration experiments is performed on synthetic target prostate data, which are aligned manually onto the MRI prostate models before registration is initiated. This effort extends to using real target prostate data leading to visually acceptable non-rigid registration results. A great deal of emphasis is placed on examining the capacity of the selected non-rigid algorithms to recover the deformation of the intraoperative prostate surfaces; the deformation of prostate can become pronounced during the surgical intervention due to surgical-induced anatomical deformities and pathological or other factors. The warping accuracy of the non-rigid registration algorithms is measured within the space of common overlap (established between the full MRI model and the target scene) and beyond. From the results of the registrations to occluded and deformed prostate surfaces (in the space beyond common overlap) it is concluded that the modified versions of the Kernel Correlation/Thin-plane Spline (KC/TPS) and Gaussian Mixture Model/Thin-plane Spline (GMM/TPS) methodologies can provide the clinical accuracy required for image-guided prostate surgery procedures (performed by the da Vinci system) as long as the size of the target scene is greater than ca. 30% of the full MRI surface. For the modified KC/TPS and GMM/TPS non-rigid registration techniques to be clinically acceptable when the measurement noise is also included in the simulations: (i) the size of the target model must be greater than ca. 38% of the full MRI surface; (ii) the standard deviation σ of the contributing Gaussian noise must be less than 0.345 for μ=0; and (iii) the observed deformation must not be characterized by excessively increased complexity. Otherwise the contribution of Gaussian noise must be explicitly parameterized in the objective cost functions of these non-rigid algorithms. The Expectation Maximization/Thin-plane Spline (EM/TPS) non-rigid registration algorithm cannot recover the prostate surface deformation accurately in full-model-to-occluded-model registrations due to the way that the correspondences are estimated. However, EM/TPS is more accurate than KC+TPS and GMM+TPS in recovering the deformation of the prostate surface in full-model-to-full-model registrations