Automating the process of postmortem identification of individuals using dental records is receiving an increased attention in forensic science, especially with the large volume of victims encountered in mass disasters. Dental radiograph alignment is a key step required for automating the dental identification process. In this paper, we address the problem of dental radiograph alignment using a Multi-Resolution Genetic Algorithm (MR-GA) approach. We use location and orientation information of edge points as features; we assume that affine transformations suffice to restore geometric discrepancies between two images of a tooth, we efficiently search the 6D space of affine parameters using GA progressively across multi-resolution image versions, and we use a <i>Hausdorff</i> distance measure to compute the similarity between a reference tooth and a query tooth subject to a possible alignment transform. Testing results based on 52 teeth-pair images suggest that our algorithm converges to reasonable solutions in more than 85% of the test cases, with most of the error in the remaining cases due to excessive misalignments.
Color and texture has been extensively studied in the field of image processing and computer vision. Industrial applications based on computer vision that uses color and texture information for produce recognition and surface change detection are more common these days. Even though color and texture have been individually studied and used for retrieval and classification purposes, very little work has been done in the problem of effective integration of color and texture information. Previously, we proposed the Collective Color Texture (CCT) model that functionally considers both the color and texture outcomes and generates an effective descriptor for the color texture. We showed that the CCT model outperformed other common integration methods when used for supervised classification. In this work, we use the CCT model for texture retrieval using histogram based color representation, various texture based representations. We used Outex 13 database for our experiments since it has wide variety of color textures (such as granite, canvas, carpet, etc) that are commonly present in industrial applications. We compare retrieval performance using individual methods with those from commonly used integrated techniques. Our results show that the CCT model provides an overall superior retrieval performance when compared with other popular approaches.
We address the problem of efficiency in image texture analysis. Motivated by the statistical occupancy model, we introduce the notion of patch re-occurrences. Using the re-occurrences, we propose the use of approximate textural features in image analysis. We describe how the proposed approximate features can be extracted for Gabor filters, a popular texture analysis method. Preliminary results on image texture classification show that the proposed method can provide an improved efficiency in the processing, without introducing any significant degradation in the classification results.
Conference Committee Involvement (2)
Intelligent Systems in Design and Manufacturing VI
24 October 2005 | Boston, MA, United States
Intelligent Systems in Design and Manufacturing V
25 October 2004 | Philadelphia, Pennsylvania, United States