Consistent Correspondences for Shape and Image Problems

Establish consistent correspondences between different objects is a classic problem in computer science/vision. It helps to match highly similar objects in both 3D and 2D domain. Inthe 3D domain, finding consistent correspondences has been studying for more than 20 yearsand it is still a hot topic. In 2D domain, consistent correspondences can also help in puzzlesolving. However, only a few works are focused on this approach. In this thesis, we focuson finding consistent correspondences and extend to develop robust matching techniques inboth 3D shape segments and 2D puzzle solving. In the 3D domain, segment-wise matching isan important research problem that supports higher-level understanding of shapes in geometryprocessing. Many existing segment-wise matching techniques assume perfect input segmentation and would suffer from imperfect or over-segmented input. To handle this shortcoming,we propose multi-layer graphs (MLGs) to represent possible arrangements of partially mergedsegments of input shapes. We then adapt the diffusion pruning technique on the MLGs to findconsistent segment-wise matching. To obtain high-quality matching, we develop our own voting step which is able to remove inconsistent results, for finding hierarchically consistent correspondences as final output. We evaluate our technique with both quantitative and qualitativeexperiments on both man-made and deformable shapes. Experimental results demonstrate theeffectiveness of our technique when compared to two state-of-art methods. In the 2D domain,solving jigsaw puzzles is also a classic problem in computer vision with various applications.Over the past decades, many useful approaches have been introduced. Most existing worksuse edge-wise similarity measures for assembling puzzles with square pieces of the same size, and recent work innovates to use the loop constraint to improve efficiency and accuracy. Weobserve that most existing techniques cannot be easily extended to puzzles with rectangularpieces of arbitrary sizes, and no existing loop constraints can be used to model such challenging scenarios. We propose new matching approaches based on sub-edges/corners, modelledusing the MatchLift or diffusion framework to solve square puzzles with cycle consistency.We demonstrate the robustness of our approaches by comparing our methods with state-of-artmethods. We also show how puzzles with rectangular pieces of arbitrary sizes, or puzzles withtriangular and square pieces can be solved by our techniques

Robust and flexible puzzle solving with corner-based cycle consistent correspondences

Solving jigsaw puzzles is a classic problem in computer vision with various applications. Over the past decades, many useful approaches have been introduced. Most existing works use edge-wise similarity measures for assembling puzzles with square pieces of the same size, and recent work innovates to use the loop constraint to improve efficiency and accuracy. We observe that most existing techniques cannot be easily extended to puzzles with rectangular pieces of arbitrary sizes, and no existing loop constraints can be used to model such challenging scenarios. In this paper, we propose a new corner-wise matching approach, modelled using the MatchLift framework to solve square puzzles with cycle consistency. We further show one exciting example illustrating how puzzles with rectangular pieces of arbitrary sizes would be solved by our techniqu

Computer aided puzzle assembly based on shape and texture information /

Puzzle assembly’s importance lies into application in many areas such as restoration and reconstruction of archeological findings, the repairing of broken objects, solving of the jigsaw type puzzles, molecular docking problem, etc. Puzzle pieces usually include not only geometrical shape information but also visual information of texture, color, continuity of lines, and so on. Moreover, textural information is mainly used to assembly pieces in some cases, such as classic jigsaw puzzles. This research presents a new approach in that pictorial assembly, in contrast to previous curve matching methods, uses texture information as well as geometric shape. The assembly in this study is performed using textural features and geometrical constraints. First, the texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The feature values are derived by these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. Two new algorithms using Fourier based image registration techniques are developed to optimize the affinity. The algorithms for inpainting, affinity and Fourier based assembly are explained with experimental results on real and artificial data. The main contributions of this research are: The development of a performance measure that indicates the level of success of assembly of pieces based on textural features and geometrical shape. Solution of the assembly problem by using of the Fourier based methods

algoritmos geneticos basados en criterios para rompecabezas np completos

El objetivo principal de esta investigacion es desarrollar un Sistema Inteligente Robotizado (SIR) resuelva un rompecabezas desconocido en un lapso de tiempo reducido. SIR aplica tecnicas de reconocimiento de patrones junto con Algoritmos Geneticos. Investigando el estado del arte, la naturaleza NP-Completa del problema aparece como el comun denominador. SIR se basa en esas experiencias para aportar una nueva aproximacion al problema. Este enfoque involucra la conversion del puzzle a un modelo de Teoria de Grafos. El estudio de este modelo sumado a la inclusion de una analogia y enfoque de solucion diferentes son los principales aportes de este trabajo. A su vez describe marcos teoricos y practicos y el estado actual del proyecto y el trabajo a futuro

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all