Local Color Voxel and Spatial Pattern for 3D Textured Recognition

3D textured retrieval including shape, color dan pattern is still a challenging research. Some approaches are proposed, but voxel-based approach has not much been made yet, where by using this approach, it still keeps both geometry and texture information. It also maps all 3D models into the same dimension. Based on this fact, a novel voxel pattern based is proposed by considering local pattern on a voxel called local color voxel pattern (LCVP). Voxels textured is observed by considering voxel to its neighbors. LCVP is computed around each voxel to its neighbors. LCVP value will indicate uniq pattern on each 3D models. LCVP also quantizes color on each voxel to generate a specific pattern. Shift and reflection circular also will be done. In an additional way, inspired by promising recent results from image processing, this paper also implement spatial pattern which utilizing Weber, Oriented Gradient to extract global spatial descriptor. Finally, a combination of local spectra and spatial and established global features approach called multi Fourier descriptor are proposed. For optimal retrieval, the rank combination is performed between local and global approaches. Experiments were performed by using dataset SHREC'13 and SHREC'14 and showed that the proposed method could outperform some performances to state-of-the-art

Towards an automatic semantic annotation of car aesthetics

The design of a new car is guided by a set of directives indicating the target market and specific engineering and aesthetic constraints, which may include also the preservation of the company brand identity or the restyling of products already on the market. When creating a new product designers are used to evaluating other existing products to take inspiration or to possibly reuse successful solutions. In the perspective of an optimised styling workflow a great benefit could come from the opportunity of easily retrieving the related documentation and existing digital models both from internal and external repositories. In fact, the rapid growth of the web contents and the widely spread adoption of computerassisted design tools have made a huge amount of digital data available, whose exploitation could be improved by more selective retrieval methods. In particular, the retrieval of aesthetic elements may help designers to more efficiently create digital models conforming to specific styling properties. The aim of the research described in this document is the definition of a framework able to support a (semi-) automatic extraction of semantic data from 3D models and other multimedia data to allow car designers to reuse knowledge and design solutions within the styling department. The first objective is then capturing and structuring both the explicit and implicit elements that contribute to the car aesthetics and can be realistically tackled through computational models and methods. The second step is the definition of a system architecture able to transfer such semantics through an automatic annotation of car models

3D Object Comparison Based on Shape Descriptors

No abstract availabl

АНАЛИЗ ФОРМЫ ТРЕХМЕРНЫХ ОРИЕНТАЦИОННЫХ ГИСТОГРАММ ТОМОГРАФИЧЕСКИХ ИЗОБРАЖЕНИЙ СТРУКТУР ГОЛОВНОГО МОЗГА

Рассматриваются различные методы описания, сравнительного анализа и классификации формы трехмерных ориентационных гистограмм, используемых для характеризации анизотропных свойств томографических изображений белого вещества головного мозга. Ориентационные гистограммы представляют собой подкласс трехмерных объектов, характеризующийся регулярным и заранее фиксированным разбиением их поверхностей

Design descriptions in the development of machine learning based design tools

Applications of machine learning technologies are becoming ubiquitous in many sectors and their impacts, both positive and negative, are widely reported. As a result, there is substantial interest from the engineering community to integrate machine learning technologies into design workflows with a view to improving the performance of the product development process. In essence, machine learning technologies are thought to have the potential to underpin future generations of data-enabled engineering design system that will deliver radical improvements to product development and so organisational performance. In this paper we report learning from experiments where we applied machine learning to two shape-based design challenges: in a given collection of designed shapes, clustering (i) visually similar shapes and (ii) shapes that are likely to be manufactured using the same primary process. Both challenges were identified with our industry partners and are embodied in a design case study. We report early results and conclude with issues for design descriptions that need to be addressed if the full potential of machine learning is to be realised in engineering design

A K-MEANS CLUSTERING BASED SHAPE RETRIEVAL TECHNIQUE FOR 3D MESH MODELS

Due to the large size of shape databases, importance of effective and robust method in shape retrieval has been increased. Researchers mainly focus on finding descriptors which is suitable for rigid models. Retrieval of non-rigid models is a still challenging field which needs to be studied more. For non-rigid models, descriptors that are designed should be insensitive to different poses. For non-rigid model retrieval, we propose a new method which first divides a model into clusters using geodesic distance metric and then computes the descriptor using these clusters. Mesh segmentation is performed using a skeleton-based K-means clustering method.  Each cluster is represented by an area based descriptor which is invariant to scale and orientation. Finally, similar objects for the input model are retrieved. Articulated objects from human to animals are used for this study’s experiments for the validation of the proposed retrieval algorithm