Geometric modeling of non-rigid 3D shapes : theory and application to object recognition.

One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This is true, especially for non-rigid 3D shapes where a great variety of shapes are produced as a result of deformations of a non-rigid object. Modeling these non-rigid shapes is a very challenging problem. Being able to analyze the properties of such shapes and describe their behavior is the key issue in research. Also, considering photometric features can play an important role in many shape analysis applications, such as shape matching and correspondence because it contains rich information about the visual appearance of real objects. This new information (contained in photometric features) and its important applications add another, new dimension to the problem\u27s difficulty. Two main approaches have been adopted in the literature for shape modeling for the matching and retrieval problem, local and global approaches. Local matching is performed between sparse points or regions of the shape, while the global shape approaches similarity is measured among entire models. These methods have an underlying assumption that shapes are rigidly transformed. And Most descriptors proposed so far are confined to shape, that is, they analyze only geometric and/or topological properties of 3D models. A shape descriptor or model should be isometry invariant, scale invariant, be able to capture the fine details of the shape, computationally efficient, and have many other good properties. A shape descriptor or model is needed. This shape descriptor should be: able to deal with the non-rigid shape deformation, able to handle the scale variation problem with less sensitivity to noise, able to match shapes related to the same class even if these shapes have missing parts, and able to encode both the photometric, and geometric information in one descriptor. This dissertation will address the problem of 3D non-rigid shape representation and textured 3D non-rigid shapes based on local features. Two approaches will be proposed for non-rigid shape matching and retrieval based on Heat Kernel (HK), and Scale-Invariant Heat Kernel (SI-HK) and one approach for modeling textured 3D non-rigid shapes based on scale-invariant Weighted Heat Kernel Signature (WHKS). For the first approach, the Laplace-Beltrami eigenfunctions is used to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the Collaborative Representation-based Classification with a Regularized Least Square (CRC-RLS) algorithm. The experimental results have shown that the proposed descriptor can achieve state-of-the-art results on two benchmark data sets. For the second approach, an improved method to introduce scale-invariance has been also proposed to avoid noise-sensitive operations in the original transformation method. Then a new 3D shape descriptor is formed based on the histograms of the scale-invariant HK for a number of critical points on the shape at different time scales. A Collaborative Classification (CC) scheme is then employed for object classification. The experimental results have shown that the proposed descriptor can achieve high performance on the two benchmark data sets. An important observation from the experiments is that the proposed approach is more able to handle data under several distortion scenarios (noise, shot-noise, scale, and under missing parts) than the well-known approaches. For modeling textured 3D non-rigid shapes, this dissertation introduces, for the first time, a mathematical framework for the diffusion geometry on textured shapes. This dissertation presents an approach for shape matching and retrieval based on a weighted heat kernel signature. It shows how to include photometric information as a weight over the shape manifold, and it also propose a novel formulation for heat diffusion over weighted manifolds. Then this dissertation presents a new discretization method for the weighted heat kernel induced by the linear FEM weights. Finally, the weighted heat kernel signature is used as a shape descriptor. The proposed descriptor encodes both the photometric, and geometric information based on the solution of one equation. Finally, this dissertation proposes an approach for 3D face recognition based on the front contours of heat propagation over the face surface. The front contours are extracted automatically as heat is propagating starting from a detected set of landmarks. The propagation contours are used to successfully discriminate the various faces. The proposed approach is evaluated on the largest publicly available database of 3D facial images and successfully compared to the state-of-the-art approaches in the literature. This work can be extended to the problem of dense correspondence between non-rigid shapes. The proposed approaches with the properties of the Laplace-Beltrami eigenfunction can be utilized for 3D mesh segmentation. Another possible application of the proposed approach is the view point selection for 3D objects by selecting the most informative views that collectively provide the most descriptive presentation of the surface

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage

Patch-based 3D reconstruction of deforming objects from monocular grey-scale videos

Abstract. The ability to reconstruct the spatio-temporal depth map of a non-rigid object surface deforming over time has many applications in many different domains. However, it is a challenging problem in Computer Vision. The reconstruction is ambiguous and not unique as many structures can have the same projection in the camera sensor. Given the recent advances and success of Deep Learning, it seems promising to use and train a Deep Convolutional Neural Network to recover the spatio-temporal depth map of deforming objects. However, training such networks requires a large-scale dataset. This problem can be tackled by artificially generating a dataset and using it in training the network. In this thesis, a network architecture is proposed to estimate the spatio-temporal structure of the deforming object from small local patches of a video sequence. An algorithm is presented to combine the spatio-temporal structure of these small patches into a global reconstruction of the scene. We artificially generated a database and used it to train the network. The performance of our proposed solution was tested on both synthetic and real Kinect data. Our method outperformed other conventional non-rigid structure-from-motion methods

Optimization of Cash Flow and Financing Costs in Construction Projects

The contractor’s cash shortage during the progress of a construction project leads to delays, penalties and may lead to project failure. Since the net difference between the cash inflow and cash outflow during construction shall be financed by the contractor, the contractor must consider methods to improve their cash flow in order to maximize the profit margin and minimize the financing costs. Several studies have covered optimization of cash flow and optimization of financing costs, separately. This model integrates both approaches in an attempt to determine the best project schedule and financing alternative; that cover the cash shortage with maximum profitability. The model proposes different ways that attempt to overcome the deficit in cash flow; first by minimizing the amount of financing required through shifting the activities with lag to enhance the cash flow, without extending the project duration, then evaluating different financing alternatives; namely long and short-term loans. The outcome of the model is a modified cash flow for the project with less financing required from the contractor, and feasible schedules of financing inflow and outflow based on the best financing alternative, that attempts to cover the lack of cash with the minimum financing cost. In addition, the model provides the user with a negotiable bid alternative that determines the optimum increase in advance payment, that shall overcome the cash shortage, without borrowing funds. The model has been tested and validated on a case study, and a sensitivity analysis has been performed

Traveling solitary wave solutions for the symmetric regularized long-wave equation

In this paper, we employ the extended tanh function method to nd the exact traveling wave solutions involving parameters of the symmetric regularized long- wave equation. When these parameters are taken to be special values, the solitary wave solutions are derived from the exact traveling wave solutions. These studies reveal that the symmetric regularized long-wave equation has a rich varietyof solutions

Traveling wave solutions for the Couple Boiti-Leon-Pempinelli System by using extended Jacobian elliptic function expansion method

In this work, an extended Jacobian elliptic function expansion method is pro-posed for constructing the exact solutions of nonlinear evolution equations. The validity and reliability of the method are tested by its applications to the Couple Boiti-Leon-Pempinelli System which plays an important role in mathematical physics

Pore Network Connectivity and Its Impacts on Electrical Resistivity of Anisotropic Rocks with Complex Pore Structure

Core data and well-log interpretation results are usually comparable in homogenous conventional reservoirs. However, in the case of thinly-bedded, heterogeneous formations consisting of organic-rich mudrocks and carbonates, core-log calibration and integration are challenging. The calibration of well-log interpretation results with core data is hence justified for thick homogeneous beds. Consequently, petrophysical properties (e.g., fluid saturation) estimated from well logs are not generally in agreement with core measurements. Therefore, upscaling of petrophysical properties from core-scale to log-scale is essential to reconcile measurements obtained from different scales. Although petrophysical measurements vary from core-scale to log-scale, previous publications have shown that the relationship between formation factor and porosity is consistent over a wide-scale range in homogenous sandstones. These correlations, however, do not persist in rocks with complex pore structure and rock fabric (e.g., carbonates). This research investigated the persistence of a correlation between the electrical resistivity and the directional connectivity tensor at different scales within the micron scale in sandstone and carbonate examples. To fulfill this objective, three-dimensional (3D), pore-scale rock images were obtained from micro-CT (Computed Tomography) images. Then, each 3D pore-scale image was divided into subsamples of varying sizes. Afterwards, tortuosity of the networks of the electrically conductive rock components (e.g., formation water) was estimated in each subsample. The next step was to numerically solve the Laplace’s equation to estimate electric field distribution and effective electrical resistivity of each subsample. The last step involved calculating the directional connectivity tensor based on the estimated tortuosity and volumetric concentration of each conductive component in the samples and subsamples. Finally, the impact of directional connectivity of pore network on electrical resistivity was quantified. The results confirmed the existence of a correlation between directional connectivity and electrical resistivity at different micron scales in the samples studied in this thesis. Improvements of up to 59% and 54% were observed in the proposed relationship compared to the conventional relationship between porosity and electrical resistivity in fully and partially water-saturated samples, respectively. An improvement of up to 50% in estimates of water saturation was observed when the directional connectivity of pore network was taken into account

Synthesis and Biological Evaluation of Some New 1,2,3-Triazole Derivatives As Anti-microbial Agents

A series of 1,2,3-triazole derivatives bearing different chemical entities were prepared starting from 2-(4-phenyl-1H-1,2,3-triazol-1-yl)acetohydrazide, compound 2. The purity of all new compounds was checked by TLC and elucidation of their structures was confirmed by IR, 1H and 13C NMR along with High Resolution Mass Spectrometry (HRMS). All the target compounds were evaluated for their possible antimicrobial activity. Most of the tested compounds showed moderate to good antibacterial activity against most of the bacterial strains used in comparison with ciprofloxacin as a reference drug. The most active compounds were 4a, 9a, 9b, and 9f. Results of antifungal activity revealed that most of the tested compounds showed a good antifungal activity in comparison to fluconazole as a reference drug. Compounds 4a, 9c, 9d and 9f were the most active ones

Development of indole sulfonamides as cannabinoid receptor negative allosteric modulators

This Letter was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) and the Scottish Universities Life Sciences Alliance (SULSA) in 2011Peer reviewedPostprin