The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack

Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map which has been an open issue for decades. To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy and occlusion handling, the optimization function of the proposed method can, in fact, converge faster and better than state of the art methods. The evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against 5 other methods. Preliminary results indicate that the proposed method has a better performance in terms of structural accuracy and optimization in comparison to the current state of the art methods.Comment: 15 pages, 8 figure

Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common network layers, and can be further optimized by retraining to achieve an improved model compared to the individual topologies. In this study, four SPDNN models are trained and have been evaluated at 2 stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study.Comment: 44 pages, 25 figure

A new approach to analyze strategy map using an integrated BSC and FUZZY DEMATEL

Today, with ever-increasing competition in global economic conditions, the necessity of effective implementation of strategy map has become an inevitable and necessary. The strategy map represents a general and structured framework for strategic objectives and plays an important role in forming competitive advantages for organizations. It is important to find important factors influencing strategy map and prioritize them based on suitable factors. In this paper, we propose an integration of BSC and Fuzzy DEMATEL technique to rank different items influencing strategy of a production plan. The proposed technique is implemented for real-world case study of glass production

A hybrid model of QFD, SERVQUAL and KANO to increase bank's capabilities

In global market, factors such as precedence of competitors extending shave on market, promoting quality of services and identifying customers' needs are important. This paper attempts to identify strategic services in one of the biggest governmental banks in Iran called Melli bank for getting competition merit using Kano and SERVQUAL compound models and to extend operation quality and to provide suitable strategies. The primary question of this paper is on how to introduce high quality services in this bank. The proposed model of this paper uses a hybrid of three quality-based methods including SERVQUAL, QFD and Kano models. Statistical society in this article is all clients and customers of Melli bank who use this banks' services and based on random sampling method, 170 customers were selected. The study was held in one of provinces located in west part of Iran called Semnan. Research findings show that Melli banks' customers are dissatisfied from the quality of services and to solve this problem the bank should do some restructuring to place some special characteristics to reach better operation at the heed of its affairs. The characteristics include, in terms of their priorities, possibility of transferring money by sale terminal, possibility of creating wireless pos, accelerating in doing bank works, getting special merits to customers who use electronic services, eliminating such bank commission, solving problems in least time as disconnecting system, possibility of receiving foreign exchange by ATM and suitable parking in city

High-Accuracy Facial Depth Models derived from 3D Synthetic Data

In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a sequence of image frames together with ground truth depth and additional metadata such as head pose, light direction, etc. The synthetic data is used to train a CNN based facial depth estimation system which is validated on both synthetic and real images. Potential fields of application include 3D reconstruction, driver monitoring systems, robotic vision systems, and advanced scene understanding

Validation of an Analytical Method for Determination of Benzoapyrene Bread using QuEChERS Method by GC-MS

A fast and simple modified QuEChERS (quick, easy, cheap, rugged and safe) extraction method based on spiked calibration curves and direct sample introduction was developed for determination of Benzo[a]pyrene (BaP) in bread by gas chromatography-mass spectrometry single quadrupole selected ion monitoring (GC/MS-SQ-SIM). Sample preparation includes: extraction of BaP into acetone followed by cleanup with dispersive solid phase extraction. The use of spiked samples for constructing the calibration curve substantially reduced adverse matrix-related effects. The average recovery of BaP at 6 concentration levels was in range of 95-120%. The method was proved to be reproducible with relative standard deviation less than 14.5% for all of the concentration levels. The limit of detection and limit of quantification were 0.3 ng/g and 0.5 ng/g, respectively. Correlation coefficient of 0.997 was obtained for spiked calibration standards over the concentration range of 0.5-20 ng/g. To the best of our knowledge, this is the first time that a QuEChERS method is used for the analysis of BaP in breads. The developed method was used for determination of BaP in 29 traditional (Sangak) and industrial (Senan) bread samples collected from Tehran in 2014. These results showed that two Sangak samples were contaminated with BaP. Therefore, a comprehensive survey for monitoring of BaP in Sangak bread samples seems to be needed. This is the first report concerning contamination of bread samples with BaP in Iran. © 2016 by School of Pharmacy