IMAGE COMPOSITION WITH COLOR HARMONIZATION

2012 ◽  
Vol 26 (03) ◽  
pp. 1254001
Author(s):  
YANLI WAN ◽  
ZHEN TANG ◽  
ZHENJIANG MIAO ◽  
BO LI
Keyword(s):  
Cost Function ◽  
Optimization Method ◽  
Image Composition ◽  
Local Smoothness ◽  
Color Harmony ◽  
Matching Cost ◽  
Computer Generated Imagery ◽  
The Cost ◽  
Previous Image

Image composition is a very important technique in computer generated imagery. Besides some factors such as contrast, texture and noise that affect the quality of the composition, color harmony between fore- and background is also an important factor that would affect the quality of the composition. However, in the previous image composition techniques, color harmony between fore- and background is seldom considered. In this paper, an optimization method is proposed to deal with the color harmonization problem that used in image composition. A cost function is derived from the local smoothness of the hue values, and the image is harmonized by minimizing the cost function. A new matching cost function is proposed to select the best matching harmonic schemes. Our approach overcomes several shortcomings of the existing color harmonization methods. We validate the performance of our method and demonstrate its effectiveness with a variety of experiments.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Convergence of Simulated Annealing with Feedback Temperature Schedules

1997 ◽  
Vol 11 (3) ◽  
pp. 279-304 ◽  
Author(s):  
M. Kolonko ◽  
M. T. Tran
Keyword(s):  
Simulated Annealing ◽  
Cost Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Method ◽  
Search Process ◽  
Local Optimum ◽  
Fixed Sequence ◽  
Temperature Parameter ◽  
The Cost

It is well known that the standard simulated annealing optimization method converges in distribution to the minimum of the cost function if the probability a for accepting an increase in costs goes to 0. α is controlled by the “temperature” parameter, which in the standard setup is a fixed sequence of values converging slowly to 0. We study a more general model in which the temperature may depend on the state of the search process. This allows us to adapt the temperature to the landscape of the cost function. The temperature may temporarily rise such that the process can leave a local optimum more easily. We give weak conditions on the temperature schedules such that the process of solutions finally concentrates near the optimal solutions. We also briefly sketch computational results for the job shop scheduling problem.

scholarly journals Mathematical modeling for a multiproduct manufacturing system featuring postponement, external suppliers, overtime, and scrap

2022 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
Yuan-Shyi P. Chiu ◽  
Jian-Hua Lian ◽  
Victoria Chiu ◽  
Yunsen Wang ◽  
Hsiao-Chun Wu
Keyword(s):  
Decision Making ◽  
Cost Function ◽  
Due Dates ◽  
Numerical Illustration ◽  
Screening Process ◽  
Multiproduct Manufacturing ◽  
The Cost ◽  
Decision Making Model ◽  
Mathematical Derivation

Manufacturing firms operating in today’s competitive global markets must continuously find the appropriate manufacturing scheme and strategies to effectively meet customer needs for various types of quality of merchandise under the constraints of short order lead-time and limited in-house capacity. Inspired by the offering of a decision-making model to aid smooth manufacturers’ operations, this study builds an analytical model to expose the influence of the outsourcing of common parts, postponement policies, overtime options, and random scrapped items on the optimal replenishment decision and various crucial system performance indices of the multiproduct problem. A two-stage fabrication scheme is presented to handle the products’ commonality and the uptime-reduced strategies to satisfy the short amount of time before the due dates of customers’ orders. A screening process helps identify and remove faulty items to ensure the finished lot’s anticipated quality. Mathematical derivation assists us in finding the manufacturing relevant total cost function. The differential calculus helps optimize the cost function and determine the optimal stock-replenishing rotation cycle policy. Lastly, a simulated numerical illustration helps validate our research result’s applicability and demonstrate the model’s capability to disclose the crucial managerial insights and facilitate manufacturing-relevant decision making.

scholarly journals Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4362
Author(s):  
Subramaniam Saravana Sankar ◽  
Yiqun Xia ◽  
Julaluk Carmai ◽  
Saiprasit Koetniyom
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Fuel Consumption ◽  
Optimization Algorithm ◽  
Internal Combustion Engines ◽  
Optimization Method ◽  
Computational Time ◽  
Weight Factor ◽  
Driving Cycles ◽  
The Cost

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

scholarly journals The cost function of the data fusion process and its application

2019 ◽  
Vol 12 (5) ◽  
pp. 2967-2977 ◽  
Author(s):  
Simone Ceccherini ◽  
Nicola Zoppetti ◽  
Bruno Carli ◽  
Ugo Cortesi ◽  
Samuele Del Bianco ◽  
...  
Keyword(s):  
Data Fusion ◽  
Cost Function ◽  
Covariance Matrix ◽  
Expected Value ◽  
Covariance Matrices ◽  
Complete Data ◽  
Sufficient Statistics ◽  
The Cost ◽  
Analytical Expressions

Abstract. When the complete data fusion method is used to fuse inconsistent measurements, it is necessary to add to the measurement covariance matrix of each fusing profile a covariance matrix that takes into account the inconsistencies. A realistic estimate of these inconsistency covariance matrices is required for effectual fused products. We evaluate the possibility of assisting the estimate of the inconsistency covariance matrices using the value of the cost function minimized in the complete data fusion. The analytical expressions of expected value and variance of the cost function are derived. Modelling the inconsistency covariance matrix with one parameter, we determine the value of the parameter that makes the reduced cost function equal to its expected value and use the variance to assign an error to this determination. The quality of the inconsistency covariance matrix determined in this way is tested for simulated measurements of ozone profiles obtained in the thermal infrared in the framework of the Sentinel-4 mission of the Copernicus programme. As expected, the method requires sufficient statistics and poor results are obtained when a small number of profiles are being fused together, but very good results are obtained when the fusion involves a large number of profiles.

Upgrade Planning of Used Products Considering Uncertainty in Market Demand, Quantity and Variability in Quality of Returns

2014 ◽  
Author(s):  
Ardeshir Raihanian Mashhadi ◽  
Sara Behdad
Keyword(s):  
Profit Maximization ◽  
Optimization Method ◽  
Quality Level ◽  
Market Demand ◽  
Environmental Legislation ◽  
Take Back ◽  
Return Flows ◽  
The Cost ◽  
Original Equipment Manufacturers

Remanufacturing is becoming an important business as market demand for remanufactured products increases and environmental legislation puts further enforcement on Original Equipment Manufacturers (OEMs). However, profitability of salvaging operations is still a challenge in remanufacturing industry. Several factors influence the cost effectiveness of remanufacturing operations, including variability in the quality of received used items and uncertainty in the quantity of return flows and the market demand for those products. Remanufacturing companies need to handle these sources of uncertainties in order to improve their profitability. The current paper develops a Stochastic Optimization method to model these sources of uncertainties in take-back and inventory planning systems. The main purpose of the model is to determine the best upgrade level for a received product with certain quality level with the aim of profit maximization. An example of personal computer is provided to show the application of the method.

scholarly journals Automatic Registration and Error Detection of Multiple Slices Using Landmarks

2001 ◽  
Vol 23 (3-4) ◽  
pp. 159-165
Author(s):  
Hans Frimmel ◽  
Lars Egevad ◽  
Christer Busch ◽  
Ewert Bengtsson
Keyword(s):  
Cost Function ◽  
Error Detection ◽  
3D Structure ◽  
Automatic Registration ◽  
Registration Method ◽  
Automated Method ◽  
Current State ◽  
Manual Registration ◽  
The Cost

Objectives. When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this paper, is based on a model of the slices and the landmark needles. The method has been used to register slices of prostates in order to create 3D computer models. Manual registration of the same prostates has been undertaken and compared with the results from the algorithm. Methods. Prostates from sixteen men who underwent radical prostatectomy were formalin fixed with landmark needles, sliced and the slices were computer reconstructed. The cost function takes rotation and translation for each prostate slice, as well as slope and offset for each landmark needle as input. The current quality of fit of the model, using the input parameters given, is returned. The function takes the built‐in instability of the model into account. The method uses a standard algorithm to optimize the prostate slice positions. To verify the result, s standard method in statistics was used. Results. The methods were evaluated for 16 prostates. When testing blindly, a physician could not determine whether the registration shown to him were created by the automated method described in this paper, or manually by an expert, except in one out of 16 cases. Visual inspection and analysis of the outlier confirmed that the input data had been deformed. The automatic detection of erroneous slices marked a few slices, including the outlier, as suspicious. Conclusions. The model based registration performs better than traditional simple slice‐wise registration. In the case of prostate slice registration, other aspects, such as the physical slicing method used, may be more important to the final result than the selection of registration method to use.

scholarly journals The cost function of the data fusion process and its application

2019 ◽  
Author(s):  
Simone Ceccherini ◽  
Nicola Zoppetti ◽  
Bruno Carli ◽  
Ugo Cortesi ◽  
Samuele Del Bianco ◽  
...  
Keyword(s):  
Data Fusion ◽  
Cost Function ◽  
Covariance Matrix ◽  
Expected Value ◽  
Covariance Matrices ◽  
Complete Data ◽  
Sufficient Statistics ◽  
The Cost ◽  
Analytical Expressions

Abstract. When the complete data fusion method is used to fuse inconsistent measurements, it is necessary to add to the measurement covariance matrix of each fusing profile a covariance matrix that takes into account the inconsistencies. A realistic estimate of these inconsistency covariance matrices is required for effectual fused products. We evaluate the possibility of assisting the estimate of the inconsistency covariance matrices using the value of the cost function minimized in the complete data fusion. The analytical expressions of expected value and variance of the cost function are derived. Modelling the inconsistency covariance matrix with one parameter, we determine the value of the parameter that makes the reduced cost function equal to its expected value and use the variance to assign an error to this determination. The quality of the inconsistency covariance matrix determined in this way is tested for simulated measurements of ozone profiles obtained in the thermal infrared in the framework of the Sentinel 4 mission of the Copernicus programme. As expected, the method requires a sufficient statistics and poor results are obtained when a small numbers of profiles are being fused together, but very good results are obtained when the fusion involves a large number of profiles.

scholarly journals Multimodal trajectory optimization for motion planning

2020 ◽  
Vol 39 (8) ◽  
pp. 983-1001
Author(s):  
Takayuki Osa
Keyword(s):  
Motion Planning ◽  
Cost Function ◽  
Trajectory Optimization ◽  
Multiple Solutions ◽  
3D Space ◽  
Single Solution ◽  
Planning Methods ◽  
The Cost ◽  
2D And 3D

Existing motion planning methods often have two drawbacks: (1) goal configurations need to be specified by a user, and (2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.

Sign in / Sign up

Export Citation Format

Share Document