Genetic algorithm for model-based matching of projected images of three-dimensional objects

2003 ◽  
Vol 150 (6) ◽  
pp. 351 ◽  
Author(s):  
P.W.M. Tsang ◽  
Z. Yu
Keyword(s):  
Genetic Algorithm ◽  
Three Dimensional ◽  
Three Dimensional Objects ◽  
Model Based

scholarly journals Model-based interpretation of complex and variable images

1997 ◽  
Vol 352 (1358) ◽  
pp. 1267-1274 ◽  
Author(s):  
C. J. Taylor ◽  
T. F. Cootes ◽  
A. Lanitis ◽  
G. Edwards ◽  
P. Smyth ◽  
...  
Keyword(s):  
Image Interpretation ◽  
Three Dimensional ◽  
Image Understanding ◽  
Two Dimensions ◽  
Develop Model ◽  
Image Structure ◽  
Three Dimensional Objects ◽  
Model Based ◽  
Natural Vision ◽  
Intractable Problems

The ultimate goal of machine vision is image understanding—the ability not only to recover image structure but also to know what it represents. By definition, this involves the use of models which describe and label the expected structure of the world. Over the past decade, model–based vision has been applied successfully to images of man–made objects. It has proved much more difficult to develop model–based approaches to the interpretation of images of complex and variable structures such as faces or the internal organs of the human body (as visualized in medical images). In such cases it has been problematic even to recover image structure reliably, without a model to organize the often noisy and incomplete image evidence. The key problem is that of variability. To be useful, a model needs to be specific—that is, to be capable of representing only ‘legal’ examples of the modelled object(s). It has proved difficult to achieve this whilst allowing for natural variability. Recent developments have overcome this problem; it has been shown that specific patterns of variability in shape and grey–level appearance can be captured by statistical models that can be used directly in image interpretation. The details of the approach are outlined and practical examples from medical image interpretation and face recognition are used to illustrate how previously intractable problems can now be tackled successfully. It is also interesting to ask whether these results provide any possible insights into natural vision; for example, we show that the apparent changes in shape which result from viewing three–dimensional objects from different viewpoints can be modelled quite well in two dimensions; this may lend some support to the ‘characteristic views’ model of natural vision.

scholarly journals Fast Overlap Detection between Hard-Core Colloidal Cuboids and Spheres. The OCSI Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 72
Author(s):  
Luca Tonti ◽  
Alessandro Patti
Keyword(s):  
Colloidal Particles ◽  
Dynamic Properties ◽  
Wide Spectrum ◽  
Three Dimensional ◽  
Hard Core ◽  
Detection Algorithm ◽  
Three Dimensional Objects ◽  
Aggregation Behaviour ◽  
Sphere Radius ◽  
User Friendly

Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

scholarly journals The Effect of Material Properties on the Perceived Shape of Three-Dimensional Objects

i-Perception ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 204166952098231
Author(s):  
Masakazu Ohara ◽  
Juno Kim ◽  
Kowa Koida
Keyword(s):  
Material Properties ◽  
Regional Variation ◽  
Three Dimensional ◽  
Mean Square ◽  
Viewing Condition ◽  
Three Dimensional Objects ◽  
Transparent Objects ◽  
Dimensional Shape ◽  
Local Root ◽  
Three Dimensional Shape

Perceiving the shape of three-dimensional objects is essential for interacting with them in daily life. If objects are constructed from different materials, can the human visual system accurately estimate their three-dimensional shape? We varied the thickness, motion, opacity, and specularity of globally convex objects rendered in a photorealistic environment. These objects were presented under either dynamic or static viewing condition. Observers rated the overall convexity of these objects along the depth axis. Our results show that observers perceived solid transparent objects as flatter than the same objects rendered with opaque reflectance properties. Regional variation in local root-mean-square image contrast was shown to provide information that is predictive of perceived surface convexity.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

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