Genetic algorithms for object recognition in a complex scene

2002 ◽  
Author(s):  
D.L. Swets ◽  
B. Punch ◽  
J. Weng
Keyword(s):  
Genetic Algorithms ◽  
Object Recognition ◽  
Complex Scene

Object Extraction Using Topological Models from Complex Scene Image

2017 ◽  
pp. 335-357
Author(s):  
Uday Pratap Singh ◽  
Sanjeev Jain
Keyword(s):  
Object Recognition ◽  
Object Segmentation ◽  
Multimedia Data ◽  
Region Merging ◽  
Complex Scene ◽  
Object Contour ◽  
Merging Process ◽  
Feasible Solutions ◽  
Neighbourhood Graph ◽  
Topological Models

Efficient and effective object recognition from a multimedia data are very complex. Automatic object segmentation is usually very hard for natural images; interactive schemes with a few simple markers provide feasible solutions. In this chapter, we propose topological model based region merging. In this work, we will focus on topological models like, Relative Neighbourhood Graph (RNG) and Gabriel graph (GG), etc. From the Initial segmented image, we constructed a neighbourhood graph represented different regions as the node of graph and weight of the edges are the value of dissimilarity measures function for their colour histogram vectors. A method of similarity based region merging mechanism (supervised and unsupervised) is proposed to guide the merging process with the help of markers. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. To the validation of proposed method extensive experiments are performed and the result shows that the proposed method extracts the object contour from the complex background.

Content-Aware Image Retargeting Based on Semantic Analysis

2014 ◽  
Vol 577 ◽  
pp. 777-781 ◽  
Author(s):  
Hao Cheng ◽  
Wei Li ◽  
Pei Min Zhong
Keyword(s):  
Object Recognition ◽  
Energy Loss ◽  
Energy Function ◽  
Semantic Analysis ◽  
Triangular Mesh ◽  
Image Retargeting ◽  
Complex Scene ◽  
Important Object ◽  
Least Energy ◽  
Content Aware

This paper presents a retargeting approach based on semantic analysis. Our approach has better performance in images that have comlicated backgroud or multi-objects. Our aim is to protect important object in retargeting process. So we brought in object recognition and image importance calculation for retargeting. This approach consists of three parts: object recognition, the importance of image calculation and image retargeting. (i) Firstly we optimized semantic texton forest (STF)[11]and got much better results of object recognition.(ii) Secondly we presented a method called dynamically adjust importance image calculation. (iii) Thirdly we give a retargeting method based on triangular mesh. According to importance image we cover Delaunay triangular mesh on image and solve optimization mesh transforming based on energy function which subjects to least energy loss and boundary restraint. Compared with previous approaches, our method has better result in some complex scene images.

scholarly journals Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network

2020 ◽  
Vol 3 (2) ◽  
pp. 258-265
Author(s):  
Al-Khowarizmi Al-Khowarizmi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Object Recognition ◽  
Success Rate ◽  
Model Classification ◽  
Evolutionary Neural Network ◽  
Evolutionary Neural Networks

Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better

scholarly journals DIGITISED HUMAN LIFE FORM ISOMORPHIC ALGORITHM

2016 ◽  
pp. 162-167
Author(s):  
T. MOHANAN ◽  
V. MOHANATHAN ◽  
D. JEEVANANDHAM ◽  
I. SARAVANAN
Keyword(s):  
Genetic Algorithms ◽  
Object Recognition ◽  
Real World ◽  
Life Form ◽  
Human Life ◽  
Life Forms ◽  
The Real ◽  
Digital World ◽  
Artificial Environment ◽  
Code Base

ISOs are "isomorphic algorithms", which are life forms that emerged-- unplanned--from the artificial environment of the grid.Isomorphic Algorithms (better known as ISOs) are a race of programs that spontaneously evolved on the Grid, as opposed to being created by users. ISOs differ from standard programs with distinctions in their appearance and capabilities, but where they are truly unique is in their code base. While regular programs conform to the rigid structure defined by their users, ISOs have evolved, complete with a genetic code of sorts.. This inner structure of their code has allowed ISOs to develop beyond the capabilities of regular programs.. These miraculous algorithms had the capacity to evolve and change and grow at tremendous rates utilizing genetic algorithms, whereas normal programs that were intentionally written by users could only change slowly in anticipated fashions. What's REALLY important about the isomorphic algorithms id, it is revealed that indeed programs can escape the grid into the real world, essentially raising questions of what is life, sentience, the soul, etc. that humanity is no longer confined to humans, but has essentially arisen out of our digital dust and the real and digital world can become interchangeable This kind of human life form is made possible for ISOs because of digital DNA and object recognition which is made possible in isomorphic algorithms.. In this paper we aregoing to describe how an algorithm can be emergedinto human life form.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

Sign in / Sign up

Export Citation Format

Share Document