Object Recognition via Adaptive Multi-level Feature Integration

2010 ◽  
Author(s):  
Mei Wang ◽  
Yanling Wu ◽  
Guangda Li ◽  
Xiangdong Zhou
Keyword(s):  
Object Recognition ◽  
Feature Integration ◽  
Multi Level

scholarly journals Multi-level Crowding and the Paradox of Object Recognition in Clutter

2018 ◽  
Vol 28 (3) ◽  
pp. R127-R133 ◽  
Author(s):  
Mauro Manassi ◽  
David Whitney
Keyword(s):  
Object Recognition ◽  
Multi Level

scholarly journals Military Object Detection in Defence using Multi-Level Capsule Networks

2021 ◽  
Author(s):  
B Janakiramiaha ◽  
Kalyani G ◽  
Karuna A ◽  
Narasimha Prasad L V ◽  
Krishna M
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Target Detection ◽  
Network Architecture ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Automatic Target Detection ◽  
Security Issues ◽  
Multi Level

Abstract Automatic target detection plays a major role in automated war operations. The key concept behind automated target detection is military objects recognition from the captured images. For object recognition in the given image, Convolutional Neural Network (CNN) is a powerful classification network. But in general CNNs are trained for general object recognition. But, the performance of CNN depends mainly on the size of the training set. The size of the training data is generally available in less proportion for military objects due to its operational and security issues. Hence the performance of CNN may degrade sharply. To address the issue of military objects, a relatively new neural network architecture called Capsule Network (CapsNet) is introduced. Hence, in this article, a variant of CapsNet called Multi-level CapsNet framework is projected for military object recognition under the case of small training set. The introduced framework of this paper is validated on a dataset of military objects which are collected from the internet. The dataset contains particularly five military objects and the similar civil ones. The proposed framework demonstrates a large improvement of 96.54% of accuracy for military object recognition. Experiments demonstrate that the proposed framework can accomplish a high recognition precision, superior to many other algorithms such as conventional Support Vector Machines and transfer learning based CNNs.

Comparative Study of the Housing Security Object Definition Methods

2014 ◽  
Vol 501-504 ◽  
pp. 2593-2600
Author(s):  
Chang Ming Hu ◽  
Jian Hua Yang ◽  
Bin Qi Guo
Keyword(s):  
Distribution Function ◽  
Object Recognition ◽  
Income Distribution ◽  
Comparative Study ◽  
Scientific Method ◽  
Housing Security ◽  
Theoretical Support ◽  
Object Based ◽  
Multi Level ◽  
Definition Of

Due to the complexity of the definition of the housing security object, the object recognition mostly depends on experience data, lacking theoretical support. The scientific method to define the income line of the housing security object is the key to ensure the appropriate level of housing security and the fairness of social welfare. This paper introduces several common methods to define the income line of housing security, and the main problem of these methods is not matched with the multi-level security system. For that, this paper introduces the method to define the income line of the housing security object based on income distribution function and the quantile theory, uses EViews to analyze house security object, and puts forward relevant suggestions for improving the management of the security object.

Verlaufsanalyse wöchentlicher Selbst- und Fremdeinschätzungen in der Langzeittherapie suchtkranker Jugendlicher

2019 ◽  
Vol 47 (2) ◽  
pp. 126-137
Author(s):  
Ferdinand Keller ◽  
Tatjana Stadnitski ◽  
Jakob Nützel ◽  
Renate Schepker
Keyword(s):  
Allen Vier ◽  
Multi Level

Zusammenfassung. Fragestellung: Über Veränderungen in der emotionalen Befindlichkeit von Jugendlichen während einer Suchttherapie ist wenig bekannt. Methode: Die Jugendlichen füllten wöchentlich einen entsprechenden Fragebogen aus, analog ihre Bezugsbetreuer eine parallelisierte Kurzfassung. Von 42 Jugendlichen liegen insgesamt 853 Bogen und von den Bezugsbetreuern 708 Bogen vor. Die Fragebogen wurden zunächst faktorenanalytisch hinsichtlich ihrer Dimensionalität ausgewertet, anschließend wurden gruppenbezogene Verlaufsanalysen (Multi-Level-Modelle) und Abhängigkeitsanalysen auf Einzelfallebene (Zeitreihenanalysen) durchgeführt. Ergebnisse: Im Jugendlichenfragebogen ergaben sich vier Faktoren: negative Befindlichkeit, Wertschätzung von Therapie/Betreuung, Motivation und Suchtdynamik. Die Übereinstimmung zwischen den Jugendlichen- und der (einfaktoriellen) Betreuereinschätzung fiel insgesamt niedrig bis mäßig aus, brachte aber auf Einzelfallebene differenziertere Ergebnisse. Im Verlauf nahmen die Werte auf allen vier Jugendlichenskalen ab. Einzig der Verlauf der Wertschätzung in der Eingewöhnungsphase war prädiktiv für den späteren Abbruch der Maßnahme: Bei den Abbrechern nahm die Wertschätzung ab, während sie bei den Beendern initial stieg. Schlussfolgerungen: Der bedeutsamste Faktor in Bezug auf die Therapiebeendigung suchtkranker Jugendlicher scheint die Wertschätzung von Therapie/Betreuung zu sein, während die Motivation jugendtypische Schwankungen aufweist. Der Suchtdynamik kam eine deutlich weniger bedeutende Rolle zu als allgemein angenommen. Programme in der Langzeittherapie sollten die Wertschätzung von Therapie/Betreuung künftig mehr fokussieren als die Suchtdynamik.

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.

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