Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00324259" target="_blank" >RIV/68407700:21730/18:00324259 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0925231218304600" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0925231218304600</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2018.04.030" target="_blank" >10.1016/j.neucom.2018.04.030</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection

Popis výsledku v původním jazyce

The essential goal of this paper is to extend the functionality of the bio-inspired intelligent HTM (Hierarchical Temporal Memory) network towards two capabilities: (i) object recognition in color images, (ii) detection of multiple objects located in clutter color images. The former extension is based on the development of a novel scheme for the application of three parallel HTM networks that separately process color, texture, and shape information in color images. For the latter HTM extension, we propose a novel system in which HTM is combined with a modified model of computational visual attention. We adopt the results of Bi et al. (2010), Hu et al. (2005), and Kučerová (2011) and add new elements for the calculation of image saliency maps. The proposed algorithm enables one to automatically locate individual objects in clutter images. For computer experiments, a special image database is created to simulate ideal single object images and cluttered images with multiple objects on an inhomogeneous background. The recognition performance of HTM alone and in combination with the salient-region detection method is evaluated. We show that the attention subsystem is able to satisfactorily locate multiple objects in clutter color images with an inhomogeneous background. We also perform benchmark calculations for two selected computer vision methods used for object detection in color clutter images. Namely, the cascade detector and template matching methods are used. Our study confirms that the proposed attention system can improve the capabilities of HTM for object classification in cluttered images. The compound system of visual attention and HTM outperforms the compared methods in both criteria (recall and correct detection rate). However, as expected, the system cannot match the recognition accuracy achieved by HTM for single object images, and thus, further research is needed.

Název v anglickém jazyce

Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection

Popis výsledku anglicky

The essential goal of this paper is to extend the functionality of the bio-inspired intelligent HTM (Hierarchical Temporal Memory) network towards two capabilities: (i) object recognition in color images, (ii) detection of multiple objects located in clutter color images. The former extension is based on the development of a novel scheme for the application of three parallel HTM networks that separately process color, texture, and shape information in color images. For the latter HTM extension, we propose a novel system in which HTM is combined with a modified model of computational visual attention. We adopt the results of Bi et al. (2010), Hu et al. (2005), and Kučerová (2011) and add new elements for the calculation of image saliency maps. The proposed algorithm enables one to automatically locate individual objects in clutter images. For computer experiments, a special image database is created to simulate ideal single object images and cluttered images with multiple objects on an inhomogeneous background. The recognition performance of HTM alone and in combination with the salient-region detection method is evaluated. We show that the attention subsystem is able to satisfactorily locate multiple objects in clutter color images with an inhomogeneous background. We also perform benchmark calculations for two selected computer vision methods used for object detection in color clutter images. Namely, the cascade detector and template matching methods are used. Our study confirms that the proposed attention system can improve the capabilities of HTM for object classification in cluttered images. The compound system of visual attention and HTM outperforms the compared methods in both criteria (recall and correct detection rate). However, as expected, the system cannot match the recognition accuracy achieved by HTM for single object images, and thus, further research is needed.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

1872-8286

Svazek periodika

307

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

172-183

Kód UT WoS článku

000436617900016

EID výsledku v databázi Scopus

2-s2.0-85047060879