Learning to Be (In)variant: Combining Prior Knowledge and Experience to Infer Orientation Invariance in Object Recognition

2016 ◽  
Vol 41 ◽  
pp. 1183-1201
Author(s):  
Joseph L. Austerweil ◽  
Thomas L. Griffiths ◽  
Stephen E. Palmer
Keyword(s):  
Object Recognition ◽  
Prior Knowledge ◽  
Orientation Invariance

scholarly journals Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition

2017 ◽  
Vol 26 (10) ◽  
pp. 4669-4683 ◽  
Author(s):  
Sebastien C. Wong ◽  
Victor Stamatescu ◽  
Adam Gatt ◽  
David Kearney ◽  
Ivan Lee ◽  
...  
Keyword(s):  
Object Recognition ◽  
Prior Knowledge

scholarly journals MAKING OBJECT LEARNING AND RECOGNITION AN ACTIVE PROCESS

2008 ◽  
Vol 05 (02) ◽  
pp. 267-286 ◽  
Author(s):  
ALEŠ UDE ◽  
DAMIR OMRČEN ◽  
GORDON CHENG
Keyword(s):  
Object Recognition ◽  
Prior Knowledge ◽  
Experimental Results ◽  
3D Object Recognition ◽  
Object Representations ◽  
Active Process ◽  
Bottom Up ◽  
3D Object ◽  
3D Objects ◽  
Object Learning

The exploration and learning of new objects is an essential capability of a cognitive robot. In this paper we focus on making use of the robot's manipulation abilities to learn complete object representations suitable for 3D object recognition. Taking control of the object allows the robot to focus on relevant parts of the images, thus bypassing potential pitfalls of purely bottom-up attention and segmentation. The main contribution of the paper consists in integrated visuomotor processes that allow the robot to learn object representations by manipulation without having any prior knowledge about the objects. Our experimental results show that the acquired data is of sufficient quality to train a classifier that can recognize 3D objects independently of the viewpoint.

RGB-D Object Recognition via Incorporating Latent Data Structure and Prior Knowledge

2015 ◽  
Vol 17 (11) ◽  
pp. 1899-1908 ◽  
Author(s):  
Jinhui Tang ◽  
Lu Jin ◽  
Zechao Li ◽  
Shenghua Gao
Keyword(s):  
Data Structure ◽  
Object Recognition ◽  
Prior Knowledge ◽  
Latent Data

scholarly journals Underwater Object Recognition Using Transformable Template Matching Based on Prior Knowledge

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jianjiang Zhu ◽  
Siquan Yu ◽  
Zhi Han ◽  
Yandong Tang ◽  
Chengdong Wu
Keyword(s):  
Object Recognition ◽  
Prior Knowledge ◽  
Template Matching ◽  
Saliency Detection ◽  
Mine Detection ◽  
Target Region ◽  
Detection Techniques ◽  
Recognition Ability ◽  
Object Rotation ◽  
Underwater Object

Underwater object recognition in sonar images, such as mine detection and wreckage detection of a submerged airplane, is a very challenging task. The main difficulties include but are not limited to object rotation, confusion from false targets and complex backgrounds, and extensibility of recognition ability on diverse types of objects. In this paper, we propose an underwater object detection and recognition method using a transformable template matching approach based on prior knowledge. Specifically, we first extract features and construct a template from sonar video sequences based on the analysis of acoustic shadows and highlight regions. Then, we identify the target region in the objective image by fast saliency detection techniques based on FFT, which can significantly improve efficiency by avoiding an exhaustive global search. After affine transformation of the template according to the orientation of the target, we extract normalized gradient features and calculate the similarity between the template and the target region, which can solve various difficulties mentioned above using only one template. Experimental results demonstrate that the proposed method can well recognize different underwater objects, such as mine-like objects and triangle-like objects and can satisfy the demands of real-time application.

scholarly journals Prior knowledge-based deep learning method for indoor object recognition and application

2018 ◽  
Vol 6 (1) ◽  
pp. 249-257 ◽  
Author(s):  
Xintao Ding ◽  
Yonglong Luo ◽  
Qingde Li ◽  
Yongqiang Cheng ◽  
Guorong Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Prior Knowledge ◽  
Learning Method ◽  
Knowledge Based

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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