Combining Convolutional Neural Networks and Cognitive Models to Predict Novel Object Recognition in Humans

2018 ◽  
Author(s):  
Jeffrey Annis ◽  
Thomas Palmeri
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Cognitive Models ◽  
Novel Object Recognition ◽  
Novel Object

scholarly journals Mice deficient in the NAAG synthetase II gene Rimkla are impaired in a novel object recognition task

2021 ◽  
Author(s):  
Ivonne Becker ◽  
Lihua Wang‐Eckhardt ◽  
Julia Lodder‐Gadaczek ◽  
Yong Wang ◽  
Agathe Grünewald ◽  
...  
Keyword(s):  
Object Recognition ◽  
Recognition Task ◽  
Novel Object Recognition ◽  
Novel Object ◽  
Object Recognition Task ◽  
Novel Object Recognition Task

scholarly journals Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110105
Author(s):  
Jnana Sai Abhishek Varma Gokaraju ◽  
Weon Keun Song ◽  
Min-Ho Ka ◽  
Somyot Kaitwanidvilai
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Doppler Radar ◽  
Autonomous Vehicle ◽  
Sampling Point ◽  
Body Segment ◽  
Kinematic Modeling ◽  
Deep Convolutional Neural Networks ◽  
Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

scholarly journals RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43110-43136 ◽  
Author(s):  
Mingliang Gao ◽  
Jun Jiang ◽  
Guofeng Zou ◽  
Vijay John ◽  
Zheng Liu
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks
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