Image Receptive Fields Neural Networks for Object Recognition

Author(s):  
Paméla Daum ◽  
Jean-Luc Buessler ◽  
Jean-Philippe Urban
Author(s):  
Katherine R. Storrs ◽  
Tim C. Kietzmann ◽  
Alexander Walther ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte

ABSTRACTDeep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual areas in the brain. What remains unclear is how strongly network design choices, such as architecture, task training, and subsequent fitting to brain data contribute to the observed similarities. Here we compare a diverse set of nine DNN architectures on their ability to explain the representational geometry of 62 isolated object images in human inferior temporal (hIT) cortex, as measured with functional magnetic resonance imaging. We compare untrained networks to their task-trained counterparts, and assess the effect of fitting them to hIT using a cross-validation procedure. To best explain hIT, we fit a weighted combination of the principal components of the features within each layer, and subsequently a weighted combination of layers. We test all models across all stages of training and fitting for their correlation with the hIT representational dissimilarity matrix (RDM) using an independent set of images and subjects. We find that trained models significantly outperform untrained models (accounting for 57% more of the explainable variance), suggesting that features representing natural images are important for explaining hIT. Model fitting further improves the alignment of DNN and hIT representations (by 124%), suggesting that the relative prevalence of different features in hIT does not readily emerge from the particular ImageNet object-recognition task used to train the networks. Finally, all DNN architectures tested achieved equivalent high performance once trained and fitted. Similar ability to explain hIT representations appears to be shared among deep feedforward hierarchies of nonlinear features with spatially restricted receptive fields.


2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110105
Author(s):  
Jnana Sai Abhishek Varma Gokaraju ◽  
Weon Keun Song ◽  
Min-Ho Ka ◽  
Somyot Kaitwanidvilai

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.


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

1989 ◽  
Vol 12 (3) ◽  
pp. 381-397 ◽  
Author(s):  
Gary W. Strong ◽  
Bruce A. Whitehead

AbstractPurely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions (the incorrect combination of features into perceived objects in a stimulus array) have been demonstrated empirically (Treisman 1986; Treisman & Gelade 1980). Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: (i) How can objects be identified from simultaneously attended features in a parallel, distributed representation? (ii) How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention.


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