scholarly journals A separable neural code in monkey IT enables perfect CAPTCHA decoding

2020 ◽  
Author(s):  
Harish Katti ◽  
S. P. Arun
Keyword(s):  
Neural Networks ◽  
Word Recognition ◽  
Machine Vision ◽  
Turing Test ◽  
Neural Code ◽  
Letter Recognition ◽  
Neural Recordings

ABSTRACTReading distorted letters is easy for us but so challenging for machine vision that it is used on websites as CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart). How does our brain solve this problem? One solution is to have neurons invariant to letter distortions but selective for letter combinations. Another is for neurons to separately encode letter distortions and combinations. Here, we provide evidence for the latter using neural recordings in the monkey inferior temporal (IT) cortex. Neurons encoded letter distortions as a product of letter and distortion tuning, and letter combinations as a sum of letters. These rules were sufficient for perfect CAPTCHA decoding and were also present in neural networks trained for word recognition. Taken together, our findings suggest that a separable neural code enables efficient letter recognition.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading

2021 ◽  
Vol 118 (46) ◽  
pp. e2104779118
Author(s):  
T. Hannagan ◽  
A. Agrawal ◽  
L. Cohen ◽  
S. Dehaene
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Visual Pathway ◽  
Neural Code ◽  
Letter Recognition ◽  
Invariant Representation ◽  
Spoken Language Processing ◽  
Visual Word Form Area ◽  
Ventral Visual Pathway

The visual word form area (VWFA) is a region of human inferotemporal cortex that emerges at a fixed location in the occipitotemporal cortex during reading acquisition and systematically responds to written words in literate individuals. According to the neuronal recycling hypothesis, this region arises through the repurposing, for letter recognition, of a subpart of the ventral visual pathway initially involved in face and object recognition. Furthermore, according to the biased connectivity hypothesis, its reproducible localization is due to preexisting connections from this subregion to areas involved in spoken-language processing. Here, we evaluate those hypotheses in an explicit computational model. We trained a deep convolutional neural network of the ventral visual pathway, first to categorize pictures and then to recognize written words invariantly for case, font, and size. We show that the model can account for many properties of the VWFA, particularly when a subset of units possesses a biased connectivity to word output units. The network develops a sparse, invariant representation of written words, based on a restricted set of reading-selective units. Their activation mimics several properties of the VWFA, and their lesioning causes a reading-specific deficit. The model predicts that, in literate brains, written words are encoded by a compositional neural code with neurons tuned either to individual letters and their ordinal position relative to word start or word ending or to pairs of letters (bigrams).

scholarly journals A STUDY ON COLOR SORTING OF TOMATOES MATURITY USING MACHINE VISION AND ARTIFICIAL NEURAL NETWORKS

2015 ◽  
Vol 93 (1) ◽  
pp. 147-161
Author(s):  
ABD EL-WAHAB S. KASSEM ◽  
MOHAMED A. SABBAH ◽  
ABED EL WAHED M. ABOUKARIMA ◽  
RABAB M. KAMEL
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Machine Vision ◽  
Artificial Neural

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks

2020 ◽  
Vol 16 (9) ◽  
pp. 5769-5779
Author(s):  
Yingjie Zhang ◽  
Hong Geok Soon ◽  
Dongsen Ye ◽  
Jerry Ying Hsi Fuh ◽  
Kunpeng Zhu
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Convolutional Neural Networks ◽  
Process Monitoring ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Powder Bed

scholarly journals Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms

2019 ◽  
Vol 181 ◽  
pp. 140-156 ◽  
Author(s):  
Henry A.M. Williams ◽  
Mark H. Jones ◽  
Mahla Nejati ◽  
Matthew J. Seabright ◽  
Jamie Bell ◽  
...  
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Convolutional Neural Networks ◽  
Robotic Arms
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