An efficient and hybrid pulse coupled neural network - based object detection framework based on machine learning

2021 ◽  
pp. 107615
Author(s):  
Dharini S ◽  
Sanjay Jain
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Object Detection ◽  
Pulse Coupled Neural Network

scholarly journals End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy

Soft Matter ◽  
2020 ◽  
Vol 16 (7) ◽  
pp. 1751-1759 ◽  
Author(s):  
Eric N. Minor ◽  
Stian D. Howard ◽  
Adam A. S. Green ◽  
Matthew A. Glaser ◽  
Cheol S. Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real World ◽  
Simulated Data ◽  
Video Microscopy ◽  
Experimental Physics ◽  
Simulated Images

We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data.

scholarly journals Brand Detection System using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 497-500
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Visual Representation ◽  
Cosmetic Products ◽  
Medical Object

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

scholarly journals Machine Learning for Detection of Palm Oil Leaf Disease Visually using Convolutional Neural Network Algorithm

2021 ◽  
Vol 4 (2) ◽  
pp. 286-293
Author(s):  
Asrianda Asrianda ◽  
Hafizh Al Kautsar Aidilof ◽  
Yoga Pangestu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Palm Oil ◽  
Leaf Disease ◽  
Neural Network Algorithm

Artificial intelligence (AI) merupakan bidang ilmu pengetahuan yang saat ini menjadi isu yang menarik dan masih diteliti secara luas. Salah satu cabang dari pengembangan AI adalah computer vision yang di dalamnya terdapat topik pembahasan image classification dan object detection. Machine learning dapat dimanfaatkan di dalam bidang computer vision untuk melakukan object detection dan image classification, yaitu dengan menggunakan algoritma Convolutional Neural Network (CNN). CNN banyak digunakan pada penelitian terdahulu karena akurasinya yang tinggi. Pada penelitian ini, CNN digunakan untuk mendeteksi jenis penyakit daun tanaman kelapa sawit, dengan dataset sebanyak 60 gambar, dimana 50 diantaranya merupakan daun dengan 5 jenis penyakit berbeda, yaitu Curvularia sp, Cochliobolus carbonus, Capnodium sp, Drecshlera, dan defisiensi unsur hara. Sedangkan 10 sisanya merupakan gambar daun sehat. Hasilnya, CNN dapat mendeteksi penyakit daun kelapa sawit dengan akurasi yang dihasilkan mencapai 99%.

Background Subtraction Based on Pulse Coupled Neural Network

2014 ◽  
Vol 701-702 ◽  
pp. 293-296
Author(s):  
Xi Cai ◽  
Guang Han ◽  
Jin Kuan Wang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Background Subtraction ◽  
Dynamic Environments ◽  
Moving Object Detection ◽  
Moving Object ◽  
Human Visual Perception ◽  
Global Features ◽  
Pulse Coupled Neural Network ◽  
Background Models

Dynamic environments often bring great challenges to moving object detection. Solving this problem will promote expansion of application fields of moving object detection. Unlike those background subtraction methods using local feature-based background models, inspired by integrity of human visual perception, we present a background subtraction method for moving object detection in dynamic environments, building its background models based on global features extracted by pulse coupled neural network. We employ the pulse coupled neural network to take good advantage of their global coupling characters, in order to imitate the human biological visual activity. After sensing images via the pulse coupled neural network, we extract global information of the scenes and then build background models robust to background disturbances based on the global features. Experimental results prove that, our method behaves well in terms of visual and quantitative evaluations for dynamic environments.

scholarly journals Physiologically motivated image fusion for object detection using a pulse coupled neural network

1999 ◽  
Vol 10 (3) ◽  
pp. 554-563 ◽  
Author(s):  
R.P. Broussard ◽  
S.K. Rogers ◽  
M.E. Oxley ◽  
G.L. Tarr
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Image Fusion ◽  
Pulse Coupled Neural Network

scholarly journals Underwater Object Detection using Tensorflow

2020 ◽  
Vol 32 ◽  
pp. 03037
Author(s):  
Avinash Mahavarkar ◽  
Ritika Kadwadkar ◽  
Sneha Maurya ◽  
Smitha Raveendran
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Computer Vision Technology ◽  
Underwater Object ◽  
Suitable Environment

Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created so that Machine Learning algorithm will be used to train different images of the object. Open source Computer Vision has various functions which can be used for the image processing needs when an image is captured.

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