scholarly journals An Analysis on Object Recognition Using Convolutional Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 1928-1935
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Detection ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Critical Analysis ◽  
Data Volume ◽  
Fundamental Reason

The global development and progress in scientific paraphernalia and technology is the fundamental reason for the rapid increasein the data volume. Several significant techniques have been introducedfor image processing and object detection owing to this advancement. The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several remarkable breakthroughs were achieved. This paper comprehensively reviews the data classification, history as well as architecture of CNN and well-known techniques bytheir boons and absurdities. Finally, a discussion for implementation of CNN over object detection for effectual results based on their critical analysis and performances is presented

scholarly journals CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net

2020 ◽  
Vol 8 (6) ◽  
pp. 953-960 ◽  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Object Recognition ◽  
Speech Recognition ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Recognition Pattern

Convolutional Neural Networks(CNNs) are a floating area in Deep Learning. Now a days CNNs are used inside the more note worthy some portion of the Object Recognition tasks. It is used in stand-out utility regions like Speech Recognition, Pattern Acknowledgment, Computer Vision, Object Detection and extraordinary photograph handling programs. CNN orders the realities in light of an opportunity regard. Right now, inside and out assessment of CNN shape and projects are built up. A relative examine of different assortments of CNN are too portrayed on this work.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals Object Detectors’ Convolutional Neural Networks backbones : a review and a comparative study

2021 ◽  
Vol 9 (11) ◽  
pp. 1379-1386
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Crucial Role ◽  
Extended Version ◽  
Backbone Networks ◽  
Detection Algorithms ◽  
Wide Range

Computer vision is a scientific field that deals with how computers can acquire significant level comprehension from computerized images or videos. One of the keystones of computer vision is object detection that aims to identify relevant features from video or image to detect objects. Backbone is the first stage in object detection algorithms that play a crucial role in object detection. Object detectors are usually provided with backbone networks designed for image classification. Object detection performance is highly based on features extracted by backbones, for instance, by simply replacing a backbone with its extended version, a large accuracy metric grows up. Additionally, the backbone's importance is demonstrated by its efficiency in real-time object detection. In this paper, we aim to accumulate the crucial role of the deep learning era and convolutional neural networks in particular in object detection tasks. We have analyzed and have been concentrating on a wide range of reviews on convolutional neural networks used as the backbone of object detection models. Building, therefore, a review of backbones that help researchers and scientists to use it as a guideline for their works.

scholarly journals What You See Is What You Transform: Foveated Spatial Transformers as a bio-inspired attention mechanism

2021 ◽  
Author(s):  
Ghassan Dabane ◽  
Laurent Perrinet ◽  
Emmanuel Daucé
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Visual Space ◽  
Attention Mechanism ◽  
Classical Approach ◽  
Weak Point ◽  
Spatial Transformations ◽  
Training Scheme

Convolutional Neural Networks have been considered the go-to option for object recognition in computer vision for the last couple of years. However, their invariance to object’s translations is still deemed as a weak point and remains limited to small translations only via their max-pooling layers. One bio-inspired approach considers the What/Where pathway separation in Mammals to overcome this limitation. This approach works as a nature-inspired attention mechanism, another classical approach of which is Spatial Transformers. These allow an adaptive endto-end learning of different classes of spatial transformations throughout training. In this work, we overview Spatial Transformers as an attention-only mechanism and compare them with the What/Where model. We show that the use of attention restricted or “Foveated” Spatial Transformer Networks, coupled alongside a curriculum learning training scheme and an efficient log-polar visual space entry, provides better performance when compared to the What/Where model, all this without the need for any extra supervision whatsoever.

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