scholarly journals HYBRID APPROACH OF GARBAGE CLASSIFICATION USING COMPUTER VISION AND DEEP LEARNING

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Anish Tatke ◽  
Madhura Patil ◽  
Anuj Khot ◽  
Parul Jadhav ◽  
Dr Vishwanath Karad
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Comparative Analysis ◽  
Deep Neural Networks ◽  
Hybrid Approach ◽  
Computer Architectures ◽  
Excellent Performance ◽  
Modern Computer ◽  
Use Of Technology ◽  
Waste Segregation

As waste segregation becomes an important issue in our lives, with the use of technology like deep neural networks and computer vision, we can make the process efficient and robust by image segmentation and classification. These systems on the rise need accurate and efficient segmentation and recognition mechanisms and this demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. This paper does a comparative analysis of various different approaches and methods like Simple CNN, ResNet50, VGG16, etc in brief. The comparative analysis and study explains the performance of every approach, this paper concludes that ResNet50 gives excellent performance. VGG16 network also provides good performance which meets the needs of daily use.

scholarly journals Analyzing and Visualizing Deep Neural Networks for Speech Recognition with Saliency-Adjusted Neuron Activation Profiles

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1350
Author(s):  
Andreas Krug ◽  
Maral Ebrahimzadeh ◽  
Jost Alemann ◽  
Jens Johannsmeier ◽  
Sebastian Stober
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Deep Neural Networks ◽  
Neuron Activation ◽  
Flexible Framework

Deep Learning-based Automatic Speech Recognition (ASR) models are very successful, but hard to interpret. To gain a better understanding of how Artificial Neural Networks (ANNs) accomplish their tasks, several introspection methods have been proposed. However, established introspection techniques are mostly designed for computer vision tasks and rely on the data being visually interpretable, which limits their usefulness for understanding speech recognition models. To overcome this limitation, we developed a novel neuroscience-inspired technique for visualizing and understanding ANNs, called Saliency-Adjusted Neuron Activation Profiles (SNAPs). SNAPs are a flexible framework to analyze and visualize Deep Neural Networks that does not depend on visually interpretable data. In this work, we demonstrate how to utilize SNAPs for understanding fully-convolutional ASR models. This includes visualizing acoustic concepts learned by the model and the comparative analysis of their representations in the model layers.

scholarly journals Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Rama K. Vasudevan ◽  
Maxim Ziatdinov ◽  
Lukas Vlcek ◽  
Sergei V. Kalinin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Bayesian Methods ◽  
Deep Neural Networks ◽  
Applied Research ◽  
Modern Science ◽  
Generative Models ◽  
Knowledge Development ◽  
Physical Constraints

AbstractDeep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

Survey on Energy-Efficient Deep Neural Networks for Computer Vision

2022 ◽  
pp. 25-52
Author(s):  
Abhinav Goel ◽  
Caleb Tung ◽  
Xiao Hu ◽  
Haobo Wang ◽  
Yung-Hsiang Lu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Energy Efficient ◽  
Deep Neural Networks

scholarly journals Technical note: AI-Track-tive: automated fission track recognition using computer vision (Artificial Intelligence)

2020 ◽  
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Neural Networks ◽  
Fission Track ◽  
Detection Algorithm ◽  
Technical Note ◽  
Success Rates ◽  
Automatic Counting ◽  
First Time

Abstract. Artificial intelligence techniques such as deep neural networks and computer vision are developed for fission track recognition and included in a computer program for the first time. These deep neural networks use the Yolov3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. These deep neural networks can be used in new software called AI-Track-tive. The developed program successfully finds most of the fission tracks in the microscope images, however, the user still needs to supervise the automatic counting. The success rates of the automatic recognition range from 70 % to 100 % depending on the areal track densities in apatite and (muscovite) external detector. The success rate generally decreases for images with high areal track densities, because overlapping tracks are less easily recognizable for computer vision techniques.

scholarly journals Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method

2018 ◽  
Author(s):  
Shuqin Gu ◽  
Yuexian Hou ◽  
Lipeng Zhang ◽  
Yazhou Zhang
Keyword(s):  
Neural Networks ◽  
Ensemble Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Experimental Results ◽  
Excellent Performance ◽  
Hidden Layer ◽  
Base Learner ◽  
Fully Connected

Although Deep Neural Networks (DNNs) have achieved excellent performance in many tasks, improving the generalization capacity of DNNs still remains a challenge. In this work, we propose a novel regularizer named Ensemble-based Decorrelation Method (EDM), which is motivated by the idea of the ensemble learning to improve generalization capacity of DNNs. EDM can be applied to hidden layers in fully connected neural networks or convolutional neural networks. We treat each hidden layer as an ensemble of several base learners through dividing all the hidden units into several non-overlap groups, and each group will be viewed as a base learner. EDM encourages DNNs to learn more diverse representations by minimizing the covariance between all base learners during the training step. Experimental results on MNIST and CIFAR datasets demonstrate that EDM can effectively reduce the overfitting and improve the generalization capacity of DNNs  

scholarly journals Avaliação do Processo para Embarcar uma Rede Neural Baseada em YOLO Utilizando um Acelerador de Hardware Dedicado

2020 ◽  
Author(s):  
Isabel Costa ◽  
Elias Silva Jr ◽  
Antônio Rodrigues ◽  
Leandro Angeloni ◽  
Edmilson Dias
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Power Distribution ◽  
Deep Neural Networks ◽  
Aerial Images ◽  
Great Progress ◽  
Operational Networks

Object Detection is a challenging task in computer vision, but Deep Neural Networks (DNN) have made great progress in this area. This work presents the process and the results obtained in the attempts to embed a YOLO V3 model in a Neural Compute Engine, the Movidius Stick. Experiments were carried out with a Tensorflow model that is converted to Movidius (using OpenVINO) including an evaluation of the Movidius stick connected to a Raspberry Pi3. The application uses aerial images of power distribution towers captured by a drone. Although there are some fully operational networks for Neural Compute Engines, there are some difficulties in porting new networks to the platform, with gains in performance, but with losses in accuracy.

scholarly journals Vision beyond Pixels: Visual Reasoning via Blocksworld Abstractions

2019 ◽  
Author(s):  
Tejas Gokhale
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Cognitive Psychology ◽  
Visual Perception ◽  
Deep Neural Networks ◽  
Natural Images ◽  
Modular Approach ◽  
Reasoning Task ◽  
Two Stages ◽  
Event Sequencing

Deep neural networks trained in an end-to-end fashion have brought about exceptional advances in computer vision, especially in computational perception. We go beyond perception and seek to enable vision modules to reason about perceived visual entities such as scenes, objects and actions. We introduce a challenging visual reasoning task, Image-Based Event Sequencing (IES) and compile the first IES dataset, Blocksworld Image Reasoning Dataset (BIRD). Motivated by the blocksworld concept, we propose a modular approach supported by literature in cognitive psychology and children's development. We decompose the problem into two stages - visual perception and event sequencing, and show that our approach can be extended to natural images without re-training.

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