Pose recognition using convolutional neural networks on omni-directional images

Georgakopoulos, Spiros V.; Kottari, Konstantina; Delibasis, Konstantinos K.; Plagianakos, Vassilis P.

doi:10.1016/j.neucom.2017.08.071

Cited by 22 publications

(7 citation statements)

References 13 publications

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“…[71][72][73] Construction worker detection has been proposed for construction safety, 74,75 worker behavior analysis 76,77 and productivity analysis. 78,79 The results obtained have shown a 98% prediction accuracy for work zone events, thus using prevent variables can be a viable proposal to predict the occurrence of a safety-critical event using those models.…”

Section: Manufacturingmentioning

confidence: 91%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

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Section: Manufacturingmentioning

confidence: 91%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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“…LeNet5 gradually transforms an original image into a series of feature maps through alternately connected convolutional layers and down-sampling layers, conveying these features to a fully connected neural network in order to classify the images according to the features. After LeNet5, CNN entered the experimental development phase [16]. [19].…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Image Feature Recognition Algorithm With Hierarchical Attribute Constraints Based on Weak Supervision and Label Correlation

et al. 2020

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The ability to extract image features largely determines the accuracy of image classification. However, external interferences in images such as translation, rotation, scaling, occlusion, light, and nonlinear deformation, result in greater intra-class differences and inter-class similarity, which substantially increases the difficulty of image classification. Benefitting from the excellent ability of feature learning and extraction, Convolutional Neural Networks (CNNs) have achieved good results in the field of image classification. However, they are also characterized by a complex training process and high-demand parameter adjustment. This paper propose a convolutional neural network optimization method to optimize the model parameters with hierarchical attribute constraints and achieve intelligent recognition of specific image features. Based on the optimized model, we further construct weak supervision and label correlation optimization models and provide a visualization solution for feature recognition results. Experimental results demonstrated that the proposed algorithm can efficiently realize intelligent image feature recognition with high accuracy.

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“…The weights of the receptive fields during the training process will differ arbitrarily; a huge training set is required. During CNN preparation, proper weight values are sought without using the acquired knowledge of signal processing and analysis [19][20].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesions Classification Using Deep Learning Techniques: Review

Kareem

Abdulazeez

Zeebaree

2021

AJRCoS

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Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

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Pose recognition using convolutional neural networks on omni-directional images

Cited by 22 publications

References 13 publications

Deep learning support for intelligent transportation systems

Deep learning support for intelligent transportation systems

An Intelligent Image Feature Recognition Algorithm With Hierarchical Attribute Constraints Based on Weak Supervision and Label Correlation

Skin Lesions Classification Using Deep Learning Techniques: Review

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