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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Color space selection for human skin detection using color-texture features and neural networks

Color space selection for human skin detection using color-t...

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International conference on Computer and Information Sciences (ICCOINS)

作者： Hani K. Al-Mohair Junita Mohamad-Saleh Shahrel Azmin Suandi School of Electrical & Electronic Engineering University Sains Malaysia Pulau Pinang Malaysia

Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper, a comprehensive comparative study using the Multilayer Perceptron artificial neural network (MLP), which is a universal classifier, is carried out to evaluate the overall performance of different color-spaces for skin detection. It aims at determining the most optimal color space using color and color-texture features separately. The study has been carried out using images of different databases. The experimental results showed that the YIQ color space gives the highest separability between skin and non-skin pixels among the different color spaces tested using color features. Combining color and texture eliminates the differences between color spaces but leads to much more accurate and efficient skin detection.

关键词： image color analysis Skin Databases Accuracy Training artificial neural networks Training data

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Survey of Literature on Machine Intelligence and Deep learning for Smart Grid applications

Survey of Literature on Machine Intelligence and Deep learni...

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International conference on Power Systems (ICPS)

作者： Vaibhav Garg Bajrang Agarwal Research and Development Genus Power Infrastructures Limited Jaipur India

ISBN: (数字)9781728141039

ISBN: (纸本)9781728141046

Deep neural networks (DNNs) are currently the state of the art for many modern artificial intelligence (AI) applications. You have surely been touched by one of its influences. Automatic tagging of contacts in Facebook, personal digital assistants (Siri/Cortana/Alexa/ Google Assistant), or automatic creation of albums by Google Photos; all use variants of a DNN. Since the breakthrough application of DNNs to image recognition, Natural Language processing, Machine Translation, Spam & Fraud detection and Speech Recognition, the number of applications that use DNNs has *** Grid is fundamentally defined as a system that has bi-directional information flow in addition to bi-directional flow of Energy. The Energy flow is bidirectional as there is a mechanism through which distributed generation and distributed storage can be integrated with the grid. The Information flow is bidirectional as the information flows from the devices to the central server and control signals flow from the server to the smart *** paper seeks to explore the modern Deep Leaning (DL) and Deep neural networks (DNN) landscape, and point to some of the possible directions in the space of Smart metering and Smart Grid.

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A Proposed Heterogeneous Convolutional neural Network for Embedded Automotive Vision Algorithms

A Proposed Heterogeneous Convolutional Neural Network for Em...

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IEEE International conference on Electro Information Technology

作者： Iyad Mansour Robert Hoffman Osamah Rawashdeh Bodo Seifert Dura Automotive Systems Auburn Hills MI Electrical and Computer Engineering Dept. Oakland University Rochester MI

The computer vision industry is relying more and more on artificial intelligence to process image data generated by cameras, LIDAR and RADAR applications. In the past years, neural networks (NN) have been specifically successful to process image data and to perform classification tasks that now exceed human classification capabilities. Traditional CNN based algorithms require significant use of central processing unit (CPU) cycles and/or graphics processing units (GPU). The majority of these models are built to operate on laptops or server like solutions that are not limited by the amount of CPU/GPU available for processing. In the transportation industry it is necessary to operate in an environment that is limited in processing power, current consumption, and thermal output and for that reason server-based AI solutions are not always practical in a land, sea or aerial vehicles. This publication will overview automotive computer vision (CV) perception algorithms for a variety of CNN models and offer a novel solution for heterogeneous CNN, H-CNN, which is optimized for automotive grade embedded System on Chips SoC's that limit the computational, current consumption and thermal output of the system.

关键词： H-CNN LIDAR RADAR

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Gender Recognition Based on Face image Using Deep Learning Method

Gender Recognition Based on Face Image Using Deep Learning M...

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International conference on Cyber and IT Service Management (CITSM)

作者： Mochamad Wahyudi Waeisul Bismi Firmansyah Mugi Raharjo Untung Rahardja Lise Pujiastuti Departement of Computer Science Universitas Bina Sarana Informatika Jakarta Indonesia Departement of Informatics University Nusa Mandiri Jakarta Indonesia Departement of Digital Business University Raharja Tangerang Indonesia Departement of Information System STMIK Antar Bangsa Tangerang Indonesia

Gender recognition based on facial images is one of the interesting applications in the field of image processing and artificial intelligence. Deep Learning methods, particularly artificial neural networks, have emerged as an effective tool for extracting complex facial features and classifying gender with high accuracy. This research outlines a study that aims to develop an employee gender recognition system based on facial images using Deep Learning methods. The method used in this study proposes the inceptionV3 model as well as the OpenCV and matplotlib libraries in Python in involving the collection of facial image datasets covering different genders with a total of 1024 facial images, each with 40 attributes. This dataset was used to train a deep artificial neural network to recognize patterns and features that are unique to male and female genders and validated. Furthermore, the artificial neural network was tested with a dataset of never-before-seen facial images to evaluate the performance of the system. The results show that the InceptionV3 Deep Learning model has great potential in gender recognition based on facial images. The developed system was able to achieve a high level of accuracy in classifying the gender of individuals from facial images with a total accuracy of 94.8% against the test data, even in complex situations such as variations in facial expressions and lighting.

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Effective Brain Tumor Detection using Convolutional neural networks

Effective Brain Tumor Detection using Convolutional Neural N...

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International conference on Computing for Sustainable Global Development (INDIACom)

作者： Aman Narang Aman Singh Rajpoot Ramkumar Jayaraman Department of Computing Technologies SRM Institute of Science and Technology Chennai India

In the recent scenario, the move from machine learning to deep learning has played a significant role in various applications such as, healthcare, automation, robotics, etc. Considerign the healthcare, diagnosing diseases, like brain tumor, brest cancer, etc. using Magnetic Resonance Imaging (MRI) images, is a complex process because the images contain various cell/image pixel divisions to identify the condition of the disease. Based on the complexity of the disease, identifying and diagnosing the tumour becomes a challenging task as it may lead to an increase in the medical survey on tumour disease based on the number of patients. In this paper, a Deep Learning (DL) - Convolutional neural networks (CNN) based approach is proposed with the integration of data augmentation and image processing. The proposed approach provides classification of MRI images to diagnose the tumour location on the dense areas based on small tumour cells. The effectiveness of the proposed approach is analyzed based on the data accuracy as it achieves 96% with low complexity rate compared to the existing models such as, CNN and artificial neural networks (ANN) models.

关键词： Training Deep learning Magnetic resonance imaging Computational modeling Medical services Brain modeling Data models

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30.2 A 22nm 0.26nW/Synapse Spike-Driven Spiking neural Network processing Unit Using Time-Step-First Dataflow and Sparsity-Adaptive In-Memory Computing

30.2 A 22nm 0.26nW/Synapse Spike-Driven Spiking Neural Netwo...

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2024 IEEE International Solid-State Circuits conference, ISSCC 2024

作者： Liu, Ying Ma, Yufei Shang, Ninghui Zhao, Tianhao Chen, Peiyu Wu, Meng Ru, Jiayoon Jia, Tianyu Ye, Le Wang, Zhixuan Huang, Ru Peking University Beijing China Nano Core Chip Electronic Technology Hangzhou China Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350306200

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated tremendous improvement in energy efficiency (EE) and low power by exploiting highly sparse spikes and event-driven design [1-2]. (top of Fig. 30.2.1) With the same spike-based information carrier, the combination of an SNN and dynamic vision sensor (DVS) [3] offers a promising solution for edge AI applications. Typically, the greater the number of synapses in an SNN can improve inference accuracy, but induce more significant power consumption and memory size. Therefore, deploying more synapses in an SNN chip while maintaining low power, memory size and high EE requires co-design of algorithm, architecture and circuits. At the algorithm level, SNN training based on conversion from an artificial NN (ANN) [1] achieves the best accuracy in static image classification, but at the cost of worse EE and higher power consumption, as it requires excessive time steps and energy arising from ANN-SNN conversion. At the architecture level, the multiple-time-step forward computation in SNNs introduces redundant data movement and storage, including input-output spikes, weights, partial sums and membrane potentials, restricting the network scale that can be accommodated by limited on-chip memory, i.e., network density (ND) [4-6]. At the circuit level, in-memory computing (IMC) is attractive to minimize data movement. However, traditional frame-based IMC circuits are incompatible with the highly sparse and fine-grained spike-driven nature of SNNs. Recent advancements in neuromorphic IMC [7] employ integrated-fire (IF) converters, which are globally triggered by power-hungry clocks even if there is no spike, resulting in high power per synapse, i.e., power density (PD). © 2024 IEEE.

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Comprehensive analysis of convolutional neural networks applied to CIFAR-10 dataset

Comprehensive analysis of convolutional neural networks appl...

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2nd International conference on Mechatronic Automation and Electrical Engineering (ICMAEE 2024)

作者： Biao Su College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 People's Republic of China

ISBN: (数字)9781837242672

This article explores the advancements and applications of Convolutional neural networks (CNNs) in image classification, focusing on the CIFAR-10 dataset. Since their inception in 2006, CNNs have revolutionized computer vision by efficiently extracting high-level features from raw images. This study discusses the structure of CNNs, including convolutional layers, pooling, and fully connected layers, which enhance feature extraction and classification accuracy. Experimentally, CNNs have demonstrated superior performance on tasks requiring the identification of complex patterns such as in autonomous driving, security, and medical imaging. The research further investigates the optimization of CNN models through architectural enhancements, attention mechanisms, data augmentation, model compression, and cross-domain knowledge transfer. This study concludes that multicore configurations with batch processing significantly outperform single-core setups, achieving lower latency and higher throughput, thus underscoring the potent applications and continuous evolution of deep learning in modern artificial intelligence challenges.

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A pipeline for detecting and classifying objects in images

A pipeline for detecting and classifying objects in images

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IEEE International conference on Data Stream Mining & processing (DSMP)

作者： Marek Vajgl Petr Hurtik Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations University of Ostrava Ostrava Czech Republic

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132150

With the increased accessibility to neural network frameworks and computation clouds, a wide range of competition websites offer real tasks for the neural network community to join. In this paper, we discuss a problem of object detection and classification. Based on our experience, we describe a general pipeline and necessary steps that may help researchers willing to participate in such competition. We partition the problem into two separate tasks. Firstly, we present state of the art for relevant neural networks concerning accuracy and computation time trade-off. Further, we create a survey of major techniques that leads to accuracy improvement. Namely, we recall image augmentation techniques, demonstrate the impact of various optimizers, and discuss ensemble techniques. The pipeline and techniques reflect our experience with a competition, in which we were able to reach a highly competitive solution and ended in fourth place. The uniqueness of our solution is that we used only free Google Colab computation service and still overperformed many more computation extensive approaches.

关键词： Training Task analysis artificial neural networks Agriculture Pipelines Object detection

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Performance analysis of stacked generalization classifiers

Performance analysis of stacked generalization classifiers

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IEEE Signal processing and Communications applications (SIU)

作者： Mete Ozay Fatos Tunay Yarman Vural Enformatik Enstitüsü Orta Doğu Teknik Üniversitesi Turkey Bilgisayar Mühendisliği Bölümü Orta Doğu Teknik Üniversitesi Turkey

Stacked generalization is a classification technique which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique, performs better than other classification schemas under some circumstances. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. Even though it is used in several application domains up to now, it is not clear under which circumstances stacked generalization technique increases the performance. In this work, the states of the performance of stacked generalization technique is analyzed in terms of the performance parameters of the individual classifiers under the architecture. This work shows that the individual classifiers should learn the training set sharing the members of the set among themselves for the success of the stacked generalization architecture.

关键词： Art artificial neural networks conferences Reactive power Information processing Estimation image retrieval

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Ensemble learning-based approach for crack detection using CNN

Ensemble learning-based approach for crack detection using C...

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International conference on Trends in Electronics and Informatics (ICEI)

作者： Vibhu Kailkhura S. Aravindh Shashank Shekhar Jha N. Jayanthi Delhi Technological University

ISBN: (数字)9781728155180

ISBN: (纸本)9781728155197

Crack detection is of pivotal importance in civil engineering and other related applications. Traditional methods of human inspection are tedious and severely limited. Automated crack detection by conventional image processing techniques is challenging due to their inability to discriminate crack features from background noise. Inhomogeneous lighting, shadows, and surface finish hinder the performance of digital image processing methods. The use of convolutional neural networks has helped achieve remarkably better results in the field of computer vision. Ensemble learning is an approach to aggregate the results of a number of individual models for classification or regression. Ensemble learning for crack detection has been implemented using deep convolutional neural networks (DCNN) in this paper. The models are evaluated on a number of performance metrics, namely-(i) accuracy, (ii) precision, (iii) recall (iv) Matthews correlation coefficient (MCC), (v) AUROC, and (vi) F1 score. Experimental results show the robustness of the ensembling method and offer promising scope in crack detection. They outperform the current best performance on open source concrete crack dataset. The ensemble models achieved much better performance than their individual counterparts with the best ensemble achieving a validation accuracy of 99.67%.

关键词： Feature extraction Convolutional neural networks Predictive models Analytical models Surface cracks image segmentation

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