Graph Neural Network (GNN) in Image and Video Understanding Using Deep Learning for Computer Vision Applications

2021 ◽  
Author(s):  
P Pradhyumna ◽  
G P Shreya ◽  
Mohana
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Video Understanding ◽  
Computer Vision Applications

scholarly journals Computer vision based obstacle detection and target tracking for autonomous vehicles

2021 ◽  
Vol 336 ◽  
pp. 07004
Author(s):  
Ruoyu Fang ◽  
Cheng Cai
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Target Tracking ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Obstacle Detection ◽  
Pid Algorithm ◽  
Deep Learning Neural Network

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

scholarly journals Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

2021 ◽  
Vol 2021 (1) ◽  
pp. 43-48
Author(s):  
Mekides Assefa Abebe
Keyword(s):  
Computer Vision ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Tone Mapping ◽  
Objective Functions ◽  
Related Research ◽  
Adversarial Networks ◽  
Computer Vision Applications ◽  
Research Domains

Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance. The quality degradations further hiders the performances of imaging and computer vision applications. Therefore, the reconstruction and enhancement of uderand over-exposed images is essential for various applications. Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years. Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy. The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details. However, the design of most DL architectures and objective functions don’t take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent. Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions. For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario. Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.

scholarly journals SMART CANTEEN : PERILAKU MENGATUR POLA MAKAN DENGAN MEMBACA NILAI NUTRISI (CONVENTIONAL DEEP LEARNING NEURAL NETWORK)

2020 ◽  
Vol 6 (2) ◽  
pp. 115-121
Author(s):  
Ari Purno Wahyu ◽  
Heri Heryono ◽  
Muhammad Benny Chaniago ◽  
Dani Hamdani
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Deep Learning Neural Network

Kesehatan merupakan bagian terpenting bagi kita dimana pengaruh atau datangnya penyakit melalui pola makan, terlebih bagi kita yang memiliki kesibukan yang luar biasa padatnya tentu saja tidak ada waktu untuk sarapan dan lebih memilih makanan cepat saji yang tersedia banyak di kantin atau kafe. Hal ini bukan berarti makanan cepat saji tidak sehat, hal ini akan menjadi masalah jika terlalu berlebih dan tidak memperhatikan takaran saji atau kandungan nutrisi yang ada pada makanan tersebut. Beberapa cara bisa dilakukan dengan menjaga sikap  pola makan misalkan dengan diet atau menggunakan aplikasi perhitungan nutrisi yang ada di pasaran dan gratis untuk diunduh. Jenis aplikasi ini masih kurang efektif dimana aplikasi tersebut masih merupakan perkiraan saja dan tidak bisa digunakan secara realtime. Penelitian sebelumnya bisa menggunakan teknik computer vision dengan menggunakan image sebagai alat pembaca dari makanan yang akan kita santap. Aplikasi tersebut mampu membaca kandungan nutrisi sekaligus  harga makanan, teknik pengolah image yang digunakan menggunakan metode Deep Learning Neural Network, algoritma ini terbukti memiliki akurasi dan pembacaan data yang tinggi dibandingkan algoritma yang lain. Aplikasi dengan Neural Network yang berbasis image bisa diimplementasikan pada mesin kasir di kantin atau cafe dan bisa dibuat dalam bentuk perangkat mobile sehingga lebih mudah digunakan. Teknik komputerisasi dengan Deep Learning Neural Network terbukti bisa diterapkan di kantin dan caf

scholarly journals Hyperspectral imaging and artificial intelligence to detect oral malignancy – part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Daniel G. E. Thiem ◽  
Paul Römer ◽  
Matthias Gielisch ◽  
Bilal Al-Nawas ◽  
Martin Schlüter ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Oral Mucosa ◽  
Deep Neural Network ◽  
Light Weight ◽  
Tissue Classification ◽  
Raw Data

Abstract Background Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library. Methods A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN). Results The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each. Conclusion Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 1573-1582
Author(s):  
S Gopi Naik
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Optimization Algorithms ◽  
Object Identification ◽  
Integrated System ◽  
Raspberry Pi ◽  
High Level

Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.

Foreground Detection by Competitive Learning for Varying Input Distributions

2018 ◽  
Vol 28 (05) ◽  
pp. 1750056 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
Miguel A. Molina-Cabello ◽  
Rafael Marcos Luque-Baena ◽  
Enrique Domínguez
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Input Data ◽  
State Of The Art ◽  
Background Modeling ◽  
Cluster Detection ◽  
Foreground Detection ◽  
Learning Mechanism ◽  
Input Distribution ◽  
Computer Vision Applications

One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.

scholarly journals PERANCANGAN APLIKASI MOBILE UNTUK KLASIFIKASI SAYURAN MENGGUNAKAN DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 300-306
Author(s):  
Muhamad Jaelani Akbar ◽  
Mochamad Wisuda Sardjono ◽  
Margi Cahyanti ◽  
Ericks Rachmat Swedia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Test Accuracy ◽  
Final Test ◽  
Testing Data

Sayuran merupakan sebutan bagi bahan pangan asal tumbuhan yang biasanya mengandung kadar air tinggi dan dikonsumsi dalam keadaan segar atau setelah diolah secara minimal. Keanekaragaman sayur yang terdapat di dunia menyebabkan keragaman pula dalam pengklasifikasian sayur. Oleh karena itu diperlukan adanya pendekatan digital agar dapat mengenali jenis sayuran dengan cepat dan mudah. Dalam penelitian ini jumlah jenis sayuran yang digunakan sebanyak 7 jenis diantara: brokoli, jagung, kacang panjang, pare, terung ungu, tomat dan kubis. Dataset yang digunakan berjumlah 941 gambar sayur dari 7 jenis sayur, ditambah 131 gambar sayur dari jenis yang tidak terdapat pada dataset, selain itu digunakan 291 gambar selain sayuran. Untuk melakukan klasifikasi jenis sayuran digunakan algoritme Convolutional Neural Network (CNN), yang merupakan salah satu bidang ilmu baru dalam Machine Learning dan berkembang dengan pesat. CNN merupakan salah satu algoritme yang terdapat pada metode Deep Learning dengan memiliki kemampuan yang baik dalam Computer Vision, salah satunya yaitu image classification atau klasifikasi objek citra. Uji coba dilakukan pada lima perangkat selular berbasiskan sistem operasi Android. Python digunakan sebagai bahasa pemrograman dalam merancang aplikasi mobile ini dengan menggunakan modul Tensor flow untuk melakukan training dan testing data. Metode yang dapat digunakan dalam melakukan klasifikasi citra ini yaitu Convolutional Neural Network (CNN). Hasil final test accuracy yang diperoleh yaitu didapat keakuratan mengenali jenis sayuran sebesar 98.1% dengan salah satu hasil pengujian yaitu klasifikasi sayur jagung dengan akurasi sebesar 99.98049%.

scholarly journals Image Captioning menurut Scientific Revolution Kuhn dan Popper

2020 ◽  
Vol 10 (2) ◽  
pp. 110-121
Author(s):  
Agus Nursikuwagus ◽  
Rinaldi Munir ◽  
Masayu Layla Khodra
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Natural Language ◽  
Scientific Revolution ◽  
Image Captioning ◽  
Image Generator

Perkembangan untuk memberikan caption pada suatu gambar merupakan suatu ranah perkembangan baru dalam bidang intelejensia buatan.  Image captioning merupakan penggabungan dari beberapa bidang seperti computer vision, natural language, dan pembelajaran mesin. Aspek yang menjadi perhatian dalam bidang image captioning ini adalah ketepatan arsitektur neural network yang dimodelkan untuk mendapatkan hasil yang sedekat mungkin dengan ground-thruth yang disampaikan oleh person. Beberapa kajian yang sudah diteliti masih mendapatkan kalimat yang masih jauh dari ground-thruth tersebut. Permasalahan yang dibahas pada umumnya mengenai image captioning adalah image generator dan text generator yaitu penggunaan deep learning seperti CNN dan LSTM untuk menyelesaikan masalah captioning. Hal ini menjadi dasar permasalahan untuk memberikan kontribusi baru dalam bidang image captioning yang meliputi image extractor, text generator, dan evaluator yang bisa digunakan pada model yang diusulkan. Perspektif Kuhn dan Popper dalam hal image captioning, diperoleh bahwa caption dalam bidang geologi sangat diperlukan dan mencapai tahap krisis. Perlu adanya metode usulan baru untuk menyajikan caption untuk citra geologi.

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