AIMC Topic: Color

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Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.

Scientific reports
Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. S...

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

The British journal of ophthalmology
AIM: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.

Communications biology
Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from...

Towards real-time photorealistic 3D holography with deep neural networks.

Nature
The ability to present three-dimensional (3D) scenes with continuous depth sensation has a profound impact on virtual and augmented reality, human-computer interaction, education and training. Computer-generated holography (CGH) enables high-spatio-a...

Color for object recognition: Hue and chroma sensitivity in the deep features of convolutional neural networks.

Vision research
In this work, we examined the color tuning of units in the hidden layers of AlexNet, VGG-16 and VGG-19 convolutional neural networks and their relevance for the successful recognition of an object. We first selected the patches for which the units ar...

HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints.

Sensors (Basel, Switzerland)
With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images ...

SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis.

Neural networks : the official journal of the International Neural Network Society
Synthesizing photo-realistic images based on text descriptions is a challenging task in the field of computer vision. Although generative adversarial networks have made significant breakthroughs in this task, they still face huge challenges in genera...

Deep learning-based detection of dental prostheses and restorations.

Scientific reports
The purpose of this study is to develop a method for recognizing dental prostheses and restorations of teeth using a deep learning. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in t...

The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms.

Evolution; international journal of organic evolution
Evolutionary biologists frequently wish to measure the fitness of alternative phenotypes using behavioral experiments. However, many phenotypes are complex. One example is coloration: camouflage aims to make detection harder, while conspicuous signal...

Application of artificial neural networks to predict multiple quality of dry-cured ham based on protein degradation.

Food chemistry
This study investigated protein degradation and quality changes during the processing of dry-cured ham, and then established the multiple quality prediction model based on protein degradation. From the raw material to the curing period, proteolysis i...