scholarly journals Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

2020 ◽  
Author(s):  
Sagi Eppel ◽  
Haoping Xu ◽  
Mor Bismuth ◽  
Alan Aspuru-Guzik
Keyword(s):  
Computer Vision ◽  
Visual Recognition ◽  
Model Development ◽  
Machine Learning Approach ◽  
Multiphase Systems ◽  
Solid Foam ◽  
Main Activity ◽  
Fill Level ◽  
The Individual ◽  
Modern Machine

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

scholarly journals Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

2020 ◽  
Author(s):  
Sagi Eppel ◽  
Haoping Xu ◽  
Mor Bismuth ◽  
Alan Aspuru-Guzik
Keyword(s):  
Computer Vision ◽  
Visual Recognition ◽  
Model Development ◽  
Machine Learning Approach ◽  
Multiphase Systems ◽  
Solid Foam ◽  
Main Activity ◽  
Fill Level ◽  
The Individual ◽  
Modern Machine

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

scholarly journals Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

2020 ◽  
Author(s):  
Sagi Eppel ◽  
Haoping Xu ◽  
Mor Bismuth ◽  
Alan Aspuru-Guzik
Keyword(s):  
Computer Vision ◽  
Visual Recognition ◽  
Model Development ◽  
Machine Learning Approach ◽  
Multiphase Systems ◽  
Solid Foam ◽  
Main Activity ◽  
Fill Level ◽  
The Individual ◽  
Modern Machine

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

scholarly journals Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

2020 ◽  
Author(s):  
Sagi Eppel ◽  
Haoping Xu ◽  
Mor Bismuth ◽  
Alan Aspuru-Guzik
Keyword(s):  
Computer Vision ◽  
Visual Recognition ◽  
Model Development ◽  
Machine Learning Approach ◽  
Multiphase Systems ◽  
Solid Foam ◽  
Main Activity ◽  
Fill Level ◽  
The Individual ◽  
Modern Machine

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

scholarly journals A Machine Learning Approach for Visual Recognition of Complex Parts in Robotic Manipulation

2017 ◽  
Vol 11 ◽  
pp. 423-430 ◽  
Author(s):  
P. Aivaliotis ◽  
A. Zampetis ◽  
G. Michalos ◽  
S. Makris
Keyword(s):  
Machine Learning ◽  
Visual Recognition ◽  
Robotic Manipulation ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Complex Parts

scholarly journals Basketball shooting technology based on acceleration sensor fusion motion capture technology

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Binbin Zhao ◽  
Shihong Liu
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Motion Capture ◽  
Visual Recognition ◽  
Gaussian Mixture ◽  
Human Vision ◽  
Support Vector ◽  
Background Removal ◽  
Graphics Processing ◽  
Basketball Shooting

AbstractComputer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.

Identification and Classification of Fungal Colonies in Moldy Paddy Based on Computer Vision

2018 ◽  
Vol 61 (5) ◽  
pp. 1497-1504
Author(s):  
Zhenjie Wang ◽  
Ke Sun ◽  
Lihui Du ◽  
Jian Yuan ◽  
Kang Tu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Discriminant Analysis ◽  
Texture Features ◽  
Fungal Species ◽  
Projection Algorithm ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sequential Selection ◽  
The Individual

Abstract. In this study, computer vision was used for the identification and classification of fungi on moldy paddy. To develop a rapid and efficient method for the classification of common fungal species found in stored paddy, computer vision was used to acquire images of individual colonies of growing fungi for three consecutive days. After image processing, the color, shape, and texture features were acquired and used in a subsequent discriminant analysis. Both linear (i.e., linear discriminant analysis and partial least squares discriminant analysis) and nonlinear (i.e., random forest and support vector machine [SVM]) pattern recognition models were employed for the classification of fungal colonies, and the results were compared. The results indicate that when using all of the features for three consecutive days, the performance of the nonlinear tools was superior to that of the linear tools, especially in the case of the SVM models, which achieved an accuracy of 100% on the calibration sets and an accuracy of 93.2% to 97.6% on the prediction sets. After sequential selection of projection algorithm, ten common features were selected for building the classification models. The results showed that the SVM model achieved an overall accuracy of 95.6%, 98.3%, and 99.0% on the prediction sets on days 2, 3, and 4, respectively. This work demonstrated that computer vision with several features is suitable for the identification and classification of fungi on moldy paddy based on the form of the individual colonies at an early growth stage during paddy storage. Keywords: Classification, Computer vision, Fungal colony, Feature selection, SVM.

scholarly journals Web Application for Emotion-based Music Player

2021 ◽  
Vol 9 (12) ◽  
pp. 11-14
Author(s):  
Kamal Naina Soni
Keyword(s):  
Computer Vision ◽  
Human Brain ◽  
Facial Expressions ◽  
Web Application ◽  
Emotional State ◽  
Current State ◽  
The Face ◽  
Current Mood ◽  
The Individual ◽  
Music Player

Abstract: Human expressions play an important role in the extraction of an individual's emotional state. It helps in determining the current state and mood of an individual, extracting and understanding the emotion that an individual has based on various features of the face such as eyes, cheeks, forehead, or even through the curve of the smile. A survey confirmed that people use Music as a form of expression. They often relate to a particular piece of music according to their emotions. Considering these aspects of how music impacts a part of the human brain and body, our project will deal with extracting the user’s facial expressions and features to determine the current mood of the user. Once the emotion is detected, a playlist of songs suitable to the mood of the user will be presented to the user. This can be a big help to alleviate the mood or simply calm the individual and can also get quicker song according to the mood, saving time from looking up different songs and parallel developing a software that can be used anywhere with the help of providing the functionality of playing music according to the emotion detected. Keywords: Music, Emotion recognition, Categorization, Recommendations, Computer vision, Camera

Career patterns of marketing managers in top European football clubs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karolina Nessel
Keyword(s):  
Sports Management ◽  
Career Patterns ◽  
Content Type ◽  
Marketing Managers ◽  
Football Clubs ◽  
European Football ◽  
Main Activity ◽  
The Individual ◽  
Functional Experience ◽  
Football Industry

PurposeThe goal of this research was to explore career patterns of senior marketing managers in the best European football clubs (SMMEFCs).Design/methodology/approachThe data came from the LinkedIn profiles of current and past SMMEFCs. Firstly, the optimal matching algorithm was used to determine clusters of pathways leading to a first SMMEFC position based on the main activity of the employing organisation. Secondly, these patterns were compared in terms of variables depicting the career paths, clubs and managers. Finally, the evolution of the post-SMMEFC careers was analysed.FindingsPeople in their first SMMEFC positions are mainly male with a university degree in business and marketing, and with a predominantly functional experience in marketing. There are five ways to become an SMMEFC: through business (40% of the sample), football (32%), other sports (11%), marketing and communication (11%), and media (6%). As the majority of SMMEFCs come to their positions from outside the sporting world, the specificity of the football industry is not a serious obstacle. Instead, the careers are bounded by functional marketing experience. Among the individual sequences leading to a first SMMEFC position, only around half of the football cluster may be considered traditional careers. Football, and sports in general, seem attractive for post-SMMEFC career development for the majority of managers coming from all pathways.Originality/valueThe study is the first one to quantify career patterns in professional sports management. It provides new insights about marketing careers and practice in European club football.

Machine Learning Applications in Computer Vision

2013 ◽  
pp. 896-926
Author(s):  
Mehrtash Harandi ◽  
Javid Taheri ◽  
Brian C. Lovell
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Visual Recognition ◽  
Sequential Decision ◽  
Biologically Inspired ◽  
Transductive Learning ◽  
Machine Learning Applications ◽  
Appearance Models ◽  
Learning Frameworks

Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed; the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.

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