Classification of Dry Beans Using Image Features

2021 ◽  
Author(s):  
Sudarshan S. Chawathe
Keyword(s):  
Image Features ◽  
Dry Beans

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

Seed Architectural Phenes Prediction and Variety Classification of Dry Beans (Phaseolus vulgaris) Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Christan Hail Mendigoria ◽  
Ronnie Concepcion ◽  
Elmer Dadios ◽  
Heinrick Aquino ◽  
Oliver John Alaias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phaseolus Vulgaris ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Dry Beans ◽  
Variety Classification

scholarly journals Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1502
Author(s):  
Ben Wilkes ◽  
Igor Vatolkin ◽  
Heinrich Müller
Keyword(s):  
Visual Analysis ◽  
Binary Classification ◽  
Statistical Significance ◽  
Image Features ◽  
Support Vector ◽  
Audio Signals ◽  
Text And Image ◽  
The Impact ◽  
Music Track

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.

Cell Phone Image-Based Plant Disease Classification

2015 ◽  
pp. 295-322
Author(s):  
Marion Neumann ◽  
Lisa Hallau ◽  
Benjamin Klatt ◽  
Kristian Kersting ◽  
Christian Bauckhage
Keyword(s):  
Cell Phone ◽  
Plant Disease ◽  
Image Features ◽  
Disease Classification ◽  
Sensor Technology ◽  
Agricultural Crop ◽  
Leaf Spots ◽  
Crop Varieties ◽  
Control Growth

Modern communication and sensor technology coupled with powerful pattern recognition algorithms for information extraction and classification allow the development and use of integrated systems to tackle environmental problems. This integration is particularly promising for applications in crop farming, where such systems can help to control growth and improve yields while harmful environmental impacts are minimized. Thus, the vision of sustainable agriculture for anybody, anytime, and anywhere in the world can be put into reach. This chapter reviews and presents approaches to plant disease classification based on cell phone images, a novel way to supply farmers with personalized information and processing recommendations in real time. Several statistical image features and a novel scheme of measuring local textures of leaf spots are introduced. The classification of disease symptoms caused by various fungi or bacteria are evaluated for two important agricultural crop varieties, wheat and sugar beet.

A SYNERGISTIC MODEL FOR REPRESENTING AND INTERPRETING HUMAN ACTIVITIES AND EVENTS FROM VIDEO

2003 ◽  
Vol 12 (01) ◽  
pp. 101-116 ◽  
Author(s):  
NIKOLAOS BOURBAKIS ◽  
JIM R. GATTIKER ◽  
GEORGE BEBIS
Keyword(s):  
Law Enforcement ◽  
Image Features ◽  
Automated System ◽  
Adaptive Model ◽  
Functional Behavior ◽  
Structural Image ◽  
Functional Knowledge ◽  
The Government ◽  
Free Environment

This paper describes a new and innovative and approach for representing, recognizing and interpreting human activity from video, contributing to an automated system capable of recognition of complex human behaviors. This technology is directly applicable for monitoring public safety and law enforcement, and capturing of activities is crucial for supporting virtual collaborations between citizens, and between citizens and government. Digital video stores of terabytes are now common, and will continue to increase until they dominate stored data. The government of the future will have to manage, organize, recall, and interpret information from this resource, This paper addresses one important facet of this. The approach presented here is a model based on the hierarchical synergy of three other models: the Local/Global (L-G) graph, the Stochastic Petri Net (SPN) graph and a neural network (NN) model. The application focus is the description of activity of actors in a video (or multi-sensor) scene, from the snapshot state description through higher levels of organization into events. The concept of importance is the distinction and interaction between structural knowledge, or knowledge about physical state, and functional knowledge, knowledge about change and events. The L-G graph provides a powerful description of the structural image features presented in an event, and the SPN model offers a description of the functional behavior. The NN (or other adaptive) model provides the capability of leaning behavioral patterns for classification of posture and activity, and forecasting possible events in a free environment.

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