Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network

This paper proposes a solution to localization and classification of rice grains in an image.All existing related works rely on conventional based machine learning approaches.However,those techniques do not do well for the problem designed in this paper,due to the high similarities between different types of rice grains.The deep learning based solution is developed in the proposed solution.It contains pre-processing steps of data annotation using the watershed algorithm,auto-alignment using the major axis orientation,and image enhancement using the contrast-limited adaptive histogram equalization(CLAHE)technique.Then,the mask region-based convolutional neural networks(R-CNN)is trained to localize and classify rice grains in an input image.The performance is enhanced by using the transfer learning and the dropout regularization for overfitting prevention.The proposed method is validated using many scenarios of experiments,reported in the forms of mean average precision(mAP)and a confusion matrix.It achieves above 80%mAP for main scenarios in the experiments.It is also shown to perform outstanding,when compared to human experts.

Knowing Your Dog Breed:Identifying a Dog Breed with Deep Learning

Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural instinct.This paper presents a solution for identifying dog breeds using their images of their faces.The proposed method applies a deep learning based approach in order to recognize their breeds.The method begins with a transfer learning by retraining existing pretrained convolutional neural networks(CNNs)on the public dog breed dataset.Then,the image augmentation with various settings is also applied on the training dataset,in order to improve the classification performance.The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons.The proposed model achieves a promising accuracy of 89.92%on the published dataset with 133 dog breeds.