摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器学习的详细数据-术语识别和人工智能已经呈现。根据NewsRx编辑在英国索尔福德的新闻报道，“由于香料图像纹理模式之间的冲突，多种香料图像的自动分类是一个挑战，本文旨在开发一个自动分类系统，以便该系统能够选择合适的香料来制作马蹄草茶。”我们的新闻记者从S alford大学的研究中得到一句话：“这项工作考虑了以下七种香料，即肉桂、柑橘皮、丁香、姜、吉拉、可可、薄荷、薄荷、摘要:现有的香辛料制作系统大多需要人工干预,不同香辛料图像的纹理特征表示不同的纹理特征,这促使我们采用基于多sobel核的特征提取方法,以减少计算量。摘要:提出了一种基于高斯分布的角点检测新思想,对每个角点采用多sobel核提取特征,并将特征反馈到卷积神经网络层,对多幅spice图像进行分类。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning - Pa ttern Recognition and Artificial Intelligence have been presented. According to news reporting out of Salford, United Kingdom, by NewsRx editors, research state d, "Classification of multiple types of spice images is automatically challengin g due to conflict between the texture patterns of spice images. This work aims t o develop an automatic system for classifying different types of spice images so that the system can choose an appropriate spice to make herbal tea using Carall uma fimbriata." Our news journalists obtained a quote from the research from the University of S alford, "This work considers the following seven spices, namely, cinnamon, citru s peel, clove, ginger, jeera, kokum, mint, and Caralluma fimbriata as one more c lass for classification. Most of the existing systems need human intervention to choose different spices to make Caralluma fimbriata tea. It is observed that th e pattern of different spice images represents different textures. This observat ion motivated us to extract features based on multi-Sobel kernels. To reduce the number of computations, the proposed work introduces a novel idea of corner det ection based on Gaussian distribution. For each corner, the method performed is multi-Sobel kernels for extracting features. The features are fed to convolution al neural network layers for the classification of multiple spice images."
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