CLASSIFICATION OF KITCHEN SEASONING WITH 360 DEGREE IMAGES (FISH EYE) USING TENSORFLOW
Abstract
Classification of objects is one of the studies that are currently being developed. One of the object classification techniques that are widely used is to implement deep learning methods. Convolutional Neural Network (CNN) is included in the type of Deep Learning because of the depth of the network. CNN is a convolution operation that combines several processing layers, using several elements that operate in parallel.
Often people make the mistake of distinguishing one object from another, such as kitchen spices. Similar colors and shapes can make an error in the taste of food, therefore this object classification takes kitchen spices as an example to recognize and classify kitchen spices objects in making it easier for humans to recognize objects. The objects used are Ginger, Candlenut, Salam Leaves, Coriander and Lemongrass which are used as ingredients for classifying kitchen spice objects based on shape. Classification of objects that are classified using the CNN and Tensorflow methods.
CNN construction will be built for object classification applications on 360° camera images (fish eye). An image in the form of a video of kitchen spices will be taken using a 360° camera and used as a model for classifying objects using Tensorflow and Jupyter Notebook for training data. The results of this detection system are expected to be able to work well in classifying objects in 360° image format that have significant distortion.
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