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Hi i want to predict health level(High,medium,low) in leaf using image processing and data mining.So far i thought using extract colors from leaf using Bayes algorithm to predict healthy of leaf. and data mining part have completed now.but i need extra features for prediction.we only used orchid leaf.So i can't use vain structure.Can anyone help me to what are the other features can be extracted from leaf for identify health level of leaf.Any idea or comments help me to improve my project. Thanks
There are many possible approaches to a problem like this. One common method is the bag-of-features model. Take a look at this example using the Computer Vision System Toolbox in MATLAB.
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I've been working on this project for a bit and would like to know which approach is the best out of the two that I have come up with. My project is about the weight of a potato.
Keep in mind it takes a lot of time and resources to record data.
Approach One
use a computer vision algorithm to find the height of an object. And since that height and weight have a linear relationship with a potato. We can use a linear regression model to figure out the weight of the object
Approach Two
collect a dataset of images of potatoes with their corresponding weights and preprocess the data using YOLO. Through this we can get the weight of the object.
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I am looking for the machine learning correct approach for predicting the lottery numbers, not the most accurate answer but at least we have some predicted output. I am implementing the regression based and neural network models for this. Is their any specific approach which follows this?
It is impossible. The lottery numbers are random - actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don't even try it.
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Id like to make a program based off data taken from a video stream. Sort of how the Chinese company SenseTime can track cars and people. I know I need a back and front end programmer for the main program but I do not know much about extracting and tracking data from the video feed. Im guessing id need a programmer that knows about machine learning, neural networks, & computer vision?
Thanks for the help
That is correct. A "Data Scientist" is someone who will have experience in this field. Someone with a specialty in computer vision and convolutional neural networks.
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Hello everyone I'm building a license plate detection model in Tensorflow. I built a function that chooses a license plate at random from a collection of ~5000 plates and puts it in a random place in on a random background and saves the coordinates. At first I thought to generate about 40K images this way and train the network on with the generated data. But wouldn't it be a good idea to just continiously keep generating new data to feed to the network and basically eliminate any chance of it getting overfitted?
This is an excellent way to train it on how to spot the discontinuities around a superimposed yellow / white / blue rectangle, but maybe not such a great way of teaching it to spot a real license plate. If you've got a good way of procedurally generating images then great! but be warned.
It might spot the wrong pattern.
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I wanna experiment k-means clustering method on different kind of images, so I am trying to find different kind of images used in image segmentation such as MRI images.I want to gather some more categories.
Any suggestion would be gratefully appreciated.
Although this is not the correct place for asking your question, to help you ,Image segmentation has a wide range of application including segmenting Satellite imagery
and Medical Imaging images, Texture Recognition, Facial Recognition System, Automatic Number Plate Recognition, and a lot of other machine vision applications.