Question title is pretty self explanatory.
I couldn't find any option to quantize vertex coordinates.
I think Meshlab determines itself but I want to determine it myself. (32bit 16bit 10bit etc.)
Does meshlab capable of quantization of vertices? If not what is the default bit value of vertex representations?
To my acknowledge, meshlab is not capable of doing that. I suggest you to export your mesh in ply ascii file format and use matlab or python to process the lines of the coordinates.
Related
I'm following the "Getting started with the built-in image classification algorithm" tutorial from Google's AI Platform and before submit a training job, one has to specify (it seems optional) the "image_size" which is defined as: "the image size (width and height) used for training". Do I have to specify a couple of scalars (comma delimited?) or a scalar ? How can we interpret this parameter? If I specify something, does it impose that all my input images should be of this particular size or will the images be automatically resized (or cropped?) to this size by the training graph? And equivalently for prediction task do I have to resize my input image to this specific size or does the prediction graph takes care of that.
Have not used the built in classifier however I am pretty sure you should specify the images as as a tuple of integers like (200,300). The classifier will read in your images and convert all of them to this size automatically.
I am trying to build a Land Cover Classification model for Sentinel Image. The Image Channels(bands) I am using are 32-bit float.
I need to understand how to best format the Image data, both the chips/patches for training and the Target Image for Classification. I have few questions?
Do I need to convert my Original Image and Training Chips from 32bit to other depth?
Do I need to ensure that both the training chips/patches and target have same depth (either 32bit, 16bit or other)?
Do I need to resale my data? I saw some papers where data was rescaled between 0-1 or 0-255?
Does data depth effect the performance of learning and predicting?
Many thanks.
Maz
The best precision to use on a PC is float32 for many reasons like, more precision makes calculation more accurate which is better, but somehow float16 is slower than float32 on PC(I don't remember why) and float64 is unusable slow on regular machines.
So
You usually need to use float32 as input anyway. So if it's float32 in the first place then just use it like that.
You do, but I think they all will got converted to ther desired precision during fit or predict for keras. It's in $HOME/.keras/keras.json.
I don't think it's a need but std centered rescale helps convergence, though, google always simply rescale to -1 to 1.
It does, but as I said, more precision gives better accuracy but it slower.
I'm about to start developing a neural net here with Tensorflow, but before I get into it too deep, I was hoping I could get some feedback on exactly what type of neural net I will need for this (If a net is the right way to go about this at all)
I need the NN to input an image, and output another image. This will be used for path-mapping on a robot I'm working on. The input image will be a disparity map, and the output will be a "driveable map" (an image that displays what in the scene can be driven on, and what can't)
I have built a dataset using Unity 3d. Here is an example from the set:
disparity map
driveable map:
As you can probably see, white represents the area where my robot can drive and black is where it can't. I will need the NN to take a disparity map, and give me back a "driveable map". Can this be done?
Thanks!
Sorry I'm not an expert. Since there hasn't been a response on this and if you are still looking, the vocabulary I would use to describe this type of problem is disparity networks and segmentation. Your best bet may be a specific type of disparity network: U-net
We have been using Tensorflow for image classification, and we all see the results for the Admiral Grace Hopper, and we get:
military uniform (866): 0.647296
suit (794): 0.0477196
academic gown (896): 0.0232411
bow tie (817): 0.0157356
bolo tie (940): 0.0145024
I was wondering if there is any way to get the coordinates for each category within the image.
Tensorflow doesn't have sample code yet on image detection and localization but it's an open research problem with different approaches to do it using deep nets; for example you can lookup the papers on algorithms called OverFeat and YOLO (You Only Look Once).
Also, usually there's some preprocessing on the object coordinates labels, or postprocessing to suppress duplicate detections. Usually a second, different network is used to classify the object once it's detected.
I have a matrix and I want to decompose it into different matrices with low to high frequency limit. As I have noticed, it can be done using wavelet transform. I found something like the figure below for 1D signal and I want to do similar procedure for my 2D matrix using MATLAB. I want to decompose it to different matrices with low to high frequency components in different levels.
I used the matrix tool box, however, when I have problems with extracting the data.
How can I do this using MATLAB?
You are looking for the wavedec2 function.
There's a basic example w/ the function documentation here