There are some a command that gives us the volume of the solid without write the formule integral of the volume?
There is no volume object in ggb, so, no way.
Related
What is the difference between these types of image storage variables in EmguCV4, I'm really confused, Unfortunately there is no good training resource for EmguCV4, Even on the EmguCV4 site
Why should there be so many variables to store an image? When and where should we use which type of variable?
Mat, MCvMat
Image, MIplImage
ScalarArray
Matrix
I'm learning Image processing. My problem is Segmentation in RGB vector space. How to describe the Euclidean distance(Fomula 6.7-1 chap6 in Image processing- Gonzalez) to segment RGB in C programming. Thanks.
Minimum requirements to solve this problem:
1) learn C (to at least an medium-advanced level)
presuming that you're not going to decode jpegs or whatever image format you have from scratch:
2) learn how to use libraries in C.
3) find a library that allows you to read and write the image file format at hand
4) implement the algorithm and apply it to the image data
I want to flatten any general n-dimensional torch.Tensor but in a way which is computationally optimized. (By "flatten" here, I mean converting a given Tensor to a one-dimensional Tensor which has the same number of elements as the given vector.) I am using the following steps currently to do so:
local original_tensor = -- output of some intermediate layer of a conv-net residing in the GPU
local shaping_tensor = torch.Tensor(original_tensor:nElement())
original_tensor = original_tensor:resizeAs(shaping_tensor:cuda())
I believe it is slightly inefficient because of :cuda() which pushes this new Tensor from memory to the GPU. Can someone please suggest a more efficient way to do this?
Thanks in advance.
Typical approach is to create a view (thus not actually reshaping the tensor).
x:view(x:nElement())
which comes directly from official "torch for numpy users" https://github.com/torch/torch7/wiki/Torch-for-Numpy-users
Isn't this solved with the reshape command?
See the documentation and this example
I assume that you know how to grab the dimensions of original_tensor. Multiply them together to get the vector size.
local my_vector = nn.reshape(vector_size, original_vector)
Am I missing something? Is this still not efficient enough? It should be a highly parallel assignment.
I would like to have some insights in running Support Vector Machine (SVM) in Apache Spark.
When I use the run-example script given in the Spark home directory and using the argument org.apache.spark.mllib.classification.SVMWithSGD it displays the following Usage: SVM <master> <input_dir> <step_size> <regularization_parameter> <niters> message. I understand the <master>, the <input_dir> and the <niters> arguments.
Can you please help me figure out the rest of the arguments or at least direct me to some tutorial site of some sort?
<step_size> is the starting point for the learning rate. For convergence the step-size should be decreasing. In SGD this is implemented by taking the input value for step_size and dividing by the square root of the iteration.
<reg_param>is the scalar adjusting the strength of the constraints. Small values imply a soft margin and large values imply a hard margin. Infinity is the hardest margin.
They are explained in the docs in the last section.
I Have decided to use OpenCV to build a 3d scene by using a series of 2D Images. I found the example code that came with OpenCV [ build3dmodel.cpp Here ].
I just want to run this once and see what kind of outcome this gives. My knowledge with OpenCV is low, I don't want to understand the whole code, I just want to know how to give inputs to this program (the image set) to see the output.
The line command of this code example requires the following parameters:
build3dmodel -i intrinsics_filename.yml [-d detector] [-de
descriptor_extractor] -m model_name.yml
The first file is the camera matrix which you obtain after the calibration process (there is an especific example with it). Detector and descriptor detector must match with valid FeatureDetector and DescriptorExtractor names. Model name is a bit confusing, it looks like part of the yml file name where data will be saved.
First see some tutorial like introduction to OpenCv or OpenCV tutorial. Also, see input and output with OpenCv.