I'm working on a simple Measuring Software for HunterLab (Color) instruments (EZ line) (screenshot here) and I hope someone can help out here.
They deliver spectral data from 400nm...700nm by 10nm using a D65 light source and 10° Observer.
I have the observer functions for ASTM D65 which work great and I can reproduce any value from the instrument 1:1, as long as i measure in D65, 10° (converting to XYZ and then CIELab using tristimulus references for perfect reflecting diffuser).
That was done mostly using algorithms from brucelindbloom.com and easyrgb.com, both have some great information!
Now I want to add the ability to convert the spectral data to another observer or another illuminant (or both). But I cant wrap my head around how to do that.
I guess some directions would be enough but I dont know if I would need even more references for that (references for illuminants by wavelength?) or if its done by some other means.
OK, here is the answer :)
Spectral data from most spectrophotometers is already corrected in so far that the hardware illuminant and angle dont matter.
What you do is just use the observer functions for every single angle/illuminant, as written in ASTM E308, to convert the spectral data to XYZ instead of only using the table which corresponds to the hardware illuminant/angle.
Thats a lot of reference values but it works perfect.
Related
I made a program aimed to simulate the intensity of light when many light bulbs are put together. I have intensity data of one bulb in xls.-files. So, I want to program to work as follows.
Open the xls.-file and get the data.
Put the data into different positions. I put one data set (one bulb) in each excel sheet. This is to simulate putting the bulb in different places.
Sum the data in the same cell across the different sheets.
My LabVIEW front panel and block diagram are:
My problem is this program runs too slowly. How should I improve this? I have an idea to make a big array and accumulate data in that array. However, I do not know how to do it. The Insert Into Array and Replace Array Subset functions are not suitable for my purposes.
The most probable reason of slow performance is that you do a lot of operations on Excel file. You should rather read data into memory and operate on them in VI. At the end, if you need, you can update the Excel file with final results.
It would be difficult to tell you exactly how to do it. As you said, you're beginner and I think that the best way would be to simple do some LabVIEW exercises and gain more experience to understand how to work with arrays :) I recommend to take a look at examples (Help->Find Examples), read some user guides from ni.com or find other "getting started" materials on the Internet.
Check these, you may find them useful:
https://zone.ni.com/reference/en-XX/help/371361R-01/lvhowto/lv_getting_started/
https://www.ni.com/getting-started/labview-basics/data-structures
https://www.ni.com/pl-pl/support/documentation/supplemental/08/labview-arrays-and-clusters-explained.html
In image processing, each of the following methods can be used to get the orientation of a blob region:
Using second order central moments
Using PCA to find the axis
Using distance transform to get the skeleton and axis
Other techniques, like fitting the contour of the region with an ellipse.
When should I consider using a specific method? How do they compare, in terms of accuracy and performance?
I'll give you a vague general answer, and I'm sure others will give you more details. This issue comes up all the time in image processing. There are N ways to solve my problem, which one should I use? The answer is, start with the simplest one that you understand the best. For most people, that's probably 1 or 2 in your example. In most cases, they will be nearly identical and sufficient. If for some reason the techniques don't work on your data, you have now learned for yourself, a case where the techniques fail. Now, you need to start exploring other techniques. This is where the hard work comes in, in being a image processing practitioner. There are no silver bullets, there's a grab bag of techniques that work in specific contexts, which you have to learn and figure out. When you learn this for yourself, you will become god like among your peers.
For this specific example, if your data is roughly ellipsoidal, all these techniques will be similar results. As your data moves away from ellipsoidal, (say spider like) the PCA/Second order moments / contours will start to give poor results. The skeleton approaches become more robust, but mapping a complex skeleton to a single axis / orientation can become a very difficult problem, and may require more apriori knowledge about the blob.
I want to get my hands dirty with some machine learning, and I finally have a problem which seems like a good beginner project. However, despite reading a lot about the subject I am unsure how to get started, and what my basic approach should be.
I have a dataset which should look like this.
a real dataset looks more like this:
I want to identify the points in the red circles (on the first image), and be robust against occasional artifacts like the one in the blue circle.
I sounds like a really easy task. However, the is quite a lot of noise in the raw data. My current implementation is pretty traditional. It blurs the data and compares the first and second derivative to some estimated threshold values. This approach works, but can "only" identify the points with ~99.7% accuracy, but since I do around 100.000 measurements a day I would love to increase this number.
So, this is what I have:
All the datasets I want/need
A pretty good model of how the data should look.
A pretty good training set, using my existing algorithm (the outlines can be fixed manually)
However, I do not have a basic idea how what approach I should use. I feels like none of the material I've read on machine learning fit's this problem.
Can someone help me with the super high level approach to solve this problem?
Can someone explain briefly how SDM (Supervised Descent Method) for Feature Extraction works?
I searched a lot on the Internet but couldn't found what I was looking for.
Is it only for feature extraction in videos, or can it be used in both videos and images?
If someone can explain, it would be of great help.
SDM is a method to align shapes in images. It uses feature extractors (SIFT and HoG) in the process, but is not a feature extractor.
Similar methods are ASM, AAM or CLM, but SDM has better performance and accuracy.
In the case of SDM, in the training process, the system learns some descent vectors from a initial shape configuration (different from the shapes in the database) to the database sets. Those vectors have the hability of fitting a new initial configuration with the face shape in the image you want to fit in.
This link can help you to learn more about it: http://arxiv.org/pdf/1405.0601v1.pdf
About the code, there is some demo samples in the main page of IntraFace but if you are looking for the code, I don´t think you can find it.
You can use vl_sift for beginning its working even more precise then their original descriptor however its not fast as their descriptor for real time implementation.
Regarding to their implementation no code were released so far. They are using some specialized version with very fast histogram calculation.
I have a database of images. When I take a new picture, I want to compare it against the images in this database and receive a similarity score (using OpenCV). This way I want to detect, if I have an image, which is very similar to the fresh picture.
Is it possible to create a fingerprint/hash of my database images and match new ones against it?
I'm searching for a alogrithm code snippet or technical demo and not for a commercial solution.
Best,
Stefan
As Pual R has commented, this "fingerprint/hash" is usually a set of feature vectors or a set of feature descriptors. But most of feature vectors used in computer vision are usually too computationally expensive for searching against a database. So this task need a special kind of feature descriptors because such descriptors as SURF and SIFT will take too much time for searching even with various optimizations.
The only thing that OpenCV has for your task (object categorization) is implementation of Bag of visual Words (BOW).
It can compute special kind of image features and train visual words vocabulary. Next you can use this vocabulary to find similar images in your database and compute similarity score.
Here is OpenCV documentation for bag of words. Also OpenCV has a sample named bagofwords_classification.cpp. It is really big but might be helpful.
Content-based image retrieval systems are still a field of active research: http://citeseerx.ist.psu.edu/search?q=content-based+image+retrieval
First you have to be clear, what constitutes similar in your context:
Similar color distribution: Use something like color descriptors for subdivisions of the image, you should get some fairly satisfying results.
Similar objects: Since the computer does not know, what an object is, you will not get very far, unless you have some extensive domain knowledge about the object (or few object classes). A good overview about the current state of research can be seen here (results) and soon here.
There is no "serve all needs"-algorithm for the problem you described. The more you can share about the specifics of your problem, the better answers you might get. Posting some representative images (if possible) and describing the desired outcome is also very helpful.
This would be a good question for computer-vision.stackexchange.com, if it already existed.
You can use pHash Algorithm and store phash value in Database, then use this code:
double const mismatch = algo->compare(image1Hash, image2Hash);
Here 'mismatch' value can easly tell you the similarity ratio between two images.
pHash function:
AverageHash
PHASH
MarrHildrethHash
RadialVarianceHash
BlockMeanHash
BlockMeanHash
ColorMomentHash
These function are well Enough to evaluate Image Similarities in Every Aspects.