I am currently working with data regarding airspeed and groundspeed of birds crossing over sea.
My aims is to explore, if weather factors (tailwind, headwind, temperature, pressure etc.) affect the birds airspeed and groundspeed (how does bird play with its speed in changing enviroment).
I have found some nonlinear relationship so I am using GAM.
My model looks something like:
mod <- gam(y ~ s(x1, k=..) + s(x2, k=..) + s(x3, k=..) + s(x1,x2,k=...), data = data_clean, method = "REML")
However, I would like to use bird ID as a random effect. Therefore I was thinking of using GAMM rather then GAM. But, I have read several topics regarding GAM and random effect. It seems that GAM can work with random effect as well.
My question is .. would be enough to add +s(ID, bs="re) if I wanna take bird performance during crossing as a random effect (but dont wanna deeply explore it on individual lvl). Or is it better to fit GAMM?
Thank you very much,
Jan
Related
I'm working on a simple project in which I'm trying to describe the relationship between two positively correlated variables and determine if that relationship is changing over time, and if so, to what degree. I feel like this is something people probably do pretty often, but maybe I'm just not using the correct terminology because google isn't helping me very much.
I've plotted the variables on a scatter plot and know how to determine the correlation coefficient and plot a linear regression. I thought this may be a good first step because the linear regression tells me what I can expect y to be for a given x value. This means I can quantify how "far away" each data point is from the regression line (I think this is called the squared error?). Now I'd like to see what the error looks like for each data point over time. For example, if I have 100 data points and the most recent 20 are much farther away from where the regression line/function says it should be, maybe I could say that the relationship between the variables is showing signs of changing? Does that make any sense at all or am I way off base?
I have a suspicion that there is a much simpler way to do this and/or that I'm going about it in the wrong way. I'd appreciate any guidance you can offer!
I can suggest two strands of literature that study changing relationships over time. Typing these names into google should provide you with a large number of references so I'll stick to more concise descriptions.
(1) Structural break modelling. As the name suggest, this assumes that there has been a sudden change in parameters (e.g. a correlation coefficient). This is applicable if there has been a policy change, change in measurement device, etc. The estimation approach is indeed very close to the procedure you suggest. Namely, you would estimate the squared error (or some other measure of fit) on the full sample and the two sub-samples (before and after break). If the gains in fit are large when dividing the sample, then you would favour the model with the break and use different coefficients before and after the structural change.
(2) Time-varying coefficient models. This approach is more subtle as coefficients will now evolve more slowly over time. These changes can originate from the time evolution of some observed variables or they can be modeled through some unobserved latent process. In the latter case the estimation typically involves the use of state-space models (and thus the Kalman filter or some more advanced filtering techniques).
I hope this helps!
I'm trying to find powerlines in LIDAR points clouds with skimage.measures ransac() function. This is my very first time meddling with these modules in python so bear with me.
So far all I knew how to do reliably was filtering low or 'ground' points from the cloud to reduce the number of points to deal with.
def filter_Z(las, threshold):
filtered = laspy.create(point_format = las.header.point_format, file_version = las.header.version)
filtered.points = las.points[las.Z > las.Z.min() + threshold]
print(f'original size: {len(las.points)}')
print(f'filtered size: {len(filtered.points)}')
filtered.write('filtered_points2.las')
return filtered
The threshold is something I put in by hand since in the las files I worked with are some nasty outliers that prevent me from dynamically calculating it.
The filtered point cloud, or one of them atleast looks like this:
Note the evil red outliers on top, maybe they're birds or something. Along with them are trees and roofs of buildings. If anyone wants to take a look at the .las files, let me know. I can't put a wetransfer link in the body of the question.
A top down view:
I've looked into it as much as I could, and found the skimage.measure module and the ransac function that comes with it. I played around a bit to get a feel for it and currently I'm stumped on how to continue.
def ransac_linefit_sklearn(points):
model_robust, inliers = ransac(points, LineModelND, min_samples=2, residual_threshold=1000, max_trials=1000)
return model_robust, inliers
The result is quite predictable (I ran ransac on a 2D view of the cloud just to make it a bit easier on the pc)
Using this doesn't really yield any good results in examples like the one I posted. The vegetation clusters have too many points and the line is fitted through it because it has the highest point density.
I tried DBSCAN() to cluster up the points but it didn't work. I also attempted OPTICS() but as I write it still hasn't finished running.
From what I've read on various articles, the best course of action would be to cluster up the points and perform RANSAC on each individual cluster to find lines, but I'm not really sure on how to do that or what clustering method to use in situations like these.
One thing I'm also curious about doing is just filtering out the big blobs of trees that mess with model fititng.
Inadequacy of RANSAC
RANSAC works best whenever your data fits a mono-modal distribution around your model. In the case of this point cloud, it works best whenever there is only one line with outliers, but there are at least 5 lines when viewed birds-eye. Check out this older SO post that discusses your problem. Francesco's response suggests an iterative RANSAC based approach.
Octrees and SVD
Colleagues worked on a similar problem in my previous job. I am not fluent in the approach, but I know enough to provide some hints.
Their approach resembled Francesco's suggestion. They partitioned the point-cloud into octrees and calculated the singular value decomposition (SVD) within each partition. The three resulting singular values will correspond to the geometric distribution of the data.
If the first singular value is significantly greater than the other two, then the points are line-like.
If the first and second singular values are significantly greater than the other, then the points are plane-like
If all three values are of similar magnitude, then the data is just a "glob" of points.
They used these rules iteratively to rule out which points were most likely NOT part of the lines.
Literature
If you want to look into published methods, maybe this paper is a good starting point. Power lines are modeled as hyperbolic functions.
I have a spatial dataset that consists of a large number of point measurements (n=10^4) that were taken along regular grid lines (500m x 500m) and some arbitrary lines and blocks in between. Single measurements taken with a spacing of about 0.3-1.0m (varying) along these lines (see example showing every 10th point).
The data can be assumed to be normally distributed but shows a strong small-scale variability in some regions. And there is some trend with elevation (r=0.5) that can easily be removed.
Regardless of the coding platform, I'm looking for a good or "the optimal" way to interpolate these points to a regular 25 x 25m grid over the entire area of interest (5000 x 7000m). I know about the wide range of kriging techniques but I wondered if somebody has a specific idea on how to handle the "oversampling along lines" with rather large gaps between the lines.
Thank you for any advice!
Leo
Kriging technique does not perform well when the points to interpolate are taken on a regular grid, because it is necessary to have a wide range of different inter-points distances in order to well estimate the covariance model.
Your case is a bit particular... The oversampling over the lines is not a problem at all. The main problem is the big holes you have in your grid. If think that these holes will create problems whatever the interpolation technique you use.
However it is difficult to predict a priori if kriging will behave well. I advise you to try it anyway.
Kriging is only suited for interpolating. You cannot extrapolate with kriging metamodel, so that you won't be able to predict values in the bottom left part of your figure for example (because you have no point here).
To perform kriging, I advise you to use the following tools (depending the languages you're more familiar with):
DiceKriging package in R (the one I use preferably)
fields package in R (which is more specialized on spatial fields)
DACE toolbox in matlab
Bonus: a link to a reference book about kriging which is available online: http://www.gaussianprocess.org/
PS: This type of question is more statistics oriented than programming and may be better suited to the stats.stackexchange.com website.
I created a multi-class SVM model using libSVM for categorizing images. I optimized for the C and G parameters using grid search and used the RBF kernel.
The classes are 1) animal 2) floral 3) landscape 4) portrait.
My training set is 100 images from each category, and for each image, I extracted a 920-length vector using Lear's Gist Descriptor C code: http://lear.inrialpes.fr/software.
Upon testing my model on 50 images/category, I achieved ~50% accuracy, which is twice as good as random (25% since there are four classes).
I'm relatively new to computer vision, but familiar with machine learning techniques. Any suggestions on how to improve accuracy effectively?
Thanks so much and I look forward to your responses!
This is very very very open research challenge. And there isn't necessarily a single answer that is theoretically guaranteed to be better.
Given your categories, it's not a bad start though, but keep in mind that Gist was originally designed as a global descriptor for scene classification (albeit has empirically proven useful for other image categories). On the representation side, I recommend trying color-based features like patch-based histograms as well as popular low-level gradient features like SIFT. If you're just beginning to learn about computer vision, then I would say SVM is plenty for what you're doing depending on the variability in your image set, e.g. illumination, view-angle, focus, etc.
I will be start working on a robotics project which involves a mobile robot that has mounted 2 cameras (1.3 MP) fixed at a distance of 0.5m in between.I also have a few ultrasonic sensors, but they have only a 10 metter range and my enviroment is rather large (as an example, take a large warehouse with many pillars, boxes, walls .etc) .My main task is to identify obstacles and also find a roughly "best" route that the robot must take in order to navigate in a "rough" enviroment (the ground floor is not smooth at all). All the image processing is not made on the robot, but on a computer with NVIDIA GT425 2Gb Ram.
My questions are :
Should I mount the cameras on a rotative suport, so that they take pictures on a wider angle?
It is posible creating a reasonable 3D reconstruction based on only 2 views at such a small distance in between? If so, to what degree I can use this for obstacle avoidance and a best route construction?
If a roughly accurate 3D representation of the enviroment can be made, how can it be used as creating a map of the enviroment? (Consider the following example: the robot must sweep an fairly large area and it would be energy efficient if it would not go through the same place (or course) twice;however when a 3D reconstruction is made from one direction, how can it tell if it has already been there if it comes from the opposite direction )
I have found this response on a similar question , but I am still concerned with the accuracy of 3D reconstruction (for example a couple of boxes situated at 100m considering the small resolution and distance between the cameras).
I am just starting gathering information for this project, so if you haved worked on something similar please give me some guidelines (and some links:D) on how should I approach this specific task.
Thanks in advance,
Tamash
If you want to do obstacle avoidance, it is probably easiest to use the ultrasonic sensors. If the robot is moving at speeds suitable for a human environment then their range of 10m gives you ample time to stop the robot. Keep in mind that no system will guarantee that you don't accidentally hit something.
(2) It is posible creating a reasonable 3D reconstruction based on only 2 views at such a small distance in between? If so, to what degree I can use this for obstacle avoidance and a best route construction?
Yes, this is possible. Have a look at ROS and their vSLAM. http://www.ros.org/wiki/vslam and http://www.ros.org/wiki/slam_gmapping would be two of many possible resources.
however when a 3D reconstruction is made from one direction, how can it tell if it has already been there if it comes from the opposite direction
Well, you are trying to find your position given a measurement and a map. That should be possible, and it wouldn't matter from which direction the map was created. However, there is the loop closure problem. Because you are creating a 3D map at the same time as you are trying to find your way around, you don't know whether you are at a new place or at a place you have seen before.
CONCLUSION
This is a difficult task!
Actually, it's more than one. First you have simple obstacle avoidance (i.e. Don't drive into things.). Then you want to do simultaneous localisation and mapping (SLAM, read Wikipedia on that) and finally you want to do path planning (i.e. sweeping the floor without covering area twice).
I hope that helps?
I'd say no if you mean each eye rotating independently. You won't get the accuracy you need to do the stereo correspondence and make calibration a nightmare. But if you want the whole "head" of the robot to pivot, then that may be doable. But you should have some good encoders on the joints.
If you use ROS, there are some tools which help you turn the two stereo images into a 3d point cloud. http://www.ros.org/wiki/stereo_image_proc. There is a tradeoff between your baseline (the distance between the cameras) and your resolution at different ranges. large baseline = greater resolution at large distances, but it also has a large minimum distance. I don't think i would expect more than a few centimeters of accuracy from a static stereo rig. and this accuracy only gets worse when you compound there robot's location uncertainty.
2.5. for mapping and obstacle avoidance the first thing i would try to do is segment out the ground plane. the ground plane goes to mapping, and everything above is an obstacle. check out PCL for some point cloud operating functions: http://pointclouds.org/
if you can't simply put a planar laser on the robot like a SICK or Hokuyo, then i might try to convert the 3d point cloud into a pseudo-laser-scan then use some off the shelf SLAM instead of trying to do visual slam. i think you'll have better results.
Other thoughts:
now that the Microsoft Kinect has been released, it is usually easier (and cheaper) to simply use that to get a 3d point cloud instead of doing actual stereo.
This project sounds a lot like the DARPA LAGR program. (learning applied to ground robots). That program is over, but you may be able to track down papers published from it.