Is there a feature in Vertex AI which will allow AI/ML to assist in labeling data? This usually works by providing a small set of labeled data, followed by a model creation which assists in labeling more data. As more and more data is labeled the model keeps getting better.
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I want to train an object tracking model in Vertex AI for one type of object. The "Train New Model" button says "To train a model, you must have at least two labels and each label included in training must have at least 15 videos assigned to it." I do not find any explanation of this requirement in the documentation. Does anyone know why I must have two labels?
The minimum condition you have mentioned to train a model is required for Vertex AI to know what object to look for, The model will learn to identify the patterns for tracking by setting bounding boxes and label for the object. Generally by having more videos with label will produce a better outcome for the training. To see more details please visit the article here.
Also I believe having more than 1 label is needed for the model to identify an object by having a reference comparison from the 2nd label. This can be handy when you are in the part of evaluating and testing your model as you can tune your score threshold and prediction outcome for a more precise model.
Using spherical nodes, cylindrical bones, and cone-twist constraints, I've managed to create a simple skeleton in 3 dimensions. I'm using an offshoot of the bullet physics library (physijs by #chandlerprall, along with threejs).
Now I'd like to add muscles. I've been trying for the last two days to get some sort of sliding constraint or generic 6-DOF constraint to get the muscle to be able to contract and pull its two nodes towards one another.
I'm getting all sorts of crazy results, and I'm beginning to think that I'm going about this in the wrong way. I don't think I can simply use two cone twist constraints and then scale the muscle along its length-wise axis, because scaling collision meshes is apparently fairly expensive.
All I need is a 'muscle' which can attach to two nodes and 'contract' to pull in both its nodes.
Can anyone provide some advice on how I might best approach this using the bullet engine (or really, any physics engine)?
EDIT: What if I don't need collisions to occur for the muscle? Say I just need a visual muscle which is constrained to 2 nodes:
The two nodes are linearly constrained to the muscle collision mesh, which instead of being a large mesh, is just a small one that is only there to keep the visual muscle geometry in place, and provide an axis for the nodes to be constrained to.
I could then use the linear motor that comes with the sliding constraint to move the nodes along the axis. Can anyone see any problems with this? My initial problem is that the smaller collision mesh is a bit volatile and seems to move around all over the place...
I don't have any experience with Bullet. However, there is a large academic community that simulates human motion by modeling the human as a system of rigid bodies. In these simulations, the human is actuated by muscles.
The muscles used in such simulations are modeled to generate force in a physiological way. The amount of force a muscle can produce at any given instant depends on its length and the rate at which its length is changing. Here is a paper that describes a fairly complex muscle model that biomechanists might use: http://nmbl.stanford.edu/publications/pdf/Millard2013.pdf.
Another complication with modeling muscles that comes up in biomechanical simulations is that the path of a muscle must be able to wrap around joints (such as the knee). This is what you are trying to get at when you mention collisions along a muscle. This is called muscle wrapping. See http://www.baylor.edu/content/services/document.php/41153.pdf.
I'm a graduate student in a lab that does simulations of humans involving many muscles. We use the multibody dynamics library (physics engine) Simbody (http://github.com/simbody/simbody), which allows one to define force elements that act along a path. Such paths can be defined in pretty complex ways: they could wrap around many different surfaces. To simulate muscle-driven human motion, we use OpenSim (http://opensim.stanford.edu), which in turn uses Simbody to simulate the physics.
I'm working on a project with computer vision (opencv 2.4 on c++). On this project I'm trying to detect certain features to build a map (an internal representation) of the world around.
The information I have available is the camera pose (6D vector with 3 position and 3 angular values), calibration values (focal length, distortion, etc) and the features detected on the object being tracked (this features are basically the contour of the object but it doesn't really matter)
Since the camera pose, the position of the features and other variables are subject to errors, I want to model the object as a 3D probability density function (with the probability of finding the "object" on a given 3D point on space, this is important since each contour has a probability associated of how likely it is that it is an actually object-contour instead of a noise-contour(bear with me)).
Example:
If the object were a sphere, I would detect a circle (contour). Since I know the camera pose, but have no depth information, the internal representation of that object should be a fuzzy cylinder (or a cone, if the camera's perspective is included but it's not relevant). If new information is available (new images from a different location) a new contour would be detected, with it's own fuzzy cylinder merged with previous data. Now we should have a region where the probability of finding the object is greater in some areas and weaker somewhere else. As new information is available, the model should converge to the original object shape.
I hope the idea is clear now.
This model should be able to:
Grow dynamically if needed.
Update efficiently as new observations are made (updating the probability inside making stronger the areas observed multiple times and weaker otherwise). Ideally the system should be able to update in real time.
Now the question:
How can I do to computationally represent this kind of fuzzy information in such a way that I can perform these tasks on it?
Any suitable algorithm, data structure, c++ library or tool would help.
I'll answer with the computer vision equivalent of Monty Python: "SLAM, SLAM, SLAM, SLAM!": :-) I'd suggest starting with Sebastian Thrun's tome.
However, there's older older work on the Bayesian side of active computer vision that's directly relevant to your question of geometry estimation, e.g. Whaite and Ferrie's seminal IEEE paper on uncertainty modeling (Waithe, P. and Ferrie, F. (1991). From uncertainty to visual exploration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10):1038–1049.). For a more general (and perhaps mathematically neater) view on this subject, see also chapter 4 of D.J.C. MacKay's Ph.D. thesis.
I have a number of images (as well as the original data sources) that exhibit specific features. Some of them have distinct vertical/horizontal regions, as shown in the following figure or simply "blobs"/concentrations of points in very specific regions.
These images are associated with specific labels/classes, for instance, a label "A" exhibits very characteristic horizontal lines (like those marked in figure) at y = 700 and y = 150. Those images that belong to class "B", exhibit vertical lines at x = 200, 260 and 370, class "C"..., and so on.
Besides these known/labelled classes, I have a bunch of images that exhibit one of these features, or their combination.
My goal is to use these known classes to train some ML algorithm in order to further use it for classifying those images that do not have any labels. I understand that I need to somehow extract these particularities (vertical/horizontal lines, blobs of high point density that usually occur in the upper-right corner of the image, or in the (x,y) region of (250-400, 800-1500) and so on). Next, I would need to train some ML algorithm with these features, and only then use the trained system for classif.
I have been looking and playing with some tools for 3-4 days now (like PIL, with different blurring, smoothing and edge detecting techniques, or MDP's Gaussian classifiers and many posts on stackoverflow). The problem is that I cannot for a clear "solution process + appropriate tools" combination.
I would greatly appreciate if someone could guide me a bit more into the techniques for extracting these very specific/weird features from images (or even original datasets), and/or tools to use.
I understand you have the feature vectors for your samples (training data).
If this is so and you are only looking for a machine learning algorithm implementation, I would suggest you to use Support Vector Machines SVM. A popular implementation called SVM-light is available free of cost for your use. http://svmlight.joachims.org/
Please note that the above site gives a 2-class implementation. If you need a multi-class SVM you can get it from http://svmlight.joachims.org/svm_multiclass.html
Yet few more popular classifiers are
Nearest Neighbour classifier
C4.5 Decision Trees
Neural Network
I have a lot of polygons. Ideally, all the polygons must not overlap one other, but they can be located adjacent to one another.
But practically, I would have to allow for slight polygon overlap ( defined by a certain tolerance) because all these polygons are obtained from user hand drawing input, which is not as machine-precised as I want them to be.
My question is, is there any software library components that:
Allows one to input a range of polygons
Check if the polygons are overlapped more than a prespecified tolerance
If yes, then stop, or else, continue
Create mesh in terms of coordinates and elements for the polygons by grouping common vertex and edges together?
More importantly, link back the mesh edges to the original polygon(s)'s edge?
Or is there anyone tackle this issue before?
This issue is a daily "bread" of GIS applications - this is what is exactly done there. We also learned that at a GIS course. Look into GIS systems how they address this issue. E.g. ArcGIS define so called topology rules and has some functions to check if the edited features are topologically correct. See http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Topology_rules
This is pretty long, only because the question is so big. I've tried to group my comments based on your bullet points.
Components to draw polygons
My guess is that you'll have limited success without providing more information - a component to draw polygons will be very much coupled to the language and UI paradigm you are using for the rest of your project, ie. code for a web component will look very different to a native component.
Perhaps an alternative is to separate this element of the process out from the rest of what you're trying to do. There are some absolutely fantastic pre-existing editors that you can use to create 2d and 3d polygons.
Inkscape is an example of a vector graphics editor that makes it easy to enter 2d polygons, and has the advantage of producing output SVG, which is reasonably easy to parse.
In three dimensions Blender is an open source editor that can be used to produce arbitrary geometries that can be exported to a number of formats.
If you can use a google-maps API (possibly in an native HTML rendering control), and you are interested in adding spatial points on a map overlay, you may be interested in related click-to-draw polygon question on stackoverflow. From past experience, other map APIs like OpenLayers support similar approaches.
Check whether polygons are overlapped
Thomas T made the point in his answer, that there are families of related predicates that can be used to address this and related queries. If you are literally just looking for overlaps and other set theoretic operations (union, intersection, set difference) in two dimensions you can use the General Polygon Clipper
You may also need to consider the slightly more generic problem when two polygons that don't overlap or share a vertex when they should. You can use a Minkowski sum to dilate (enlarge) two and three dimensional polygons to avoid such problems. The Computational Geometry Algorithms Library has robust implementations of these algorithms.
I think that it's more likely that you are really looking for a piece of software that can perform vertex welding, Christer Ericson's book Real-time Collision Detection includes extensive and very readable description of the basics in this field, and also on related issues of edge snapping, crack detection, T-junctions and more. However, even though code snippets are included for that book, I know of no ready made library that addresses these problems, in particular, no complete implementation is given for anything beyond basic vertex welding.
Obviously all 3D packages (blender, maya, max, rhino) all include built in software and tools to solve this problem.
Group polygons based on vertices
From past experience, this turned out to be one of the most time consuming parts of developing software to solve problems in this area. It requires reasonable understanding of graph theory and algorithms to traverse boundaries. It is worth relying upon a solid geometry or graph library to do the heavy lifting for you. In the past I've had success with igraph.
Link the updated polygons back to the originals.
Again, from past experience, this is just a case of careful bookkeeping, and some very careful design of your mesh classes up-front. I'd like to give more advice, but even after spending a big chunk of the last six months on this, I'm still struggling to find a "nice" way to do this.
Other Comments
If you're interacting with users, I would strongly recommend avoiding this issue where possible by using an editor that "snaps", rounding all user entered points onto a grid. This will hopefully significantly reduce the amount of work that you have to do.
Yes, you can use OGR. It has python bindings. Specifically, the Geometry class has an Intersects method. I don't fully understand what you want in points 4 and 5.