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What's the best approach to recognize patterns in data, and what's the best way to learn more on the topic? A developer I am working with is developing a program that analyzes images of pavement to find cracks in the pavement.
2) If he finds a crack, he only finds small sections of it and denotes those sections as being separate cracks. My job is to write software that will read this data, analyze it, and tell the difference between false-positives and actual cracks.
I have tried various ways of filtering the data to eliminate false-positives, and have been using neural networks to a limited degree of success to group cracks together. EDIT My question is more about how to notice patterns in my coworker's data and identify those patterns as actual cracks. EDIT In all actuality, it would take AT LEAST 20 sample images to give an accurate representation of the data I'm working with. In addition to the useful comments about image processing, it also sounds like you're dealing with a clustering problem.
Clustering algorithms come from the machine learning literature, specifically unsupervised learning. In your case, a clustering algorithm would attempt to repeatedly merge small cracks to form larger cracks, until some stopping criteria is met.
Popular clustering algorithms include k-means clustering (demo) and hierarchical clustering. Chenn-Jung Huang, Chua-Chin Wang, Chi-Feng Wu, "Image Processing Techniques for Wafer Defect Cluster Identification," IEEE Design and Test of Computers, vol.
They're doing a visual inspection for defects on silicon wafers, and use a median filter to remove noise before using a nearest-neighbor clustering algorithm to detect the defects. If you want to fast track to a solution then I suggest you first try the your luck with a Convolutional Neural Net, which can perform pretty good image classification with a minimum of preprocessing and noramlization.
That aside, I think this is an edge detection problem rather than a classification problem. If you are still set on classification, then you are going to need a training set with known answers, since you need a way to quantify what differentiates a false positive from a real crack. My coworker's job is to find the cracks (mostly using edge detection), and my job is to classify them. I have to agree with ire_and_curses, once you dive into the realm of edge detection to patch your co-developers crack detection, and remove his false positives, it seems as if you would be doing his job. If the spec is for him to detect the cracks, and you classify them, then it's his job to do the edge detection and remove false positives. Particularly, you might be interested in whats called Morphological Operations like Dilation and Erosion?, which complements the job of an edge detector. What’s the best approach to recognize patterns in data, and what’s the best way to learn more on the topic? Start with one (any) pixel in a crack, then "follow" it to make a multipoint line out of the crack -- save the points that make up the line. It seems like no matter the algorithm, some parameter adjustment will be necessary for good performance. Basically what you have to do is apply statistical tools and methodologies to your datasets. It sounds a little like a problem there is in Rock Mechanics, where there are joints in a rock mass and these joints have to be grouped into 'sets' by orientation, length and other properties. Once you have that then for each crack, I would generate a random crack or pattern of cracks based on the classification you have created. To then deal with the false positives you will need to create a pattern for each of the different types of false positives i.e.
Finally, you will need to 'tweak' the definition of different crack types to try and get a better result. One other modification that sometimes helps when I'm doing problems like this is to have a random group. Read the latest teen books for free and more on Riveted, where YA Fiction is Our Addiction! Get book club recommendations, access to more 1,000 reading group guides, author updates, and more! Get relationship help, parenting advice, healthy recipes, and tips for living a happy life from our author experts.
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Digital products purchased from this site are sold by Simon & Schuster Digital Sales Inc. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. This is how I learned to program and I’m sure many other people learned that way too. Find a library and read the documentation, follow the tutorials and start trying things out. Start with a library in a language you know well then move on to other more powerful libraries. R Project for Statistical Computing: This is an environment and a lisp-like scripting language.
WEKA: This is a Data Mining workbench providing API, and a number of command line and graphical user interfaces for the whole data mining lifecycle. Frankly, none of the video courses I have seen are really suitable for a beginner, for a true beginner. Andrew Ng’s Stanford lectures are probably the best place to start for a course, otherwise there are one-off videos I recommend.
The Discipline of Machine Learning: A white paper defining the discipline of Machine Learning by Tom Mitchell. A Few Useful Things to Know about Machine Learning: This is a great paper because it pulls back from specific algorithms and motivates a number of important issues such as feature selection generalizability and model simplicity.
I’ve only listed two important papers, because reading papers can really bog you down. Most likely you’re coming to machine learning from another field, most likely computer science, programming or statistics. Nevertheless, there are a few books out there that encourage eager programmers to get started by teaching the minimum intuition for an algorithm and point to tools and libraries so that you can run off to and try things out.
Machine Learning (Affiliate Link): This is an old book and does include formulas and lots of references.

I've hand-picked the essential machine learning resources and created an exclusive Resource Guide. You will also get emails with my best Tips and Tricks for getting started in machine learning. I thought deeply about this post and I also went off and looked at other peoples lists of resources to make sure I didn’t miss anything important. For completeness, here are some other great lists of resources around the web for getting started in machine learning. Did I leave out a critically useful resource for a programmer interested in getting started in machine learning? He is a husband, proud father, academic researcher, author, professional developer and a machine learning practitioner. Good practice with textbooks, they are written for undergrads or more typically graduate level courses.
I suggest that you start with a language you are familiar with and find a machine learning library for that language. You could phrase machine learning tasks as DSP problems, I have seen that done most in the area of neural networks.
Books: I felt that the first two books mentioned by you were not really helpful in a constructive way. Nice work for getting through both courses, Yaser’s is significantly more challenging.
I agree that the algorithms will not click (the why) until you have an intuition for what they are doing. Mathematical monk has one of the simplest and straight forward video tutorials on machine learning. I’m working on machine learning in that specially Reinforcement Learning I want programming for RL with its implementation basics. This was the first book I read in this field and I think it provides beginners with the perfect mix between theoretical background and practical thinking. In the last 8 years, I have been actively doing research in the fields of Machine Learning, Data Mining & NLP.
Our goal was to rank the books objectively so that the buyers could have a data-driven, objective and fair list of top books. I take each book seriously as I read though each line, take notes and research every concept I don’t understand. For every crack his program finds, it produces an entry in a file that tells me which pixels make up that particular crack. There are no differing rules for light or dark cracks, other than the fact that it may be less likely for a dark crack to be grouped with a light one. If your coworker isn't identifying complete cracks, and that's the spec, then that makes it your problem. However I still think it is unlikely that your classifier will be able to connect the cracks, since these are specific to each individual paving slab.
If you can patch what his software did not detect, and remove his false positives around what he has given you. There has to be some higher-level logic that can sort out the lower level data, and as of this moment, the higher-level logic is my job and the lower level is his. I would start with Duda's Pattern Classification and use Bishop's Pattern Recognition and Machine Learning as reference. Also, looking at the sample images, I'd say you should try improving the edge detection a bit. There are lots of books written on the subject, and much of the material in these books will go beyond a line-detection problem like this. In other words, check the neighbors of the pixels, and if there are too many then ignore that pixel. Write it so it's easy to make minor changes in things like intensity thresholds, minimum and maximum thickness, etc. As more data are gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information.
The most used comparison methodologies are Student's t-test and the Chi squared test, to see if two unrelated variables are related with some confidence. In this instance one method that works well is clustering, although classical K-means does seem to have a few problems which I have addressed in the past using a genetic algorithm to run the interative solution. I guess this could either use an automated approach or a manual approach depending on how you define your different crack types. The scholarship provided in Enriched Classics enables readers to appreciate, understand, and enjoy the world's finest books to their full potential.
I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. If you are a true beginner and excited to get started in the field of machine learning, I hope you find something useful. If you’re a good programmer, you know you can move from language to language reasonably easily. All the stats stuff you could ever want to do will be provided in to R, including amazing plotting. You can prepare data, visualize explore, build classification, regression and clustering  models and many algorithms are provided built in as well as provided in third party plugins. It is an environment for numerical computing just like Matlab and makes it easy to write programs to solve linear and non-linear problems, such as those that underlie most machine learning algorithms.
They all presuppose a working knowledge of at least linear algebra and probability theory, and more.
In addition to enrolling, you can watch all the lectures anytime and get the handouts and lecture notes from the actual Stanford CS229 course. All the lectures and materials are available on the CalTech site. Again, like the Stanford class, you can take it at your own pace and complete the homework and assignments. This is very valuable because so few people talk about what it’s actually like to work on a problem and how to do it.
A paper is like a snippet of a textbook, but describes an experiment or some other frontier of the field. Nevertheless, there are some papers that you might find interesting if you are looking to get started in machine learning. This was a piece of the argument Mitchell used to convince the President of CMU to create a standalone Machine Learning department for a subject that will still be around in 100 years (also see this short interview with Tom Mitchell). Even then, most books expect you to have a grounding in at least linear algebra and probability theory.
It’s lite on theory, heavy on code examples and practical web problems and solutions.

It again provides worked examples that are practical, but it has a more of a data analysis flavor and uses R.
I was a Java programmer and this book and the companion library WEKA provided a perfect environment for me to try things out, implement my own algorithms as plug-ins and generally practice Machine Learning and the broader process of Data Mining. It’s a text book but is also very accessible with grounded motivations for each algorithm. He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production. Alternatively, there are multi-platform tools like WEKA that provide a user interface to start playing around. But they really helped me to realise that ML can’t be learnt by blindly following these algorithmic approaches. I am new to this area but keenly interested to go in details, so I was finding out from where to start. Of course, the area is broad and lots of time you need to read papers to stay updated & learn about the latest results. This is an ongoing project where we’ll continue improving the quality of our ranking algorithm. This journey has led me to Dive into probability and statistics as well as times series analysis. I will try my best to follow your suggestion on getting started will learning this vast field of Machine intelligence and learning.
Does anyone have any insight for a non-AI expert as to the best way to accomplish my task or learn more about it? Batchelder, “Patterned Wafer Inspection Using Spatial Filtering for Cluster Environment,” Applied Optics, vol. Liu, “A Neural-Network Approach to Recognize Defect Spatial Pattern in Semiconductor Fabrication.” IEEE Trans. However, there are so many false-positives that I doubt a clustering algorithm will notice a small number of edges oriented in a curve. Can you provide insight into how to classify his data rather than the pixel data from the image itself?
But if you manage to stitch all the cracks together, and avoid his false positives, then haven't you just done his job?
If you have to do edge detection to do that, then it sounds like you are not far from putting your co-developer out of work. You know you've got a difficult problem if people on stackoverflow are telling you to pay someone else to solve it.
It would take a good while for the material to sink in, but getting basic sense of pattern recognition and major approaches of classification problem should give you the direction.
Maybe smoothing the image with Gaussian and running more aggressive edge detection can increase detection of smaller cracks. If the lines are within a tolerance, then "connect" the lines -- link them or add them to the same structure or array.
It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
You will then be able to run the analysis picking out which is the most likely group for each crack you analyse. He was the author of numerous classics such as The Invisible Man, The Time Machine, The Island of Dr. My suggestion would be to pick one thing, one book or one library and read it cover to cover or work through all of the tutorials. I don’t think they are all suitable for using in your production system, but they are ideal for learning, exploring and prototyping. The Machine Learning category on CRAN (think: third-party Machine Learning packages) has code written by leaders in the field with state of the art methods, as well as anything else you can think of. Not related to WEKA, Mahout is a good Java framework for Machine Learning on Hadoop infrastructure if that is more your thing. You can go one step further and use services like BigML that offer machine learning interfaces on the web where you can explore building models all in the browser. I recommend you should always take notes when watching a video, even if you discard the notes later. It covers similar subjects and goes into a little bit more details and is more mathematical.
I not-so-secretly fantasise about funding a web reality TV show that follows participants in machine leaning competitions. It has similar aims (get programmers started in Machine Learning), but it includes maths and references as well as examples and snippets in python. I think that beginners can get a long way with spatial metaphors and analogy before having to dig down into the maths.
I know people who use it for all kinds of things like this (verification that a human can do easily but proves hard to code). I can sit here and make some assumptions about your data, but honestly you probably have the best idea about the data set since you've been dealing with it more than anyone. This way, you can connect the close cracks, which would likely be the same crack in the concrete. Then have the discipline to go and learn the math for the technique before you implement it a production system.
If you’re new to big data and machine learning, stick with WEKA and learn one thing at a time. The most valuable parts of this answer are the list of machine learning courses with lecture notes and the list of related posts on Q&A sites. I also think there are plenty of methods (instance based, trees, etc) where you can get along just fine with such intuitions. The problem, though, is that his algorithm for finding them isn't perfect, so I must find a way to sort out and classify imperfect data. Just follow the pixels in one (or two) directions to make up a line, then remove these pixels from the set of crack pixels. Otherwise you’ll be learning two things at once (language and machine learning) and making your life unnecessarily difficult.

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