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I tried to read Digital Image Processing by Gonzalez/Woods but I found it difficult to understand/grasp. I have taken a Graduate Course in Computer Vision, which is more practically oriented and I am doing lot of cool stuff with OpenCV, however I still feel I am swimming in higher abstractions, and do NOT understand the basics beneath.
I am planning to read a book on Computer Vision/Image Processing during the Winter Break to solidify my understanding of the content and would appreciate some must-read suggestions
I have done assignments like - camera calibration, image transforms, stitching images into panoramas, haar classification.
You should probably take a look at Szeliski's book
Hartley and Zisserman's book is also excellent.
Gonzales and woods (or Wintz in my day) is a very good introduction.
There is a more readable but less concise introduction - Image-Processing-Analysis-Machine-Vision
And since you are working with opencv - you can do worse than read the opencv book
Have a look at this book. It's quite heavy (and expensive!), but it covers a lot of topics, and each chapter is authored by a different person that is competent in the corresponding field. If cost is a huge issue, I've seen reprints from Taiwan that appear to be legitimate for a fraction of the original price (they are soft cover, though, and the print quality is obviously not as good).
Mind you, I've got both The Handbook and Gonzalez & Woods, and I've found Gonzalez to be easier to digest during the initial stages. Rather than just reading, it is definitely recommended to attempt to reproduce all the examples that they give, and make an honest attempt at the exercises at the end of each chapter. The Handbook is great for coverage but lacks exercises.
Finally, your choice of must read really depends which specific direction you are expecting to be working in. The basic knowledge (spatial and frequency domain filtering, for example) has been around since the dawn of the field (early 60s) and is usually covered fairly well by most texts. If you want to learn about more recent applications, you have be a bit more specific (or go for The Handbook as it attempts to cover it all).
For contemporary readers viewing this question, an outstanding text is Prince's Computer Vision: Models, Learning, and Inference . The pdf is available free on that site.
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I picked up coding during quarantine and haven't gone a day without learning since. I've managed to learn the basics of Ruby in little under two weeks and run a few programs/started creating a basic app. Now I have to get a hang of Ruby on Rails. Furthermore, I have started learning Data Structures & Algorithms as a separate topic to complement the programming followed by Logic & Discrete Mathematics. I'm a very fast and curious learner and simply cannot just let a question be without knowing the solution to it (which led me to making my StackOverflow account).
Learning is always easier and more engaging when you have an enthusiastic and passionate person to guide you through a subject.
I was wondering if anyone knows where I can find a good one-to-one mentor that caters to an enthusiastic beginner programmer?
Alternatively, is there a recommended online forum, group or organisation that does the same thing?
Answers would be very much appreciated.
Ultimately there isn't really a great place for this yet, perhaps because there's a point at which developers no longer wish to be mentored rather than rely upon a mutual network. StackOverflow being an obvious example.
Coding Coach tries to tie mentors and mentees together, for free. In my experience it can be quite difficult to find active mentors on the platform though.
CodeMentor isn't free but also has a large number of active mentors.
RailsLink has a channel called beginners-and-mentors for small bits of advice.
It can be quite difficult to find someone willing to engage one-to-one when you're learning because, unfortunately, it's often quite boring for the mentor. For that reason, networking with peers is a great way to learn when starting out. It also means that one 'mentor' can help a collective with greater ease.
Try reaching out to people on Twitter or other social media and try to be helpful where you can too. Even if you're only one week in you're a week ahead of someone who's not tried at all.
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I have a problem related to computer vision and machine learning. Basically we are working on video surveillance system which will be trained to detect any suspicious activity like theft or shop lifting in stores.We are confused that is that will be able to solve this problem or not. We don't know that is it feasible or not. So kindly just suggest us something. Any help will be appreciated.
While I understand that Open CV is great for face-detection and usable for face-recognition, can it be used for analyzing "actions", s.a. the act of sitting, the act of lifting an object off the shelf ? If so, what are some of these algorithms I should dig deeper into ?
Are there other libraries (other than OpenCV) which need to be used for such tasks? Are there open-source libraries for the same?
What you are trying to achieve is currently a very active area in computer vision and machine learning research called Behaviour Analysis or Activity Detection. State of the art approaches can be found in journals like PAMI or conferences like CVPR or NIPS. As of today, it is nowhere near the performance you would require to build an automatic theft-detection system in the general case (i.e., any surveillance camera looking into any scene in any orientation). Behaviour Analysis is based on many underlying techniques, such as identifying the pose of people in images. Current research is still trying to figure out if there's a person in the picture and the position of its limbs in the general case.
Here's what might be feasible with the current research state: A system that help an operator focus on potential threats when cameras have a clear unobstructed view to a clear and mostly static environment (e.g., glass displays). An operator could therefore monitor many more cameras than before, because the system will automatically hide the cameras that clearly does not contain suspicious activity or movement.
To know more about current possibilities, I recommend you to check the literature (like this example), decompose the problem into subparts and leverage your priors (your a priori knowledge of the scene and people you're looking at) as much as possible.
By using object recognition (by helping deep learning) we can detect object and by using the data set of recorded object in the shop we can assess to the detailed (price) of that object. based on the number of objects and information about the object we can recognize the issue such as thrift in the counter.
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Disclaimer: although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I'll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I'll be able to reword it more precisely once I get a reaction.
So,
I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I'm reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.
I wonder if there're any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.
Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches:
https://www.coursera.org/course/ml
Also to get a broader picture I suggest coursera's DataSciense course:
https://www.coursera.org/course/datasci
Finally a good book is Mahout in action - it is more about solving practical matters with mahout and has lots of examples and case-studies.
I beleive after that you will have a better understanding of what you want to do next.
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Looking for APIs, methods, research, etc on the subject of deciding whether a tweet (a string, really) conveys a mood of danger.
For example:
Danger: "this house across the street is on fire!!
Not danger: "this girl is on fire! love this song"
There is little research done on the particular problem of detecting danger, but there are a few research papers describing methods to detect natural hazards. Your example is reminiscent of the title of one of them: Finding Fires with Twitter. Another research that you may find useful is Emergency Situation Awareness: Twitter Case Studies.
In general, however, the best approach to solve such a problem is through supervised classification, very similar to how sentiment analysis is (or rather, was, because there are more sophisticated machine learning paradigms like Deep Learning being applied nowadays) done.
The essence is to label documents (in your case, tweets) into "danger" and "not danger". This labeling is done by human experts. Ideally, they should be well versed in the language and the domain. So, using native English speakers who know the colloquialisms of Twitter would be perfect annotators for this task.
Once adequate number of documents have been labeled, the baseline (i.e. the basic approach) is usually achieved by creating n-gram word vectors as feature vectors, and running SVM. If you are not aware of machine learning details, please read up on them before doing this.
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Are there any libraries, in any language, out there to help identify and grab the images of people in a still photo? Something similar in effect to the way the Kinect can isolate users.
Thanks much!
I think it depends very much on the setup (e.g. simple bg. with decent lighting condition vs. random bg. with random lighting). If you can make life easier for yourself and isolate a few simpler use cases that would be great. Still there are other available method, look at the plethora of research around pedestrian detection for example.
One thing I did try and it works surprisingly well although computationally intensive is the Histogram of Gradient Orientations, implemented in OpenCV as the HoG descriptor. For a still photo this should produce decent results. You can have a look at the OpenCV sample. I also recommend having a look at Dramanan's excellent papers.
Long story short, thanks for years of inspiring research in computer vision, there are quite a few interesting options out there, it's up to how willing you are to go into detail. Still, regardless of how clever algorithms can be, I believe it's far more important to get a decent setup that allows simple and efficient solutions rather than complex solutions that try to cater for every possible situation. Goodluck!