image-recognition

问题 I have read a fair amount about Haar training and I'm not clear on how many images one should use for the positive and negative sample sets. I see it recommended to use many images, some people recommend thousands. I'm also unclear of whether the number of positive and negative sample images should be the same? 回答1: Here is the best tutorial on Haar training. Have you tried this? http://note.sonots.com/SciSoftware/haartraining.html It says they used 5000 for positive and 3000 for negative.

问题 I know tl;dr; I'll try to explain my problem without bothering you with ton's of crappy code. I'm working on a school assignment. We have pictures of smurfs and we have to find them with foreground background analysis. I have a Decision Tree in java that has all the data (HSV histograms) 1 one single node. Then tries to find the best attribute (from the histogram data) to split the tree on. Then executes the split and creates a left and a right sub tree with the data split over both node

问题 I will need to identify, rotate and crop a rectangle(business card) from a photo took from an iphone. I believe this can be done by using OpenCV, but I have not used it before. Could anyone give some tips on this? 回答1: See opencv sample code OpenCV2.2\samples\cpp\squares.cpp . They do the following: Detect edges using Canny Retrieve contours by findContours For each contour approximate contour using approxPolyDP to decrease number of vertices if contour has 4 vertices and each angle is ~90

问题 Right now I'm trying to create digit recognition system using OpenCV. There are many articles and examples in WEB (and even on StackOverflow). I decided to use KNN classifier because this solution is the most popular in WEB. I found a database of handwritten digits with a training set of 60k examples and with error rate less than 5%. I used this tutorial as an example of how to work with this database using OpenCV. I'm using exactly same technique and on test data ( t10k-images.idx3-ubyte ) I

问题 Sadly, the face.com API is being shut down due acquisition by Facebook. Are there any decent alternatives out there? I'm looking to check for a given image if there is a face in it + demographics content about it. 回答1: Sad news indeed. If you have a good reason and already have an account, they may extend you until October, 2012: http://developers.face.com/extension-request/ For everyone else, here's a list of potential alternatives I've compiled: OpenCV: http://opencv.willowgarage.com/ (C/C+

问题 I'm not sure what this error means. This error occurs when I try to calculate acc : acc = accuracy.eval(feed_dict = {x: batch_images, y: batch_labels, keep_prob: 1.0}) I've tried looking up solutions, but I couldn't find any online. Any ideas on what's causing my error? Here's a link to my full code. 回答1: I had a similar error but the problem for me was that I was trying to use argmax on a 1 dimensional vector. So the shape of my label was (50,) and I was trying to do a tf.argmax(y,1) on that

问题 I've programmed a solution in Python which worked great, but required several libraries to install and a lot of burocratic setup to work. I've decided to build it with a GUI in C# on Visual Studio Community 2017 but in the first successful function the result was way slower than in Python. Which IMO it should actually be faster. The code essentially is just doing a needle in a haystack image search, by getting all images from a folder and testing each needle (total 60 images) in a haystack,

问题 I've been searching for resources for number recognition in images on the web. I found many links providing lots of resources on that topic. But unfortunately it's more confusing than helping, I don't know where to start. I've got an image with 5 numbers in it, non-disturbed (no captcha or something like this). The numbers are black on a white background, written in a standard font. My first step was to separate the numbers. The algorithm I currently use is quite simple, it just checks if a

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 3 years ago . I need to make an iOS app with these features: Use the camera capture a image. Recognize that image: Does it match with a sample image or not? Is there any online API to do that (from Google, Yahoo, ...)? For example, can I upload an image and I get an image URL and after that request a url to compare a new

问题 hi I am building a image classifier for one-class classification in which i've used autoencoder while running this model I am getting this error (ValueError: Layer conv2d_3 was called with an input that isn't a symbolic tensor. Received type: . Full input: [(128, 128, 3)]. All inputs to the layer should be tensors.) num_of_samples = img_data.shape[0] labels = np.ones((num_of_samples,),dtype='int64') labels[0:376]=0 names = ['cat'] Y = np_utils.to_categorical(labels, num_class) input_shape=img