image-recognition

Does anyone know if you can use the OpenCV library within the processing Android template ? I want to do some image recognition/comparison for these devices within Processing. What are the means to do such? and does anyone have an example of source code for it? Thanks! jesses.co.tt The OpenCV library that you would normally use ( http://ubaa.net/shared/processing/opencv/ ) will not work for Android. However, Android has it's own port of OpenCV, which is available here: http://opencv.org/platforms/android.html That being said, it is a little tricky to setup itself vis-a-vis Processing,

I am very new in openCV, I saw it could figure out the face and return a rectangle to indicate the face. I am wondering whether there is anyway for openCV to access two images, with which contains one face, and I expect openCV to return the possibility of whether those two people are the same. Thanks. OpenCV does not provide a full face recognition engine. You might want to check out this work: The One-Shot Similarity Kernel which proposes something similar to what you need. It also provides Matlab code. 来源： https://stackoverflow.com/questions/4559332/can-i-use-opencv-to-compare-two-faces-on

I am trying to code a face-recognition program in Python (I am going to apply k-nn algorithm to classify). First of all, I converted the images into greyscale and then I created a long column vector (by using Opencv's imagedata function) with the image's pixels (128x128= 16384 features total) So I got a dataset like the following (last column is the class label and I only showed first 7 features of the dataset instead of 16384). 176, 176, 175, 175, 177, 173, 178, 1 162, 161, 167, 162, 167, 166, 166, 2 But when I apply k-nn to this dataset, I get awkward results. Do I need to apply additional

what approach would you recommend for finding obstacles in a 2D image? Here are some key points I came up with till now: I doubt I can use object recognition based on "database of obstacles" search, since I don't know what might the obstruction look like. I assume color recognition might be problematic if the path does not differ a lot from the object itself. Possibly, adding one more camera and computing a 3D image (like a Kinect does) would work, but that would not run as smooth as I require. To illustrate the problem; robot can ride either left or right side of the pavement. In the

Obviously this image is pretty tough as it is low clarity and is not a real word. However, with this code, I'm detecting nothing close: import pytesseract from PIL import Image, ImageEnhance, ImageFilter image_name = 'NedNoodleArms.jpg' im = Image.open(image_name) im = im.filter(ImageFilter.MedianFilter()) enhancer = ImageEnhance.Contrast(im) im = enhancer.enhance(2) im = im.convert('1') im.save(image_name) text = pytesseract.image_to_string(Image.open(image_name)) print(text) outputs , Mdfiaodfiamms Any ideas here? The image my contrasting function produces is: Which looks decent? I don't have

Project: Content Based Image Retrieval - Semi-supervised (manual tagging is done on images while training) Description I have 1000000 images in the database. The training is manual (supervised) - title and tags are provided for each image. Example: coke.jpg Title : Coke Tags : Coke, Can Using the images and tags, I have to train the system. After training, when I give a new image (already in database/ completely new) the system should output the possible tags the image may belong to and display few images belonging to each tag. The system may also say no match found. Questions: 1) What is mean