image-recognition

问题 I am trying to modify the network structure provided by Cifar10 in TensorFlow. Typically, I added another convolution layer (conv12) after the first convolution layer (conv1). No matter how I set the filter (I tried all 1x1, 3x3, 5x5) and whether using weight decay or not, having a new layer will decrease the accuracy to below than 10%. This is equivalent to a random guess in Cifar10 since there are 10 classes. The code structure is as following, I don't modify any other part of the cifar

问题 i am planning to create an application to compare pill images if they were the same. Is there any source that i could use that detects colors, shape, size of the pill in the picture and compre if that picture is the same with the other picture of the same pill? Regards, ZaldzBgz 回答1: I know a good library for computer vision, not sure if it can help you. You can look at: OpenCV. It worked with iPhone 来源： https://stackoverflow.com/questions/3474204/image-comparison-using-objective-c

问题 I have a set of points from a laser recognition of a sewer. This sewer has a hole in it. The main goal is to detect this hole and decide whether it's clean or not. One way to detect it is to find the hole and see if it is a circle (in which case it is clean) or a circle with the upper or bottom side straight (in which case it is dirty). There may be several solutions to obtain my main goal. I think that detecting this set of points that generate the hole and projecting them could be a good

问题 I have several images like this and trying to recognise objects using Neural Networks and GIST as features. My data set has 50 classes and 4 images per class. Using 75% of the images as training data, I get an test accuracy of 83% To improve the accuracy I want to preprocess the images I.e. I want to make the background transparent or white, while keeping the original object. I have been trying cv2.BackgroundSubtractorMOG2() but it's making the entire image gray. What will be the best way to

问题 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

问题 Does anyone know of a c++ library for taking an image and performing image recognition on it such that it can find letters based on a given font and/or font height? Even one that doesn't let you select a font would be nice (eg: readLetters(Image image). 回答1: I've been looking into this a lot lately. Your best is simply Tesseract. If you need layout analysis on top of the OCR than go with Ocropus (which in turn uses Tesseract to do the OCR). Layout analysis refers to being able to detect

问题 I am trying to train custom object classifier in Darknet YOLO v2 https://pjreddie.com/darknet/yolo/ I gathered a dataset for images most of them are 6000 x 4000 px and some lower resolutions as well. Do I need to resize the images before training to be squared ? I found that the config uses: [net] batch=64 subdivisions=8 height=416 width=416 channels=3 momentum=0.9 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 thats why I was wondering how to use it for different sizes of data

问题 I'm looking for an explanation / API doc / examples of how to use (and train?) Tesseract in C++, nothing useful on the google Tesseract page, and yet to find something over the web. Anyone useful sources, experiences would be more than welcome, as I have no idea how to begin with it. P.S: I'm open for suggestions on other libraries. Only FREE libraries 回答1: I have some experience with Tesseract... a simple google of 'training tesseract' reveals this page: http://code.google.com/p/tesseract

问题 I am new to image processing.As my project i am doing "image classifier using SVM".I have the idea of my final software "I select some image and give it as input to my software and it will classify that image .if i give the image of an animal it will classify it to cat or snake suitably" When I google about it.it says "First you need to train SVM" What it mean by Training SVM? What is the actual input to SVM in my case (image classification)? SVM is just a classifier how it classify images.Is

问题 I'm working on a project about recognizing moroccan license plates which look like this image : Moroccan License Plate Please how can I use OpenCV to cut the license plate out and Tesseract to read the numbers and arabic letter in the middle. I have looked into this research paper : https://www.researchgate.net/publication/323808469_Moroccan_License_Plate_recognition_using_a_hybrid_method_and_license_plate_features I have installed OpenCV and Tesseract for python in Windows 10. When I run the