image-recognition | 易学教程

How many images to use for positive and negative samples when Haar training?

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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? Abid Rahman K 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. This link says 3000 for positive and 5000 for negative. Anyway, higher number of images improves

image to barcode recognition [closed]

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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 5 years ago . I know this has been discussed a few times on stackoverflow but I wasn't able to find a sufficient answer. For a project, I am seeking the best possible piece of software/api/library that can recognise barcode numbers from the input image. It will be used on mobile phones(iphone, windows mobile, android etc.) so

opencv matching edge images

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问题 I am working on the project and part of it is to recognize objects recorded on camera. So to be more specofic: I am using OpenCV I have correctly setup camera and am able to retrieve pictures from it I have compiled and experimented with number of demos from OpenCV I need a scale- AND rotation- invariant algorithm for detection Pictures of original objects are ONLY available as edge-images All feature detection/extraction/matching algorithms I have seen so far are working reasonably well with

Performance issues using BRISK detector/descriptor OpenCV

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问题 I'm experiencing a performance problem when I use BRISK for feature detection and description in OpenCV. Basically I try to match all the descriptors I get from this image: against all the descriptors I get from a database of images using flann based matcher with the LSH algorithm and BRISK for feature detection and description. My database of images is composed of 242 images. In this 242 images there are three images corresponding to each object taken separately in the above "complex" image

Train High Definition images with Tensorflow and inception V3 pre trained model

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I'm looking to do some image classification on PDF documents that I convert to images. I'm using tensorflow inception v3 pre trained model and trying to retrain the last layer with my own categories following the tensorflow tuto . I have ~1000 training images per category and only 4 categories. With 200k iterations I can reach up to 90% of successful classifications, which is not bad but still need some work: The issue here is this pre-trained model takes only 300*300p images for input. Obviously it messes up a lot with the characters involved in the features I try to recognize in the

Looking for a little python machine learning advice

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I'm interested in having a dabble with Python and machine learning/automatic data entry. However as my research has progressed I realise there are so many different techniques each with there own strengths. I've decided i might get further if i learn in the opposite direction. I.e. pick a problem/task and learn by solving/completing it. I occasionally have to data process invoices that are faxed, I'm hoping to make a program that can enter these for me once I've scanned then in. The faxes basically consist of 2 identical tables. Each row denotes a seperate worker. The 1st column is for a

Coin Recognition on Android

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问题 I am currently developing an Android application that is capable of taking an existing image of a coin, or using an in-built camera to scan a single coin (very much like Google Goggles does). I am using OpenCV for Android. My question is as follows: What method(s) would be most appropriate for performing Coin Recognition on Android using OpenCV? The method I have tried so far is below (maybe I'm doing it wrong, or I'm just going down the wrong route completely) I am currently using OpenCV for

OpenCV different approach on detecting go board

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问题 i am working on an Android app that will recognize a GO board and create a SGF file of it. i made a version that is able to detect a board and warp the perspective to make it square ( code and example image below) unfortunately it gets a bit harder when adding stones.(image below) Important things about a average go board: round black and white stones black lines on the board board color ranges from white to light brown and sometimes with a wood grain stones are placed on intersections of two

Recognizing text from a picture in delphi

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问题 i need an advice on how to approach this problem. I have some picture data: *.jpg, *.bmp ... and i need to extract the data from it. The data is alphanumeric text. I work in delphi. 回答1: You will have to head for a OCR (Optical Character Recognition) library. This is a pretty complex procedure, I believe you wouldn't be asking this question if you knew any way to implement this by yourself. A quick Google yielded this result, maybe it's of help for you: http://www.planet-source-code.com/vb

Searching a sub-array inside a 2D array (image recognition) [closed]

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Closed. This question is off-topic. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . Essentially, I have a numpy image array and I'm trying to find if it contains a 2x2 block of particular RGB pixel values. So, for example, if my (simplified) image array was something like: A B C D E F G H I J K L M N O P Q R S T U V W X I am trying to check if it contains, say: J K P Q I'm pretty new to numpy so I'd appreciate any help on this, thanks. How about this solution: 1) Identify all the