feature-extraction

问题 I am trying to extract different point descriptors (SIFT, SURF, ORB, BRIEF,...) to build Bag of Visual words. The problem seems to be that I am using very small images : 12x60px. Using a dense extractor I am able to get some keypoints, but then when extracting the descriptor no data is extracted. Here is the code : vector<KeyPoint> points; Mat descriptor; // descriptor of the current image Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("BRIEF"); Ptr<FeatureDetector> detector

问题 I am gathering data from X, Y and Z accelerometer sensors sampled at 200 Hz. The 3 axis are combined into a single signal called 'XYZ_Acc'. I followed tutorials on how to transform time domain signal into frequency domain using scipy fftpack library. The code I'm using is the below: from scipy.fftpack import fft # get a 500ms slice from dataframe sample500ms = df.loc[pd.to_datetime('2019-12-15 11:01:31.000'):pd.to_datetime('2019-12-15 11:01:31.495')]['XYZ_Acc'] f_s = 200 # sensor sampling

问题 I'm using the local_binary_pattern from skimage.feature with uniform mode like this: >>> from skimage.feature import local_binary_pattern >>> lbp_image=local_binary_pattern(some_grayscale_image,8,2,method='uniform') >>> histogram=scipy.stats.itemfreq(lbp_image) >>> print histogram [[ 0.00000000e+00 1.57210000e+04] [ 1.00000000e+00 1.86520000e+04] [ 2.00000000e+00 2.38530000e+04] [ 3.00000000e+00 3.23200000e+04] [ 4.00000000e+00 3.93960000e+04] [ 5.00000000e+00 3.13570000e+04] [ 6.00000000e+00

问题 I am trying to extract pitch features from an audio file which I would use for a classification problem. I am using python(scipy/numpy) for classification. I think I can get frequency features using scipy.fft but I don't know how to approximate musical notes using frequencies. I researched a bit and found that I need to get chroma features which map frequencies to 12 bins for notes of a chromatic scale. I think there's a chroma toolbox for matlab but I don't think there's anything similiar

问题 It is the result of GLCM matrix. What is the meaning of black horizontal and vertical lines in GLCM image? Are they a problem? N = numel(unique(img)); % img is uint8 glcm = graycomatrix(img, 'NumLevels', N); imshow(glcm) 回答1: I suspect this is the problem: For the function graycomatrix , You have supplied a 'NumLevels' argument which is larger than the number of unique graylevels in your image. For instance, a 256-level (8-bit) image will have only 256 graylevels. Asking for 1000 levels in

问题 I am working on SIFT features and 'm using a visual bag-of-words approach to make a vocabulary first and then do the matching. I've found similar questions but didn't find the appropriate answer. Same question is asked in below link but there is no satisfactory answer, can anyone help me. Thank u in advance. https://stackoverflow.com/questions/29366944/finding-top-similar-images-from-a-database-using-sift-surf 回答1: Sift and Surf Method are all implemented in lire project and ready to use.

问题 This question is an attempt to reframe this question to make it clearer. This slide shows an equation for Q(state, action) in terms of a set of weights and feature functions. These discussions (The Basic Update Rule and Linear Value Function Approximation) show a set of weights for each action. The reason they are different is that the first slide assumes you can anticipate the result of performing an action and then find features for the resulting states. (Note that the feature functions are

问题 What are the steps in feature-based registration? If I have a transformed image matching to a stack of images (of which only one is the best match), what are the steps in registration? Once I find matches and use them to estimate a transform, what's usually the next step? 回答1: Find points/areas which are invariant to different kind of transformations like: translation, rotation, scale.. because this points are invariant to those transformation we can find them in the different images that we

问题 Hi I have been searching though research papers on what features would be good for me to use in my handwritten OCR classifying neural network. I am a beginner so I have been just taking the image of the handwritten character, made a bounding box around it, and then resize it into a 15x20 binary image. So this means i have an input layer of 300 features. From the papers i have found on google (most of which are quite old) the methods really vary. My accuracy is not bad with just a binary grid

问题 This is a follow up question of Extracting rows containing specific value using mapReduce and hadoop Mapper function public static class MapForWordCount extends Mapper<Object, Text, Text, IntWritable>{ private IntWritable saleValue = new IntWritable(); private Text rangeValue = new Text(); public void map(Object key, Text value, Context con) throws IOException, InterruptedException { String line = value.toString(); String[] words = line.split(","); for(String word: words ) { if(words[3]