feature-extraction

Simple speech recognition from scratch

由 前提是你 提交于 2019-12-09 23:57:13
问题 The most alike question I found related to my question is this (simple speech recognition methods) but since had passed 3 years and the answers are not enough I will ask. I want to compute, from scratch, a simple speech recognition system, I only need to recognize five words. As much as I know, the more used audio features for this application are the MFCC, and HMM for classification. I'm able to extract the MFCC from audio but I still have some doubts about how to use the features for

Robust tracking of blobs

由 别说谁变了你拦得住时间么 提交于 2019-12-09 09:46:13
问题 I have an image feature extraction problem. The input images are binary (black and white) and may contain blobs of approximately known area and aspect ratio. These need to be fit with ellipses using some best fit algorithm. Example input: Desired output: There may be multiple blobs (zero or more), the number is not known in advance. The approximate area and aspect ratio of all the blobs is known (and is the same). How many are in the image, their position, orientation and actual size are what

Problems during Skeletonization image for extracting contours

由 纵然是瞬间 提交于 2019-12-09 01:16:26
问题 I found this code to get a skeletonized image. I have a circle image (https://docs.google.com/file/d/0ByS6Z5WRz-h2RXdzVGtXUTlPSGc/edit?usp=sharing). img = cv2.imread(nomeimg,0) size = np.size(img) skel = np.zeros(img.shape,np.uint8) ret,img = cv2.threshold(img,127,255,0) element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3)) done = False while( not done): eroded = cv2.erode(img,element) temp = cv2.dilate(eroded,element) temp = cv2.subtract(img,temp) skel = cv2.bitwise_or(skel,temp) img =

How to improve features detection in opencv

由 家住魔仙堡 提交于 2019-12-07 17:07:11
问题 I am working on a project that I need to detect features on images using opencv. I am using SURF detector; SURF extractor; BFMatcher matcher; for detection, extraction and matching points. It works well for some images, but fails on some other images. For example, the system fails on this image: Apparently, this image has some texture and the feature detector should detect them, but no feature is detected and consequently no match is generated. How can I improve this feature detection? Can I

Merging CountVectorizer in Scikit-Learn feature extraction

由 你离开我真会死。 提交于 2019-12-07 12:55:11
问题 I am new to scikit-learn and needed some help with something that I have been working on. I am trying to classify two types of documents (say, type A and type B) using Multinomial Naive Bayes classification. In order to get the term counts for these documents, I am using the CountVectorizer class in sklearn.feature_extraction.text. The problem is that the two types of documents require different regular expressions to extract tokens (token_pattern parameter to CountVectorization). I can't

How to improve features detection in opencv

由 a 夏天 提交于 2019-12-06 02:43:10
I am working on a project that I need to detect features on images using opencv. I am using SURF detector; SURF extractor; BFMatcher matcher; for detection, extraction and matching points. It works well for some images, but fails on some other images. For example, the system fails on this image: Apparently, this image has some texture and the feature detector should detect them, but no feature is detected and consequently no match is generated. How can I improve this feature detection? Can I use any image processing technique for this? Is there any other detector that I can use which help in

Feature Extraction with Javascript

由 青春壹個敷衍的年華 提交于 2019-12-05 23:29:34
问题 I am wondering whether there is any open source or free library for Image feature extraction with Javascript? I am developing an app where I need to use an algorithm like SIFT. It is tough to implement in JS, and I couldn't find a good SIFT implementation in JS. I thought of implementing a feature extraction library in JS if one doesn't exist. Please can anybody help me to find a good solution or guide me to write one from scratch. Thanks, Keshan. 回答1: I'm not familiar with implementation for

Merging CountVectorizer in Scikit-Learn feature extraction

由 大城市里の小女人 提交于 2019-12-05 22:04:55
I am new to scikit-learn and needed some help with something that I have been working on. I am trying to classify two types of documents (say, type A and type B) using Multinomial Naive Bayes classification. In order to get the term counts for these documents, I am using the CountVectorizer class in sklearn.feature_extraction.text. The problem is that the two types of documents require different regular expressions to extract tokens (token_pattern parameter to CountVectorization). I can't seem to find a way to first load the training documents of type A and then of type B. Is it possible to do

Uniform LBP with scikit-image local_binary_pattern function

由 久未见 提交于 2019-12-05 08:22:40
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 2.19800000e+04] [ 7.00000000e+00 2.46530000e+04] [ 8.00000000e+00 2.76230000e+04] [ 9.00000000e+00 4

Bag of Visual Words in Opencv

由 試著忘記壹切 提交于 2019-12-05 02:36:45
问题 I am using BOW in opencv for clustering the features of variable size. However one thing is not clear from the documentation of the opencv and also i am unable to find the reason for this question: assume: dictionary size = 100. I use surf to compute the features, and each image has variable size descriptors e.g.: 128 x 34, 128 x 63, etc. Now in BOW each of them are clustered and I get a fixed descriptor size of 128 x 100 for a image. I know 100 is the cluster center created using kmeans
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