19 skills found
nourani / LBPC++ implementation of the Local Binary Pattern texture descriptors. This class integrates with OpenCV and FFTW3 to bring a complete and fast implementation of the popular descriptors: LBP u2, ri, riu2 & hf. The routines for calculating these descriptors are inspired by the Matlab code of the original authors.
bikz05 / Texture MatchingTexture Matching using Local Binary Patterns (LBP)
Ashwani21 / Local Texture DescriptorsMatlab implementation, comparision and improvement of Local texture descriptors. This repo demonstrate usage of Local binary pattern (LBP), Local derivative pattern (LDP), Local Tetra pattern (LTrP), Noise Resistant LBP (NR-LBP), Histogram Refinement of Local texture descriptor for Content based image retrieval (CBIR) application.
dakshayh / Face Spoofing DetectionDeep Texture feature extraction and implementing Local Binary Pattern(LBP)-based Convolutional Neural Network
carolinepacheco / Xcs LbpAn eXtended Center-Symmetric Local Binary Pattern (XCS-LBP) descriptor for background modeling and subtraction in videos.
timvandermeij / Lbp.pyPython implementation of the local binary patterns (LBP) algorithm
adityajain10 / Human Detection Using Hog Lbp Neural NetworksThe program uses HOG and LBP features to detect human in images. First, use the HOG feature only to detect humans. Next, combine the HOG feature with the LBP feature to form an augmented feature (HOG-LBP) to detect human. A Two-Layer Perceptron (feedforward neural network) will be used to classify the input feature vector into human or no-human.
vivien-yang / Calorie Estimation Of Fast FoodThe Calorie Estimation Project can be mainly divided into two parts, identifying food from image, and estimating calorie from certain food image. For the identification of food image, we performed multi-class SVM algorithms, with different features explored and compared, including HOG (Histogram of Gradients), LBP (Linear Binary Pattern) and CNN. The result shows that the local feature LBP performs the best overall. The food calorie data from Internet is collected to conclude a table for easy conversion from food category to calorie.
lcit / Ext3DLBPExtended three-dimensional rotation invariant local binary patterns (LBP), Image and Vision Computing (2017)
PUTvision / Decision TreePython program for training the decision tree for people detection (based on LBP - Local Binary Patterns) and converting the tree to hardware (FPGA) implementation.
nasibehm / LBP3DA module that can extract LBP features (local binary pattern) from 3D images. Can be used for extracting features from medical images.
MIPT-Oulu / LocalBinaryPatternCalculates Median robust extended local binary pattern (MRELBP) or local binary pattern (LBP) from images.
shreyas-dharanesh / Fingerprint Spoof Detector Based On Local Binary PatternsA two-class fingerprint spoof detector that uses Local Binary Patterns (LBP) features along with Support Vector Machines (SVM) to distinguish live fingerprints images from spoof samples.
VL97 / Sand Dune Detection And Delineation On MARSThis project utilises ML based classifier and segmentation (U-NET) techniques to work upon texture information extracted through Local Binary Pattern(LBP) of extraterrestrial dune field images. The aim is to detect and delineate such sand dunes in a given dune field.
shashank140195 / LBP HOG SVM Feature ExtractionTo develop a two-class fingerprint spoof detector that uses Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features along with Support Vector Machines (SVM) to distinguish live fingerprints images from spoof samples
welintonandrey / 3d DenoisingA NEW denoising method that combines 3D Non-Local Means, LBP-TOP (local binary patterns on three orthogonal planes) and MSB (Most Significant Bit)
rezasputra / Klasifikasi Ekspresi WajahProject ini dibuat untuk memenuhi syarat meraih gelar Sarjana Komputer, Dengan melakukan Klasifikasi Ekspresi Wajah Manusia menggunakan algoritme Local Binary Pattern (LBP) untuk ekstraksi fitur dan Support Vector Machine untuk klasifikasi.
afaq-ahmad / MNIST English Handwritten Digits And Cifar10 ClassificationHuman written styles vary person to person even for a single letter or a digit. But there is some similarity in those digits and some unique features that help human to understand it by visualizing different digits. In this we are performing these digits classification, so machine can learn from it and recognized unique features and layout of individual digits. We have applied Histogram-Of-Oriented-Gradients (HOG) and Local Binary Pattern (LBP) on the images to extract features of digits. We used Support vector machine (SVM), K-Nearest Neighbors (KNN) and Neural Network Supervised Algorithms for digits classification. All these algorithms have some pros and cons like time consumption, dataset length and Accuracy. We have received 98% accuracy of classification using Neural Network of 3 layers.
graceugochinneji / LBPLocal Binary Pattern (LBP) implementation on single and multiple images
