OBJECT RECOGNITION BASED ON BAG OF FEATURES AND A NEW LOCAL PATTERN DESCRIPTOR

2014 ◽  
Vol 28 (08) ◽  
pp. 1455010 ◽  
Author(s):  
CAROLINA TOLEDO FERRAZ ◽  
OSMANDO PEREIRA ◽  
MARCOS VERDINI ROSA ◽  
ADILSON GONZAGA
Keyword(s):  
Object Recognition ◽  
Effective Means ◽  
Recognition System ◽  
Scale Invariant ◽  
Image Scaling ◽  
Bag Of Features ◽  
Image Patches ◽  
Image Pixels ◽  
Scale Invariant Feature ◽  
Local Image

Bag of Features (BoF) has gained a lot of interest in computer vision. Visual codebook based on robust appearance descriptors extracted from local image patches is an effective means of texture analysis and scene classification. This paper presents a new method for local feature description based on gray-level difference mapping called Mean Local Mapped Pattern (M-LMP). The proposed descriptor is robust to image scaling, rotation, illumination and partial viewpoint changes. The training set is composed of rotated and scaled images, with changes in illumination and view points. The test set is composed of rotated and scaled images. The proposed descriptor more effectively captures smaller differences of the image pixels than similar ones. In our experiments, we implemented an object recognition system based on the M-LMP and compared our results to the Center-Symmetric Local Binary Pattern (CS-LBP) and the Scale-Invariant Feature Transform (SIFT). The results for object classification were analyzed in a BoF methodology and show that our descriptor performs better compared to these two previously published methods.

A Contactless Palm Vein Recognition System Based on Principle Line SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3469-3472 ◽  
Author(s):  
Wei Wu ◽  
Sen Lin ◽  
Hui Song
Keyword(s):  
Traditional Method ◽  
Recognition System ◽  
Scale Invariant ◽  
Limited Effect ◽  
Image Deformation ◽  
Invariant Feature ◽  
Feature Transform ◽  
Vein Recognition ◽  
Palm Vein ◽  
Scale Invariant Feature

Compared with the traditional method of contact collection, contactless acquisition is the mainstream and trend of palm vein recognition. However, this method may lead to image deformation caused by no parallel of the palm plane and the sensor plane. In order to improve the limited effect of Scale Invariant Feature Transform (SIFT) about this problem, a better method of palm vein recognition which based on principle line SIFT is proposed. Based on the self-built database, this method is compared with the SIFT and other typical palm vein recognition methods, the experimental results show that our system can achieve the best performance.

scholarly journals Context-Dependent Object Proposal and Recognition

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1619
Author(s):  
Ray-I Chang ◽  
Chao-Lung Ting ◽  
Syuan-Yi Wu ◽  
Peng-Yeng Yin
Keyword(s):  
Object Recognition ◽  
Support Vector ◽  
Scale Invariant ◽  
Edge Image ◽  
Low Level ◽  
Automatic Driving ◽  
Object Proposal ◽  
Context Dependent ◽  
Scale Invariant Feature ◽  
Rule Out

Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.

scholarly journals SIFT Based Vein Recognition Models: Analysis and Improvement

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Guoqing Wang ◽  
Jun Wang
Keyword(s):  
Dynamic Range ◽  
State Of The Art ◽  
Recognition System ◽  
Negative Influence ◽  
Unified Framework ◽  
Scale Invariant ◽  
Hand Vein ◽  
Gradient Based ◽  
Vein Recognition ◽  
Scale Invariant Feature

Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR) and Equal Error Rate (EER). Rigorous experiments with state-of-the-art and other CE adopted in published SIFT based hand vein recognition system demonstrate the influence. What is more, an improved SIFT model by importing the kernel of RootSIFT and Mirror Match Strategy into a unified framework is proposed to make use of the positive keypoints change and make up for the negative influence brought by CE.

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