Object recognition based on the region of interest and optical bag of words model

2013 ◽  
Author(s):  
Weisheng Li ◽  
Peng Dong
Keyword(s):  
Object Recognition ◽  
Region Of Interest ◽  
Bag Of Words

Object recognition based on the Region of Interest and optimal Bag of Words model

2016 ◽  
Vol 172 ◽  
pp. 271-280 ◽  
Author(s):  
Weisheng Li ◽  
Peng Dong ◽  
Bin Xiao ◽  
Lifang Zhou
Keyword(s):  
Object Recognition ◽  
Region Of Interest ◽  
Bag Of Words

A Study of Household Object Recognition Using SIFT-Based Bag-of-Words Dictionary and SVMs

2015 ◽  
pp. 573-580 ◽  
Author(s):  
Aadarsh Sampath ◽  
Aravind Sivaramakrishnan ◽  
Keshav Narayan ◽  
R. Aarthi
Keyword(s):  
Object Recognition ◽  
Bag Of Words

20Clustered Domain Colors and Bag of Words Algorithm Based Image Retrieval

2013 ◽  
Vol 321-324 ◽  
pp. 956-960 ◽  
Author(s):  
Lei Tang ◽  
Chang Sheng Zhou ◽  
Liang Zhang
Keyword(s):  
Object Recognition ◽  
Image Retrieval ◽  
Search Algorithm ◽  
Recognition Algorithm ◽  
Features Extraction ◽  
Bag Of Words ◽  
Semantic Features ◽  
Color Information ◽  
Visual Words ◽  
Detection And Recognition

Bag of words algorithm is an efficient object recognition algorithm based on semantic features extraction and expression. It learns the virtues of the text-based search algorithm to make images a range of visual words, extract the semantic characters and carry out the detection and recognition of interesting objects. Bag of words algorithm is extracted from gray images and discard s color information of images. We propose in this paper a method of image retrieval based on clustered domain colors and bag of words algorithm. The results of experiments show that this method can improve the precision of retrieval efficiently.

scholarly journals Fusing Color and Shape for Bag-of-Words Based Object Recognition

2013 ◽  
pp. 25-34 ◽  
Author(s):  
Joost van de Weijer ◽  
Fahad Shahbaz Khan
Keyword(s):  
Object Recognition ◽  
Bag Of Words

Research on Bioinformatics with Sparse Feature Fusion for Object Recognition

2013 ◽  
Vol 830 ◽  
pp. 485-489
Author(s):  
Shu Fang Wu ◽  
Jie Zhu ◽  
Zhao Feng Zhang
Keyword(s):  
Object Recognition ◽  
Sparse Coding ◽  
Feature Fusion ◽  
Image Representation ◽  
Experimental Results ◽  
Bag Of Words ◽  
Fusion Algorithm ◽  
Fusion Methods

Combining multiple bioinformatics such as shape and color is a challenging task in object recognition. Usually, we believe that if more different bioinformatics are considered in object recognition, then we could get better result. Bag-of-words-based image representation is one of the most relevant approaches; many feature fusion methods are based on this model. Sparse coding has attracted a considerable amount of attention in many domains. A novel sparse feature fusion algorithm is proposed to fuse multiple bioinformatics to represent the images. Experimental results show good performance of the proposed algorithm.

Creating the Bag-of-Words with Spatial Context Information for Image Retrieval

2014 ◽  
Vol 556-562 ◽  
pp. 4788-4791
Author(s):  
Zhen Wei Li ◽  
Jing Zhang ◽  
Xin Liu ◽  
Li Zhuo
Keyword(s):  
Image Retrieval ◽  
Region Of Interest ◽  
Focus Of Attention ◽  
Spatial Context ◽  
Image Feature ◽  
Context Information ◽  
Bag Of Words ◽  
Attention Shift ◽  
Spatial Coding ◽  
Attention Model

Recently bag-of-words (BoW) model as image feature has been widely used in content-based image retrieval. Most of existing approaches of creating BoW ignore the spatial context information. In order to better describe the image content, the BoW with spatial context information is created in this paper. Firstly, image’s regions of interest are detected and the focus of attention shift is produced through visual attention model. The color and SIFT features are extracted from the region of interest and BoW is created through cluster analysis method. Secondly, the spatial context information among objects in an image is generated by using the spatial coding method based on the focus of attention shift. Then the image is represented as the model of BoW with spatial context. Finally, the model of spatial context BoW is applied into image retrieval to evaluate the performance of the proposed method. Experimental results show the proposed method can effectively improve the accuracy of the image retrieval.

scholarly journals Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5098
Author(s):  
Nasim Hajari ◽  
Gabriel Lugo Bustillo ◽  
Harsh Sharma ◽  
Irene Cheng
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Template Matching ◽  
Low Cost ◽  
Region Of Interest ◽  
Depth Information ◽  
Natural Scenes ◽  
Proposed Model ◽  
Industrial Assembly ◽  
Rgb Images

The task of recognising an object and estimating its 6d pose in a scene has received considerable attention in recent years. The accessibility and low-cost of consumer RGB-D cameras, make object recognition and pose estimation feasible even for small industrial businesses. An example is the industrial assembly line, where a robotic arm should pick a small, textureless and mostly homogeneous object and place it in a designated location. Despite all the recent advancements of object recognition and pose estimation techniques in natural scenes, the problem remains challenging for industrial parts. In this paper, we present a framework to simultaneously recognise the object’s class and estimate its 6d pose from RGB-D data. The proposed model adapts a global approach, where an object and the Region of Interest (ROI) are first recognised from RGB images. The object’s pose is then estimated from the corresponding depth information. We train various classifiers based on extracted Histogram of Oriented Gradient (HOG) features to detect and recognize the objects. We then perform template matching on the point cloud based on surface normal and Fast Point Feature Histograms (FPFH) to estimate the pose of the object. Experimental results show that our system is quite efficient, accurate and robust to illumination and background changes, even for the challenging objects of Tless dataset.

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