scholarly journals Selecting Discriminative Binary Patterns for a Local Feature

2015 ◽  
Vol 15 (3) ◽  
pp. 104-113
Author(s):  
Yingying Li ◽  
Jieqing Tan ◽  
Jinqin Zhong
Keyword(s):  
Object Recognition ◽  
Image Matching ◽  
State Of The Art ◽  
Local Feature ◽  
High Dimensionality ◽  
Learning Method ◽  
Local Descriptor ◽  
Local Descriptors

Abstract The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

scholarly journals Affine Shape Descriptor Algorithm for Image Matching and Object Recognition in a Digital Image/Algoritmo para clasificación de formas invariante a transformaciones afines para objetos rígidos en una imagen digital

2017 ◽  
Vol 6 (11) ◽  
pp. 13
Author(s):  
Jacob Morales G. ◽  
Nancy Arana D. ◽  
Alberto A. Gallegos
Keyword(s):  
Comparative Analysis ◽  
Object Recognition ◽  
Digital Image ◽  
Image Matching ◽  
State Of The Art ◽  
Shape Descriptor ◽  
Correct Classification ◽  
Affine Transformations ◽  
Affine Shape ◽  
Object Classes

The use of shape, as a mean to discriminate between object classes extracted from a digital image, is one of the major roles in machine vision. The use of shape has been studied extensively in recent decades, because the shape of the object holds enough information for its correct classification; additionally, the quantity of memory used to store a border is much less than that of the whole region within it. In this paper, a novel shape descriptor is proposed. The algorithm demonstrates that it has useful properties such as: invariance to affine transformations that are applied to the border (e.g., scales, skews, displacements and rotations), stability in the presence of noise, and good differentiability between different object classes. A comparative analysis is included to show the performance of our proposal with respect to the state of the art algorithms.

scholarly journals Keypoint-Less, Heuristic Application of Local 3D Descriptors

2017 ◽  
Vol 42 (3) ◽  
pp. 239-255
Author(s):  
Bogdan Harasymowicz-Boggio ◽  
Łukasz Chechliński
Keyword(s):  
Object Recognition ◽  
State Of The Art ◽  
Massively Parallel ◽  
Classification Methods ◽  
Single Object ◽  
Local Descriptors ◽  
Indoor Scenes ◽  
Full Power ◽  
3D Descriptors ◽  
Hierarchical Manner

AbstractOne of the most important topics in the research concerning 3D local descriptors is computational efficiency. The state-of-the-art approach addressing this matter consists in using keypoint detectors that effectively limit the number of points for which the descriptors are computed. However, the choice of keypoints is not trivial and might have negative implications, such as the omission of relevant areas. Instead, focusing on the task of single object detection, we propose a keypoint-less approach to attention focusing in which the full scene is processed in a hierarchical manner: weaker, less rejective and faster classification methods are used as heuristics for increasingly robust descriptors, which allows to use more demanding algorithms at the top level of the hierarchy. We have developed a massively-parallel, open source object recognition framework, which we use to explore the proposed method on demanding, realistic indoor scenes, applying the full power available in modern computers.

Multi‐image matching for object recognition

2018 ◽  
Vol 12 (3) ◽  
pp. 350-356 ◽  
Author(s):  
Jie Zhu ◽  
Shufang Wu ◽  
Xizhao Wang ◽  
Guoqing Yang ◽  
Liyan Ma
Keyword(s):  
Object Recognition ◽  
Image Matching

scholarly journals Contact Lens Classification by Using Segmented Lens Boundary Features

2018 ◽  
Vol 11 (3) ◽  
pp. 1129
Author(s):  
Nur Ariffin Mohd Zin ◽  
Hishammuddin Asmuni ◽  
Haza Nuzly Abdul Hamed ◽  
Razib M. Othman ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Contact Lens ◽  
State Of The Art ◽  
Classification Method ◽  
Support Vector ◽  
Local Descriptors ◽  
Iris Image ◽  
Vector Machines ◽  
False Reject Rate ◽  
Better Than

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

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