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.

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.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

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.

Object Recognition with a Limited Database Using Shape Space Theory

2012 ◽  
pp. 128-147
Author(s):  
Yuexing Han ◽  
Bing Wang ◽  
Hideki Koike ◽  
Masanori Idesawa
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Shape Space ◽  
Image Understanding ◽  
Data Models ◽  
Hard Work ◽  
Space Theory ◽  
Single Object ◽  
Intermediate Data ◽  
Computer Vision Applications

One of the main goals of image understanding and computer vision applications is to recognize an object from various images. Object recognition has been deeply developed for the last three decades, and a lot of approaches have been proposed. Generally, these methods of object recognition can successfully achieve their goal by relying on a large quantity of data. However, if the observed objects are shown to diverse configurations, it is difficult to recognize them with a limited database. One has to prepare enough data to exactly recognize one object with multi-configurations, and it is hard work to collect enough data only for a single object. In this chapter, the authors will introduce an approach to recognize objects with multi-configurations using the shape space theory. Firstly, two sets of landmarks are obtained from two objects in two-dimensional images. Secondly, the landmarks represented as two points are projected into a pre-shape space. Then, a series of new intermediate data can be obtained from data models in the pre-shape space. Finally, object recognition can be achieved in the shape space with the shape space theory.

scholarly journals A Comparative Analysis for 2D Object Recognition: A Case Study with Tactode Puzzle-Like Tiles

2021 ◽  
Vol 7 (4) ◽  
pp. 65
Author(s):  
Daniel Silva ◽  
Armando Sousa ◽  
Valter Costa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Object Recognition ◽  
Template Matching ◽  
Recognition Performance ◽  
Regions Of Interest ◽  
The Other ◽  
Classification Methods

Object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM; (ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSD and YOLOv4; (iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB; and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSD and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively; VGG19 with an accuracy of 99.96% and 99.68% for the same datasets; and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.

scholarly journals A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 7824-7835
Author(s):  
H. Alalawi ◽  
M. Alsuwat ◽  
H. Alhakami
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Future Research ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Research Directions ◽  
The World ◽  
Modern Methods ◽  
Future Research Directions ◽  
Class Labels

The importance of classification algorithms has increased in recent years. Classification is a branch of supervised learning with the goal of predicting class labels categorical of new cases. Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies. This paper gives an overview of classification algorithms to provide the readers with an understanding of the concept of the state-of-the-art classification algorithms and their applications used in the COVID-19 diagnosis and detection. It also describes some of the research published on classification algorithms, the existing gaps in the research, and future research directions. This article encourages both academics and machine learning learners to further strengthen the basis of classification methods.

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