RGB-D Images for Objects Recognition using 3D Point Clouds and RANSAC Plane Fitting

Jalal, Ahmad; Sarwar, Mansoor; Kim, Ki-Bum

doi:10.1109/ibcast51254.2021.9393166

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…By using 2D to 3D reconstruction, the creation of the virtual world and 3D objects is easier and takes very less time and effort. According to [4], using a 3D virtual world will reduce the gap between learning management system (LMS) and learning theories. The 3D virtual world also improves the interactions of people with the instructors.…”

Section: Introductionmentioning

confidence: 99%

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

Chelloug¹,

Alsuhibany²,

Alsufyani³

et al. 2023

Computers, Materials &Amp; Continua

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In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification. The major research areas of this field include object detection and object recognition. Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed. There are different phases of changes in the traditional system. Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks. Because human can easily perceive but making 3D using software will take time manually. Firstly, the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization. Secondly, the computer labs in schools are now more common than ever. Thirdly, online classes have become a reality. However, transferring to online education or e-learning is not without challenges. Therefore, we propose a method for improving the efficiency of e-learning. Our proposed system consists of twoand-a-half dimensional (2.5D) features extraction using machine learning and image processing. Then, these features are utilized to generate 3D mesh using ellipsoidal deformation method. After that, 3D bounding box estimation is applied. Our results show that there is a need to move to 3D virtual reality (VR) with haptic sensors in the field of e-learning for a better understanding of real-world objects. Thus, people will have more information as compared to the traditional or simple online education tools. We compare our result with the ShapeNet dataset to check the accuracy of our proposed method. Our proposed system achieved an accuracy of 90.77% on plane class, 85.72% on chair class, and car class have 72.14%. Mean accuracy of our method is 70.89%.

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Section: Introductionmentioning

confidence: 99%

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

Chelloug¹,

Alsuhibany²,

Alsufyani³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

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“…To develop robots capable of perceiving the environment as humans do, researchers have focused heavily on Scene Semantic Recognition (SSR), automated analysis of item placements [8] and structural connection between many objects in landscape photos. Improving the dependability of SSR in practical applications such as security navigation to automatically detect suspicious/violent situations [9], recognition of social interaction types in public settings, differentiating between diverse sports scenarios, and remote sensing still faces significant obstacles.…”

Section: Introductionmentioning

confidence: 99%

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

Sarwar¹,

Alatiyyah²,

Jalal³

et al. 2023

Computers, Materials &Amp; Continua

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In the last decade, there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras. They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios. Depth cues can improve the quality of object detection and recognition. The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets. The proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model (mWMM). mWMM is challenging to handle when the quality of the image is not good. Hence, the proposed RGB-D-based system uses depth cues for segmentation with the help of mWMM. Then it extracts multiple features from the segmented images. The selected features are fed to the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) for detecting objects. We achieved 92.13% of mean accuracy over NYUv1 dataset and 90.00% of mean accuracy for the Redweb_v1 dataset. Finally, their results are compared and the proposed model with CNN outperforms other state-of-the-art methods. The proposed architecture can be used in autonomous cars, traffic monitoring, and sports scenes.

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“…Một số hệ thống đạt được tốc độ cao bằng cách theo dõi từ frame này sang frame khác của hình ảnh nhưng rất khó khăn để có thể tái khởi tạo hình ảnh một cách nhanh chóng, như vậy không phải là tối ưu nhất. Trong nghiên cứu này, chúng tôi tập trung vào nhận dạng tư thế bàn tay và các bộ phận của bàn tay bằng cách theo dõi và phát hiện chuyển động của bàn tay từ các hình ảnh RGB-D[7][8][9] . Hình ảnh RGB-G là hình ảnh có 3 thông số về màu sắc là RED, GREEN, BLUE và thông số về DEPTH là khoảng cách từ điểm ảnh tới thiết bị thu hình.…”

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Applying machine learning for hand posture recognition from a series of depth images

Phạm¹,

Nguyễn

2022

Sci. Tech.Dev. J. - Eco. Law Manag.

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Image recognition one of the most difficult issue, is becoming an applied research in recent times. The possibility of allowing computers to recognize human’s hand posture in real-time is gradually becoming popular and involved in many fields. It is also integrated into so many products. Thus, this study proposes an approach for faster and more accurate image recognition. With the development of technology, this camera device has had many significant changes; the camera can capture both RGB images and images in depth. This makes identification in low-light conditions more accurate, which improves recognition performance. The Random Forest algorithm is evaluated to be more efficient, faster, and more accurate than previous recognition algorithms used for model testing. We collected about 10,000 hand image data samples, then trained and evaluated the model with nearly 80% accuracy results. In addition, the study presents some results of gesture recognition of human hands in real-time using information obtained from Kinect sensor; the model has the ability to train quite large data and the recognition speed is faster. Research results can be used in control applications serving in life and industrial production.

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RGB-D Images for Objects Recognition using 3D Point Clouds and RANSAC Plane Fitting

Cited by 5 publications

References 56 publications

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

Applying machine learning for hand posture recognition from a series of depth images

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