3D computer vision based on machine learning with deep neural networks: A review

Bhowmik, Achintya K.

doi:10.1002/jsid.617

Cited by 32 publications

(11 citation statements)

References 50 publications

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“…Machine learning (ML) approaches, which use pattern recognition algorithms to discern mathematical relationships between empirical observations, have been widely applied in the field of biomedicine, such as medical image classification, protein secondary structure prediction, drug repositioning, drug design, etc. In recent years, a more data‐hungry ML algorithm, deep learning (DL), which has gained great success in a wide variety of applications, such as computer vision, speech recognition, computer games, and natural language processing, has also attracted considerable interest from computational chemists and medicinal chemists. Up to now, various reviews related to the applications of ML or DL in drug design and discovery have been published .…”

Section: Introductionmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

186

180

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Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy highly depends on the reliability of scoring functions (SFs). With the rapid development of machine learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and virtual screening, and most of them have shown significantly better performance than a wide range of classical SFs. Emergence of more data-hungry deep learning (DL) approaches in recent years further fascinates the exploitation of more accurate SFs. Here, we summarize the progress of traditional ML-based SFs in the last few years and provide insights into recently developed DL-based SFs. We believe that the continuous improvement in ML-based SFs can surely guide the early-stage drug design and accelerate the discovery of new drugs. This article is categorized under:Computer and Information Science > Chemoinformaticsdeep learning, machine learning, molecular docking, scoring function, structure-based drug design | INTRODUCTIONTraditional drug discovery largely relies on the application of high-throughput screening, an experimental technique with acceptable performance but high cost and low efficiency. 1 With the rapid development of computational chemistry and computer technology, computer-aided drug design (CADD) has gradually emerged as a powerful technique in the design and development of new drug candidates in the past three decades. 2 Virtual screening (VS), an important branch of CADD, can enrich potential actives from large virtual compound libraries through in silico methods rather than real experiments, which can not only accelerate the process of drug discovery but also greatly reduce the time and resource cost. 3-5 Depending on whether the three-dimensional (3D) structure of a target is used or not, VS approaches can be classified into two major categories: ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS). 6 LBVS aims to discover active molecules through the models developed based on a set of known ligands of a target of interest, which may limit its capability to find novel chemotypes. Compared with LBVS, SBVS is considered to be a better choice to discover novel active compounds if the 3D structure of a given target is available. 7 Chao Shen and Junjie Ding are equivalent first authors.

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Section: Introductionmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

186

180

View full text Add to dashboard Cite

show abstract

“…In view of the difficulty in determining and measuring ceramic fragments by traditional archaeological methods, a dimensional analysis method based on the geometric and morphological characteristics of fragments was proposed to assist the restoration of ceramic fragments [23]. Through computer coding ceramic structure data [24], the Chinese ceramic structure database can be initially seen, but the sample size is small at present, which needs further improvement. The digital representation of the shape characteristics of skimming bowls was analyzed, and the structural information was extracted to provide auxiliary information for ceramic recognition and value recognition [25].…”

Section: Related Workmentioning

confidence: 99%

Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

Zhang

Liu

2021

Advances in Mathematical Physics

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Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.

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“…And he also verified the accuracy of the proposed method by comparing with other algorithms (as Table 2). Vodrahalli and Bhowmik [42] summarized studies related to the computer vision based on deep neural network (DNN), and concluded that the information fusion of the color and depth images based on DNN can improve the accuracy of the target recognition, but the execution efficiency of this method is still an open problem for the visual system with the requirement of high real time performance.…”

Section: B Target Recognitionmentioning

confidence: 99%

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

Zhang

Liu

et al. 2019

IEEE Access

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In most existing bolt assembling operations, the human-machine collaboration (HMC) is used to guarantee both the operational efficiency and the connection quality. However, the huge labor intensity and costs are still needed when there exist bolts with huge quantity and multiple models in some special working environments, for example locomotive maintenance and automobile assembly. Actually, the robotic application can better solve the above problem. The main reason why this application is limited in the bolt assembly is due to the technological barriers related to the intelligent control. This paper firstly introduces the HMC-based and the robot-guided bolt assembly processes in detail, then summarizes the latest researches related to the significant technologies needed in the robot-guided assembly to show their present status and future trends. Finally based on the technology limitations this paper further presents some suggestions for future studies of each technology, which attempts to help readers broaden their research thoughts and speed up the development of the robot-guided bolt assembly.INDEX TERMS Automatic bolt assembly, robotic application, technological barrier, intelligent control.

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3D computer vision based on machine learning with deep neural networks: A review

Cited by 32 publications

References 50 publications

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

A Review on Significant Technologies Related to the Robot-Guided Intelligent Bolt Assembly Under Complex or Uncertain Working Conditions

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