3D Printing Errors Detection During the Process by Florin-Bogdan MARIN, Mihaela MARIN

“…In this paper, a computer vision algorithm able to detect specific errors is proposed.…”
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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Muhammad Sajid, Kaleem Razzaq Malik, Ateeq Ur Rehman, Tauqeer Safdar Malik, Masoud Alajmi, Ali Haider Khan, Amir Haider, Seada Hussen

“…Masking autoencoding is a promising self-supervised learning approach that greatly advances computer vision and natural language processing. For robust 2D representations, pre-training with large image data has become standard practice. …”
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Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…Pengecekan secara manual untuk mendeteksi wajah yang tidak menggunakan masker adalah pekerjaan yang lama dan melelahkan. Computer vision merupakan salah satu cabang ilmu komputer yang dapat digunakan untuk klasifikasi citra. …”
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Determination of the melanin and anthocyanin content in barley grains by digital image analysis using machine learning methods by E. G. Komyshev, M. A. Genaev, I. D. Busov, M. V. Kozhekin, N. V. Artemenko, A.  Y. Glagoleva, V. S. Koval, D. A. Afonnikov

“…Four models based on computer vision techniques and convolutional neural networks of different architectures were developed to predict grain pigment composition from images. …”
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Efficient Method for Robust Backdoor Detection and Removal in Feature Space Using Clean Data by Donik Vrsnak, Marko Subasic, Sven Loncaric

“…These methods target different areas, such as computer vision (CV), natural language processing (NLP), and thus utilize different assumptions about the nature of the input data and the type of backdoor trigger used in the attack. …”
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Kernelized correlation tracking based on point trajectories by Yunqiu LYU, Kai LIU, Fei CHENG

“…Visual tracking is one of the most important directions in computer vision.However,many state-of-the-art algorithms cannot track the interested object reliably due to occlusion during tracking process,which leads to deficiency of object information.In order to solve occlusion problem,a kernelized correlation tracking method based on point trajectories was proposed.Through analyzing long-term motion cues of the local information,point trajectories were labeled by spectral clustering.These labeled points were used to differentiate the foreground and background objects and thus detect whether the target was occluded or drifts.If drifting and occlusion occur,re-detection was used to detect the re-entering of the target.Experimental results show that the proposed algorithm can handle occlusion and drifting problems effectively.…”
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Development of a multi-object tracking algorithm with untrained features of object matching by V.A. Gorbachev, V.F. Kalugin

“…The problem of multiple object tracking is one of the most difficult tasks in computer vision. The article is devoted to a task of multiple object tracking on video footage received from an unmanned aerial vehicle. …”
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Big Data Deep Learning: Challenges and Perspectives by Xue-Wen Chen, Xiaotong Lin

“…It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. …”
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Automatic Cell Segmentation in Cyto- and Histometry Using Dominant Contour Feature Points by U. Pal, Karsten Rodenacker, B. B. Chaudhuri

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ARTigo: Data from Social Tagging with Art-historical Images by Stefanie Schneider

“…The annotations serve to improve the accessibility of art-historical images and offer vast research potential well beyond their utility as training datasets for Computer Vision (CV) algorithms.…”
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Machine learning security and privacy:a survey by Lei SONG, Chunguang MA, Guanghan DUAN

“…As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and privacy issues which are getting more and more attention.Firstly,the adversary model was described according to machine learning.Secondly,the common security threats in machine learning was summarized,such as poisoning attacks,adversarial attacks,oracle attacks,and major defense methods such as regularization,adversarial training,and defense distillation.Then,privacy issues such were summarized as stealing training data,reverse attacks,and membership tests,as well as privacy protection technologies such as differential privacy and homomorphic encryption.Finally,the urgent problems and development direction were given in this field.…”
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Akuisisi Foreground dan Background Berbasis Fitur DTC pada Matting Citra secara Otomatis by Meidya Koeshardianto, Eko Mulyanto Yuniarno, Mochamad Hariadi

“… Teknik pemisahan foreground dari background pada citra statis merupakan penelitian yang sangat diperlukan dalam computer vision. Teknik yang sering digunakan adalah image segmentation, namun hasil ekstraksinya masih kurang akurat. …”
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Research on structure and defense of adversarial example in deep learning by Guanghan DUAN, Chunguang MA, Lei SONG, Peng WU

“…With the further promotion of deep learning technology in the fields of computer vision,network security and natural language processing,which has gradually exposed certain security risks.Existing deep learning algorithms can not effectively describe the essential characteristics of data or its inherent causal relationship.When the algorithm faces malicious input,it often fails to give correct judgment results.Based on the current security threats of deep learning,the adversarial example problem and its characteristics in deep learning applications were introduced,hypotheses on the existence of adversarial examples were summarized,classic adversarial example construction methods were reviewed and recent research status in different scenarios were summarized,several defense techniques in different processes were compared,and finally the development trend of adversarial example research were forecasted.…”
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Parallel Processor for 3D Recovery from Optical Flow by Jose Hugo Barron-Zambrano, Fernando Martin del Campo-Ramirez, Miguel Arias-Estrada

“…3D recovery from motion has received a major effort in computer vision systems in the recent years. The main problem lies in the number of operations and memory accesses to be performed by the majority of the existing techniques when translated to hardware or software implementations. …”
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A survey of efficient deep neural network by Rui MIN

“…Recently,deep neural network (DNN) has achieved great success in the field of AI such as computer vision and natural language processing.Thanks to a deeper and larger network structure,DNN’s performance is rapidly increasing.However,deeper and lager deep neural networks require huge computational and memory resources.In some resource-constrained scenarios,it is difficult to deploy large neural network models.How to design a lightweight and efficient deep neural network to accelerate its running speed on embedded devices is a great research hotspot for advancing deep neural network technology.The research methods and work of representative high-efficiency deep neural networks in recent years were reviewed and summarized,including parameter pruning,model quantification,knowledge distillation,network search and quantification.Also,vadvantages and disadvantages of different methods as well as applicable scenarios were analyzed,and the future development trend of efficient neural network design was forecasted.…”
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A survey of neural architecture search by Mingjie HE, Jie ZHANG, Shiguang SHAN

“…Recently,deep learning has achieved impressive success on various computer vision tasks.The neural architecture is usually a key factor which directly determines the performance of the deep learning algorithm.The automated neural architecture search methods have attracted more and more attentions in recent years.The neural architecture search is the automated process of seeking the optimal neural architecture for specific tasks.Currently,the neural architecture search methods have shown great potential in exploring high-performance and high-efficiency neural architectures.In this paper,a survey in this research field and categorize existing methods based on their performance estimation methods,search spaces and architecture search strategies were presented.Specifically,there were four performance estimation methods for computation cost reduction,two typical neural architecture search spaces and two types of search strategies based on discrete and continuous spaces respectively.Neural architecture search methods based on continuous space are becoming the trend of researches on neural architecture search.…”
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The Tangibilization of Indigenous Dances and the Rehearsal of a Similarity Model for Quantitative Analysis of Movement by Jorge Poveda, Rory Fewer, Benedikte Wallace

“…Following a general theoretical overview of new technologies developed to process human movement, including motion capture, video visualization, and computer vision, this paper offers an investigation into the practical applications of such technology when applied to dance. …”
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Research on intelligent computing network technology for large-scale pre-trained models by WANG Xuecong, JI Siwei, LI Cong

“…With the development of artificial intelligence, significant achievements are made in various fields such as natural language processing and computer vision through the utilization of large-scale pre-trained models,which promotes the construction of intelligent computing centers. …”
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Research on the Application of Variational Autoencoder in Image Generation by Liu Jianing

“…The rapid development of artificial intelligence and deep learning has significantly influenced the domain of image creation, finding extensive applications in applications in fields like medical imaging, computer vision, and entertainment. Despite these advancements, challenges remain, especially in enhancing the quality and variety of produced images. …”
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Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
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