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11th IAPR TC3 workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2024

11th IAPR TC3 workshop on Artificial Neural Networks in Patt...

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11th IAPR TC3 workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2024

ISBN: (纸本)9783031716010

The proceedings contain 27 papers. The special focus in this conference is on Artificial Neural Networks in Pattern Recognition. The topics include: Neural Decompiling of Tracr Transformers;pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data;robust Clustering with McDonald’s Beta-Liouville Mixture Models for Proportional Data;evaluating Support Vector Machines with Multiple Kernels by Random Search;Automatic Interpretation of 18F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks;a Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical Image Segmentation;An Improved Pix2Pix GAN for Medical Image Generation;vision Transformer Features-based Leukemia Classification;comparative Study of Deep Learning Models in Melanoma Detection;a Metaheuristic Optimization based Deep Feature Selection for Oral Cancer Classification;machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson’s Disease;explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task;Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks;VAeViT: Fusing Multi-views for Complete 3D Object Recognition;leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos;palmprint Classification via Filter Faces and Feature Extraction;deep Multi-label Classification of Personality with Handwriting Analysis;license Plate Detection and Character Recognition Using Deep Learning and Font Evaluation;experiments in modeling Disagreement;deep Multiresolution Wavelet Transform for Speech Emotion Assessment of High-Risk Suicide Callers;Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming;Leveraging LSTM Embeddings for River Water Temperature modeling;research on the Identification of Common Economic Shellfish in Jiang

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Preprocessing Pathology Reports for vision-Language Model Development

Preprocessing Pathology Reports for Vision-Language Model De...

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2024 MICCAI workshop on Computational Pathology, MICCAI COMPAYL 2024

作者： Lucassen, Ruben T. van de Luijtgaarden, Tijn Moonemans, Sander P.J. Blokx, Willeke A.M. Veta, Mitko Dept. of Pathology University Medical Center Utrecht Netherlands Dept. of Biomedical Engineering Eindhoven University of Technology Netherlands Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Pathology reports are increasingly being used for development of vision-language models. Because the reports often include information that cannot directly be derived from paired images, careful selection of information is required to prevent hallucinations in tasks like report generation. In this paper, we present a language model for subsentence segmentation based on the information content, as part of a preprocessing workflow for 27,500 pathology reports of cutaneous melanocytic lesions. After initial clean up, the reports were first translated from Dutch to English and then segmented by separate language models. Both models were developed using an iterative approach, in which the development dataset was expanded with manually corrected model predictions for previously unannotated reports before finetuning the next version of the models. Over the course of eight iterations, the development dataset was in the end scaled up to 1,500 translated and annotated reports. On the independent test set of 3,597 sentences from 150 reports, 219 translation errors (6,1%) of different severities were counted. The subsentence segmentation model achieved a strong predictive performance on the test set with a macro average F1-score of 0.921 (95% CI, 0.890-0.940) and a weighted average F1-score of 0.952 (95% CI, 0.944-0.960) over 13 different classes. The remaining 25,850 unannotated reports were translated and segmented using the final models to complete the dataset preprocessing. Differences in word count and class distribution between section types of the reports were explored in preparation for future vision-language modeling. The presented methodology is generic and can, therefore, easily be extended to multiple or different pathology domains beyond melanocytic skin lesions. Code and trained model parameters are made publicly available. ©2024 Lucassen et al.

关键词： Visual languages

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Hierarchical vision Transformers with Shuffled Local Self-Attentions 3

Hierarchical Vision Transformers with Shuffled Local Self-At...

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3rd International workshop on Intelligent Systems, IWIS 2023

作者： Vo, Xuan-Thuy Nguyen, Duy-Linh Priadana, Adri Jo, Kang-Hyun University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of

ISBN: (纸本)9798350305043

vision Transformers have reached breakthrough improvements in addressing computer visual fields, for instance, object classification, bounding box localization, semantic/instance pixel-wise predictions, single/multiple tracking, and generative AI models such as GPT-4, SAM, and UniAD. The key success of the Transformers is derived from the flexibility in fulfilling long-range dependencies from raw data and the generalization capability of input-dependent weight adaption. With these properties, Transformer models operated with the self-attention heart and without inductive biases become the new paradigm in processing multiple-modality data. However, the main bottle-neck of the Transformer is that global multi-head self-attention layers have high computational costs with the input lengths, e.g., quadratic complexity. When exploiting Transformer-based models on pixel-wise predictions, the cost is not affordable. To deal with this issue, recent methods try to calculate attention weights in local non-overlapped areas and require extra designs that exchange information across windows, for example, window shifting, window expanding, and window sliding. Although these strategies improve accuracy, their implementation is unfriendly and produces additional inference time. Following a line of this research, this paper introduces a new block that consists of non-overlapped local self-attention and overlapped local self-attention. Non-overlapped local self-attention learns interactions inside each window and overlapped local self-attention captures relationships among non-overlapped windows to boost receptive fields and modeling abilities. To be more efficient, both layers are performed in parallel in which each half of the heads is assigned to each layer. Therefore the diversity of the model is enhanced since conventional methods treat all heads equally. Experimental results are conducted and evaluated on the medium dataset, ImageNet-1K. As a result, the proposed approach achiev

关键词： Bottles

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A Lightweight Object Grasping Network using GhostNet 18

A Lightweight Object Grasping Network using GhostNet

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Colloidal Nanoparticles for Biomedical Applications XVIII 2023

作者： Deng, Yangfan Guo, Qinghua Zhao, Yong Xu, Junli School of Mathematical Sciences Ocean University of China Shandong Qingdao China Sinopec Geophysical Research Institute Co.Ltd Jiangsu Nanjing China School of Mathematics and Physics Qingdao University of Science and Technology Shandong Qingdao China

ISBN: (纸本)9781510658950

Object grasping is a very challenging problem in computer vision and robotics. Existing algorithms generally have a large number of training parameters, which lead to long training times and require high performance facilities. In this paper, we present a lightweight neural network to solve the problem of object grasping. Our network is able to generate grasps at real-time speeds (∼30ms), thus can be used on mobile devices. The main idea of GhostNet is to reduce the number of parameters by generating feature maps from each other in the process of convolution. We adopt this idea and apply it on the deconvolution process. Besides, we construct the lightweight grasp network based on these two processes. A lot of experiments on grasping datasets demonstrate that our network performs well. We achieve accuracy of 94% on Cornell grasp dataset and 91.8% on Jacquard dataset. At the same time, compared to traditional models, our model only requires 15% of the number of parameters and 47% of training time. © 2023 SPIE.

关键词： computer vision

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Combined physics and Event Camera Simulator for Slip Detection

Combined Physics and Event Camera Simulator for Slip Detecti...

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2025 IEEE/CVF Winter Conference on Applications of computer vision workshops, WACVW 2025

作者： Reinold, Thilo Ghosh, Suman Gallego, Guillermo Technische Universität Berlin Robotics Institute Germany Einstein Center for Digital Future and Science of Intelligence Excellence Cluster Germany

ISBN: (纸本)9798331536626

Robot manipulation is a common task in fields like industrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and reliable operation. Event cameras, which register pixel-level brightness changes at high temporal resolution (called 'events'), offer an elegant feature when mounted on a robot's end effector: since they only detect motion relative to their viewpoint, a properly grasped object produces no events, while a slipping object immediately triggers them. To research this feature, representative datasets are essential, both for analytic approaches and for training machine learning models. The majority of current research on slip detection with event-based data is done on real-world scenarios and manual data collection, as well as additional setups for data labeling. This can result in a significant increase in the time required for data collection, a lack of flexibility in scene setups, and a high level of complexity in the repetition of experiments. This paper presents a simulation pipeline for generating slip data using the described camera-gripper configuration in a robot arm, and demonstrates its effectiveness through initial data-driven experiments. The use of a simulator, once it is set up, has the potential to reduce the time spent on data collection, provide the ability to alter the setup at any time, simplify the process of repetition and the generation of arbitrarily large data sets. Two distinct datasets were created and validated through visual inspection and artificial neural networks (ANNs). Visual inspection confirmed photorealistic frame generation and accurate slip modeling, while three ANNs trained on this data achieved high validation accuracy and demonstrated good generalization capabilities on a separate test set, along with initial applicability to real-world data. © 2025 IEEE.

关键词： Grippers

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MLI. Mouse: a new computer vision-based cursor control software 5

MLI. Mouse: a new computer vision-based cursor control softw...

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5th International Scientific workshop on modeling, Information Processing and Computing, MIP: Computing-V 2022

作者： Peresunko, Pavel Pleshkova, Ekaterina Semizorova, Anastasiya Kovalev, Igor Institute of Space and Information Technologies Siberian Federal University 26 Kirensky st. Krasnoyarsk660074 Russia

The personal computer opens tremendous opportunities for people, whether it is entertainment or work. However, people with disabilities cannot successfully use usual methods of interacting with a computer like a mouse and a keyboard. There are analogs that allow individuals who had disabilities to control the computer using computer vision. They can use their limbs, head, eyes, color markers as reference point, that is used to move a cursor on their computer screen. One of the most popular cursor control software is The Camera Mouse, in which the user selects the area that will serve as a reference point. However, people may find it difficult to adjust the sensitivity of the reference point movement. For example, if the sensitivity is low, some area around the edges of the screen may become inaccessible. At high sensitivity, not all the pointer movement potential is realized. And the high sensitivity, the small movements, noise, errors affect the swing of the reference pointer more. In this study, the MLI. Mouse proposed. It allows user to control the computer cursor using computer vision techniques. To control the cursor, a person needs a webcam, from which the current position of the reference point will be read. A hand or a QR code can be used as the reference point. A useful feature of this program is a convenient way to visualize and set the active area of the cursor movements. This is the area where the reference pointer movement is broadcast on the computer screen. With this, the program can be configured faster and more conveniently. The left mouse button clicking is implemented using Dwell click. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： computer vision

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BinDev: a Metric of Geometric Accuracy for Plasma-etch 3D modeling Using computer vision : YM: Yield Methodologies

BinDev: a Metric of Geometric Accuracy for Plasma-etch 3D Mo...

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IEEE/SEMI Conference and workshop on Advanced Semiconductor Manufacturing

作者： Yutong Xie Benyamin Davaji Peter C. Doerschuk Amit Lal Ivan Chakarov Sandy Wen Michael Hargrove David Fried Cornell University Northeastern University Lam Research

physics-based semiconductor device and fabrication process modeling provides design and validation solutions for state-of-the-art CMOS and MEMS devices. In this paper, a metric for the geometric differences between two structures visualized by CD-SEM images is defined, and a computer-vision-based algorithm is developed to evaluate the metric. This algorithm is implemented using Python and SEMulator3D ® , a physics-based process modeling software system for semiconductor and MEMS devices. computer vision tools, such as filters, thresholding, and morphology operations, are used to extract geometric features from CD-SEM images and pattern matching and symmetric difference are used to compute the metric. Examples of the metric quantifying the geometric similarity between a simulated nanostructure and an experimental CD-SEM image of the fabricated nanostructure are presented. The data consists of eight classes of nanostructures which are defined, fabricated in the cleanroom with 36 combination of layout parameters, and imaged with CD-SEM.

关键词： Measurement Semiconductor device modeling Micromechanical devices Fabrication computer vision Nanofabrication Computational modeling

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The Method of Applying Machine vision in the Concept of 'Smart Oil Storage' 4

The Method of Applying Machine Vision in the Concept of 'Sma...

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4th International Conference on Control Systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Levin, Maksim Levina, Ekaterina Nagornov, Stanislav Levina, Lyubov Kovalenko, Irina Lipetsk State Technical University Department of Physics and Biomedical Engineering Lipetsk Russia Bauman Moscow State Technical University Department of Physics Moscow Russia FGBNU VNIITiN Tambov Russia Lipetsk State Technical University Department of Applied Mathematics Lipetsk Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The use of intelligent systems during the oil storage and handling process enhances quality preservation and reduction of motor fuel waste caused by evaporation, oxidation and hydration while stored in above-ground horizontal steel tanks. Systems managing 'smart' oil-storage facilities combine technologies for on-line collection, transmission and storage of information with instant data processing and analysis, and managerial decision-making techniques. The concept and developed digital intelligent control solutions for oil storage allows combining data in oil management into a single information space, and to control the automated oil storage system with application of neural networks, deep learning and Big Data. Machine (computer) based vision is a fairly young and rapidly developing field of scientific and applied research, the main purpose of which is to build systems capable of 'seeing', that is, extracting information about world objects from images, which is valuable for further use within any application and its nest step of progressing 'smart' oil-storage. At the same time, the processing of video information is carried out on universal or specialized computers. The formation of algorithms and practical methods of applying machine vision in the concept of smart oil storage will significantly increase the autonomy of intelligent algorithms for farm takers management and ensure its safe operation. © 2022 IEEE.

关键词： computer vision

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Continuous sign language recognition based on 3DCNN and BLSTM 5

Continuous sign language recognition based on 3DCNN and BLST...

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5th International Conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Zhang, Hengbo Li, Daming Fu, Nana College of Physics Electronics and Electrical Engineering Ningxia University Ningxia Yinchuan750021 China

ISBN: (纸本)9781510662520

Sign language recognition can make the communication between deaf mutes and healthy people more convenient and fast. In recent years, with the continuous development of deep learning, the research method of deep learning has also been introduced into the field of sign language recognition. Compared with the recognition of isolated words, the recognition of continuous sign language is more time-dependent. The current research still has shortcomings in recognition accuracy. Therefore, we proposed a continuous sign language recognition method based on 3DCNN and BLSTM. based on the spatial feature information extracted by 3DCNN and the short-term temporal relationship established, the global temporal modeling of the video information of continuous sign language is carried out by using the bidirectional semantic mining ability of BLSTM. The CTC loss function is constructed to solve the problem of time series label misalignment. At the same time, we add the calculation of auxiliary loss function and auxiliary classifier. Experiments show that the auxiliary loss function and classifier can effectively reduce the error rate of the network. The word error rate of the continuous sign language recognition algorithm proposed in this paper on the large continuous sign language dataset RWTH-PHONEIX-Weather 2014 is as low as 23.5%, which is lower than the previous algorithm. © 2023 SPIE.

关键词： Semantics

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10th International workshop on Ophthalmic Medical Image Analysis, OMIA-X 2023

10th International Workshop on Ophthalmic Medical Image Anal...

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10th International workshop on Ophthalmic Medical Image Analysis, OMIA-X 2023

ISBN: (纸本)9783031440120

The proceedings contain 16 papers. The special focus in this conference is on International workshop on Ophthalmic Medical Image Analysis. The topics include: Dual-Modality Grading of Keratoconus Severity based on Corneal Topography and Clinical Indicators;automated Optic Disc Finder and Segmentation Using Deep Learning for Blood Flow Studies in the Eye;multi-relational Graph Convolutional Neural Networks for Carotid Artery Stenosis Diagnosis via Fundus Images;Pretrained Deep 2.5D Models for Efficient Predictive modeling from Retinal OCT: A PINNACLE Study Report;A Structure-Consistency GAN for Unpaired AS-OCT Image Inpainting;STAGE Challenge: Structural-Functional Transition in Glaucoma Assessment Challenge in MICCAI 2023;Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images;auxiliary-Domain Learning for a Functional Prediction of Glaucoma Progression;quickQual: Lightweight, Convenient Retinal Image Quality Scoring with Off-the-Shelf Pretrained Models;Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation;UAU-Net: United Attention U-Shaped Network for the Segmentation of Pigment Deposits in Fundus Images of Retinitis Pigmentosa;glaucoma Progression Detection and Humphrey Visual Field Prediction Using Discriminative and Generative vision Transformers;utilizing Meta Pseudo Labels for Semantic Segmentation of Targeted Optic Nerve Features;privileged Modality Guided Network for Retinal Vessel Segmentation in Ultra-Wide-Field Images.

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