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international conference on 3d imaging, modeling, Processing, Visualization and Transmission (3dIMPVT)

作者： Liang Pan Jingbo Wang Buzhen Huang Junyu Zhang Haofan Wang Xu Tang Yangang Wang Southeast University Shanghai AI Laboratory Xiaohongshu Inc.

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

We present a physics-based character control framework for synthesizing human-scene interactions. recent advances adopt physics simulation to mitigate artifacts produced by data-driven kinematic approaches. However, existing physics-based methods mainly focus on single-object environments, resulting in limited applicability in realistic 3d scenes with multi-objects. To address such challenges, we propose a framework that enables physically simulated characters to perform long-term interaction tasks in diverse, cluttered, and unseen 3d scenes. The key idea is to decouple human-scene interactions into two fundamental processes, Interacting and Navigating, which motivates us to construct two reusable Controllers, namely InterCon and NavCon. Specifically, InterCon uses two complementary policies to enable characters to enter or leave the interacting state with a particular object (e.g., sitting on a chair or getting up). To realize navigation in cluttered environments, we introduce NavCon, where a trajectory following policy enables characters to track pre-planned collision-free paths. Benefiting from the divide and conquer strategy, we can train all policies in simple environments and directly apply them in complex multi-object scenes through coordination from a rule-based scheduler. Video and code are available at https://***/InterScene/.

关键词： Training Three-dimensional displays Codes Navigation Tracking Process control Kinematics

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Optimizing Multi-Grade Brain Tumor Classification in MRI Images with Hybrid Structure descriptors: A detailed Analysis

Optimizing Multi-Grade Brain Tumor Classification in MRI Ima...

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recent advances in Science and Engineering Technology (ICRASET), international conference on

作者： B N Krishna Reddy Santosh Kumar d. Bhanu Shubhansh Bansal S. Sathiya Naveena Kanika Seth School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India AI & Ds Vishwakarma Institute of Technology Pune India Department of Information Technology Karpagam Institute of Technology Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The work focuses on developing classification models to distinguish the four most common pediatric cerebral tumor types: meningioma, astrocytoma, glioma, and metastases. This tumor kind is hard to see on MRI. Accurately distinguishing meningioma, astrocytoma, glioma, and metastases from MRI data is critical for establishing a surgical plan for tumor excision, since each kind needs distinct activities for a favorable prognosis. The third paper introduces a novel approach to enhance cancer classification accuracy, including meningioma, astrocytoma, glioma, and metastases, utilizing modified multi-texton distribution and hybridization kernel support vector machine methods. Brain tumors develop from uncontrolled cell proliferation. Tumor-forming brain cells surround gland, nerve, and brain membranes. Tumors preferentially destroy brain cells. Ganglioglioma, pilocytic astrocytoma. Slow-growing tumors seem strange under a microscope. Few spreads to adjacent tissues and recur as category high tumors. Rapidly multiplies, looking weirdly on a magnifying glass, and forms brain tissues and arteries. These tumor cells have dead cores. Cancers include GBM. Progressive pre-processing and segmentation may enhance multi-class brain cancer and normal image recognition and categorization. Texture fusion boosts performance. With $\mathbf{9 7 . 4 7 \%}$ accuracy and $\mathbf{9 6 . 0 3 \%}$ sensitivity, HK+FSVM exceeds all other algorithms. The FSVM model is second-best with $\mathbf{9 4 . 7 5 \%}$ accuracy and $\mathbf{9 4 . 4 8 \%}$ sensitivity.

关键词： Solid modeling Image segmentation Accuracy Sensitivity Magnetic resonance imaging Brain cells Brain modeling Transformers Biological neural networks Testing

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Controllable dynamic Appearance for Neural 3d Portraits

Controllable Dynamic Appearance for Neural 3D Portraits

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international conference on 3d imaging, modeling, Processing, Visualization and Transmission (3dIMPVT)

作者： ShahRukh Athar Zhixin Shu Zexiang Xu Fujun Luan Sai Bi Kalyan Sunkavalli dimitris Samaras Stony Brook University Adobe Research

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and viewing direction. However, training such models assumes photometric consistency over the deformed region e.g. the face must be evenly lit as it deforms with changing head-pose and facial expression. Such photometric consistency across frames of a video is hard to maintain, even in studio environments, thus making the created reanimatable neural portraits prone to artefacts during reanimation. In this work, we propose CodyNeRF, a system that enables the creation of fully controllable 3d portraits in real-world capture conditions. CodyNeRF learns to approximate illumination dependent effects via a dynamic appearance model in the canonical space that is conditioned on predicted surface normals and the facial expressions and head-pose deformations. The surface normals prediction is guided using 3dMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes. Using only a smartphone-captured short video of a subject for training, we demonstrate the effectiveness of our method on free view synthesis of a portrait scene with explicit head pose and expression controls, and realistic lighting effects.

关键词： Training deformable models Three-dimensional displays Monte Carlo methods deformation Lighting Predictive models

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deep hydraulic fracture imaging: recent advances in tiltmeter technologies

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international Journal of Rock Mechanics and Mining Sciences 1997年第3-4期34卷 47.e1-47.e9页

作者： Castillo, d. Hunter, S. Harben, P. Wright, C. Conant, R. davis, E. Earth Science Division Lawrence Livermore National Laboratory LivermoreCA94551 United States Pinnacle Technologies Inc. 600 Townsend Street Suite 160W San FranciscoCA94103 United States

Significant advancements in tiltmeter technologies has improved our ability to map the geometry of hydraulic fractures at great depth. This department of Energy sponsored effort with widespread industry collaboration has extended the capability of near-surface tiltmeters to image details of the hydraulic fracture process at depths approaching 10,000 feet. Improvements in the analog-digital circuitry, the data-logger, and the remote mechanical leveling device has enabled us to install tiltmeters in relatively deep holes where the degradation affects due to the natural-cultural noise sources on the tilt-signal are minimized. Another benefit of utilizing this technology in the oil and gas industry is that it provides valuable information on the geometry of these hydraulic fractures, which is important for production field design. modeling tilmeter data with algorithms that take into account details of the fracture growth process and the local geology are currently being developed, especially since recent results indicate that signal strength from deeper hydraulic fractures may be stronger than previous models had suggested. © 1997

关键词： Fracture

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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recent advances in Science and Engineering Technology (ICRASET), international conference on

作者： J. Jagannathan A. Ponmalar Rajkumar Pandiarajan I. Sudha P.S. Ramesh Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India R.M.K. Engineering College Chennai Tamilnadu India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study.

关键词： Accuracy Wildfires Multispectral imaging disasters Ecosystems Vegetation mapping Water quality Soil Sensors Remote sensing

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SAvEM3: Pretrained and distilled Models for General-purpose 3d Neuron Reconstruction

SAvEM3: Pretrained and Distilled Models for General-purpose ...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Zhai Jinyue Guo Yanchao Zhang Jing Liu Hua Han Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With an explosion in the uptake of volume electron microscopy (vEM) across neuroscience and fast-paced advances in imaging protocols, it is timely to introduce general-purpose automation for newly generated large-scale vEM datasets. recent vision foundation models (e.g., SAM) set a new benchmark for the generalization of 2d segmentation. However, SAM has difficulty handling neurons that are densely packed into 3d volumes. To overcome this obstacle, we consider solutions from both data and model aspects. In terms of data, we introduce a data engine to optimize manual labeling, including (i) human-in-the-loop data cleansing and (ii) model-in-the-loop data unification. In terms of model, we present the SAEM 2 -SAvEM 3 with strategies, including (i) auxiliary learning, which predicts complementary representations for SAM masks and improves performance on dense instances; (ii) full-stage distillation, which integrates ViT embeddings into a 3d U-Net, achieves 2d-to-3d lifting and model slimming at the same time; and (iii) prompt-based graph partitioning, which reuses SAM prompts to assign weights of nodes and edges in the oversegmentation graph. According to evaluations of dense and large-scale sparse neurons, the out-of-distribution performance of our pretrained-distilled models is on par with the state-of-the-art supervised and semi-supervised methods. The overall pipeline provides a possible general-purpose solution for 3d neuron reconstruction in any new vEM data. Our code are available at https://***/JackieZhai/SAvEM3.

关键词： Solid modeling Three-dimensional displays Protocols Biological system modeling Neurons Predictive models data models Electron microscopy Image reconstruction Electrons

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Improving Clinical decision-Making: detection and Classification of disease like Kidney Stone from CT Kidney Images Using deep Learning

Improving Clinical Decision-Making: Detection and Classifica...

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Intelligent Technologies (CONIT), international conference on

作者： Shivani Verma Pawan Kumar Singh Bhanu Sharma K.d Sharma Department of Electronics and Communication Engineering SRM University Sonipat India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Kidney stones are a common medical problem that causes a lot of people to go to the emergency department because they hurt so much and affect the quality of life. The use of several imaging modalities is essential in the diagnosis of kidney stone disease. The in-depth examination and analysis of these images call for the knowledge and experience of experts. When doctors need a helping hand making a diagnosis, they might turn to computer-aided diagnostic (CAd) techniques, which are practical ways. recent advances in the application of deep learning methods to AI have prompted the authors of this work to suggest using these methods to automatically diagnose kidney stones by analyzing coronal computed tomography images. An evaluation of a deep learning model (dL) trained for classification tasks is provided in this paper. By examining training and validation data, the research determines the model’s efficacy. With a training and validation accuracy of 99% percent, the model excels at accurately classifying data instances. Contributing to a deeper understanding of the model’s capabilities, the study findings pave the path for its potential use in the world of healthy life.

关键词： Training deep learning Solid modeling Accuracy Computational modeling Computed tomography Predictive models

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Graph Models of Neurodynamics to Support Oscillatory Associative Memories

Graph Models of Neurodynamics to Support Oscillatory Associa...

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international Joint conference on Neural Networks

作者： Gabriel P. Andrade Miklos Ruszinko Robert Kozma College of Information and Computer Sciences University of Massachusetts Amherst 140 Governors Drive Amherst MA USA Alfréd Rényi Institute of Mathematics Hungarian Academy of Sciences 13-15 Reáltanoda utca Budapest Hungary

recent advances in brain imaging techniques require the development of advanced models of brain networks and graphs. Previous work on percolation on lattices and random graphs demonstrated emergent dynamical regimes, including zero- and non-zero fixed points, and limit cycle oscillations. Here we introduce graph processes using lattices with excitatory and inhibitory nodes, and study conditions leading to spatio-temporal oscillations. Rigorous mathematical analysis provides insights on the possible dynamics and, of particular concern to this work, conditions producing cycles with very long periods. A systematic parameter study demonstrates the presence of phase transitions between various regimes, including oscillations with emergent metastable patterns. We studied the impact of external stimuli on the dynamic patterns, which can be used for encoding and recall in robust associative memories.

关键词： Firing Associative memory Fires Computational modeling Numerical models Mathematical model Brain

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3dPeople: modeling the Geometry of dressed Humans

3DPeople: Modeling the Geometry of Dressed Humans

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international conference on Computer Vision (ICCV)

作者： Albert Pumarola Jordi Sanchez Gary P. T. Choi Alberto Sanfeliu Francesc Moreno UPC Barcelona Spain IRI Harvard University Cambridge MA USA

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

recent advances in 3d human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothing geometry. In this paper, we present an approach to model dressed humans and predict their geometry from single images. We contribute in three fundamental aspects of the problem, namely, a new dataset, a novel shape parameterization algorithm and an end-to-end deep generative network for predicting shape. First, we present 3d-People, a large-scale synthetic dataset with 2 Million photo-realistic images of 80 subjects performing 70 activities and wearing diverse outfits. Besides providing textured 3d meshes for clothes and body we annotated the dataset with segmentation masks, skeletons, depth, normal maps and optical flow. All this together makes 3d-People suitable for a plethora of tasks. We then represent the 3d shapes using 2d geometry images. To build these images we propose a novel spherical area-preserving parameterization algorithm based on the optimal mass transportation method. We show this approach to improve existing spherical maps which tend to shrink the elongated parts of the full body models such as the arms and legs, making the geometry images incomplete. Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner. We obtain very promising results in jointly capturing body pose and clothing shape, both for synthetic validation and on the wild images.

关键词： Shape Geometry Three-dimensional displays Clothing Cameras Two dimensional displays Optical imaging

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Cervical Cancer Vascular Segmentation in 3d Power doppler Ultrasound Using Advanced deep Learning Models

Cervical Cancer Vascular Segmentation in 3D Power Doppler Ul...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Mihaela Miron Anisia-Luiza Culea-Florescu Bogdan Ioan Ştefãnescu Department of Computer Science and Information Technology University “Dunărea de Jos” Galati Romania Department of Medicine University “Dunârea de Jos” Galati Romania

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

Cervical cancer remains a significant global health challenge. Precise segmentation of vascular anatomy in 3d Power doppler ultrasound (3d - PdU) images is crucial for accurate diagnosis, effective treatment planning and continuous monitoring of the disease. recent advances in deep learning have significantly enhanced the precision of segmentations for assessing tumor aggressiveness in various types of tumors. However, these advancements are still limited and face challenges when applied to cervical cancer vascular anatomy. Among deep learning models, U-Net excels in pixel-level image analysis, allowing for detailed mapping of vascular structures, which is vital for improving the management and outcomes of cervical cancer patients. This study proposes a framework with two stages: first, extracting the ground-truth masks of the vascular regions from cervical cancer 3d-PdU images, and second, evaluating the performance of three deep learning models in segmenting the vascular regions within these images. After the first stage is validated by gynecological clinicians, three models are evaluated: a U-Net, a U-Net variant with VGG16 backbone, and a U-Net variant with denseNet121 backbone. The U-Net variant with denseNet121 backbone achieved the best results on the test dataset, with a pixel accuracy of 97.08%, an Intersection over Union (IoU) of 90.63%, and a dice coefficient of 95.08%.

关键词： deep learning Image segmentation Solid modeling Accuracy Ultrasonic imaging Three-dimensional displays Image color analysis doppler effect Cervical cancer Tumors

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