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Real-Time 3D Object Detection, Recognition and Presentation Using a Mobile Device for Assistive Navigation

SN computer science 2023年第5期4卷 1-18页

作者： Chen, Jin Zhu, Zhigang Visual Computing Laboratory Computer Science Department The City College-CUNY New York 10031 NY United States Nearabl Inc. New York 10023 NY United States PhD Program in Computer Science The Graduate Center-CUNY New York 10016 NY United States

This paper presents an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update module, and a voice and AR-enhanced interface. The 3D object detection module consists of pre-trained 2D object detectors and 3D bounding box estimation methods to detect the 3D poses and sizes of the objects in each camera frame. This module can easily adapt to various 2D object detectors (e.g., YOLO, SSD, mask RCNN) based on the requested task and requirements of the run time and details for the 3D detection result. It can run on a cloud server or mobile application. The object tracking and update module minimizes the computational power for long-term environment scanning by converting 2D tracking results into 3D results. The voice and AR-enhanced interface integrates ARKit and SiriKit to provide voice interaction and AR visualization to improve information delivery for different user groups. The system can be integrated with existing applications, especially assistive navigation, to increase travel safety for people who are blind or have low vision and improve social interaction for individuals with autism spectrum disorder. In addition, it can potentially be used for 3D reconstruction of the environment for other applications. Our preliminary test results for the object detection evaluation and real-time system performance are provided to validate the proposed system. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 3D object detection ARKit Assistive technology Blind or low vision Voice assistance

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Vegetation Coverage and Urban Amenity Mapping Using computer Vision and Machine Learning

Vegetation Coverage and Urban Amenity Mapping Using Computer...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Karkut, Nicholas Kiriluk, Alexey Zhang, Zihao Zhu, Zhigang Computer Science Department The City College of New York - CUNY New YorkNY10031 United States Graduate Landscape Architecture Program The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. Image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection;(2) vegetation analysis, bench detection and depth estimation;and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Vegetation mapping

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An Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings

An Integrated Mobile Vision System for Enhancing the Interac...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Chen, Jin Ramnath, Satesh Samaroo, Tyron Maksakuli, Fani Ruci, Arber Sturdivant, E’edresha Zhu, Zhigang Nearabl Inc New YorkNY10023 United States Computer Science Department The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper presents a mobile-based solution that integrates 3D vision and voice interaction to assist people who are blind or have low vision to explore and interact with their surroundings. The key components of the system are the two 3D vision modules: the 3D object detection module integrates a deep-learning based 2D object detector with ARKit-based point cloud generation, and an interest direction recognition module integrates hand/finger recognition and ARKit-based 3D direction estimation. The integrated system consists of a voice interface, a task scheduler, and an instruction generator. The voice interface contains a customized user request mapping module that maps the user’s input voice into one of the four primary system operation modes (exploration, search, navigation, and settings adjustment). The task scheduler coordinates with two web services that host the two vision modules to allocate resources for computation based on the user request and network connectivity strength. Finally, the instruction generator computes the corresponding instructions based on the user request and results from the two vision modules. The system is capable of running in real time on mobile devices. We have shown preliminary experimental results on the performance of the voice to user request mapping module and the two vision modules. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Palmprint recognition

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To Sharpen or not to Sharpen? On the Effect of Sharpening Filters Applied to Magnified Images 14

To Sharpen or not to Sharpen? On the Effect of Sharpening Fi...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Emmendorfer, Leonardo R. de Olviveira, Vinicius Menezes Schein, Tatiana T. de Jesus, Junior C. Rodriguez, Bárbara D.A. Electrical Energy Processing Department Federal University of Santa Maria 97105-340 Brazil Center for Computational Science Federal University of Rio Grande 96201-900 Brazil Computational Modeling PhD Program Federal University of Rio Grande 96201-900 Brazil Computer Engineering Graduate Program Federal University of Rio Grande 96201-900 Brazil Institute of Mathematics Statistics and Physics Federal University of Rio Grande 96201-900 Brazil

ISBN: (纸本)9781510657564

Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual Image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent. © 2022 SPIE.

关键词： Quality control

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Using prosody to improve dependency parsing 10

Using prosody to improve dependency parsing

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10th International Conference on Speech Prosody 2020

作者： Ghaly, Hussein Mandel, Michael Linguistics PhD Program Graduate Center City University of New York United States Computer and Information Science Brooklyn College United States

The goal of the present study is to use prosodic information to improve automatic syntactic parsing of conversational speech in the Switchboard Corpus. To achieve this, an ensemble classifier, based on a Recurrent Neural Network, is developed to predict the parse with the highest Unlabelled Attachment Score (UAS) from the outputs of multiple dependency parsers, based on syntactic and prosodic features. The main syntactic features proposed, which we refer to as "dependency configurations," represent the relative dependency location of each of a pair of consecutive words. Empirical analysis indicates that configurations with a direct dependency between consecutive words are less likely to be associated with major prosodic breaks. Using syntactic features alone, the system achieved an improvement of 1.1% of UAS on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4%. Both empirical analysis of dependency configurations and parsing improvement suggest a relationship between prosody and direct dependency relationships between consecutive words. © 2020 International Speech Communications Association. All rights reserved.

关键词： Recurrent neural networks

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm

Impact of Image Enhancement for Osteoporosis Detection Based...

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computer System, Information Technology, and Electrical Engineering (COSITE), International Conference on

作者： Riza Dianil Iman Roslidar Roslidar Kahlil Muchtar Amalia Amalia Mohammad Syaryadhi Alfatirta Mufti Novi Maulina Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an Fl-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance.

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Robust prediction of personalized in vivo response to unseen drugs from in vitro screens using a novel context-aware deconfounding autoencoder

Research Square

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Research Square 2021年

作者： He, Di Liu, Qiao Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient-specific responses to drug treatments is critical for drug development and personalized medicine. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell line data, few of them can reliably predict individual patient clinical responses to new drugs due to data distribution shift and confounding factors. We have developed a novel Context-aware Deconfounding Autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization, significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific in vivo drug responses purely from in vitro screens. Using CODE-AE, we screened 59 drugs for 9,808 cancer patients. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized anti-cancer therapies and drug-response biomarkers. © 2021, CC BY.

关键词： Cell culture

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CODE-AE: A coherent de-confounding autoencoder for predicting patient-specific drug response from cell line transcriptomics

arXiv

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arXiv 2021年

作者： He, Di Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient’s response to drug treatments is critical for developing precision medicine. However, it is often difficult to obtain a sufficient amount of coherent drug response data from patients directly for training a generalized machine learning model. Although the utilization of rich cell line data provides an alternative solution, it is challenging to transfer the knowledge obtained from cell lines to patients due to various confounding factors. Few existing transfer learning methods can reliably disentangle common intrinsic biological signals from confounding factors in the cell line and patient data. In this paper, we develop a Coherent Deconfounding Autoencoder (CODE-AE) that can extract both common biological signals shared by incoherent samples and private representations unique to each data set, transfer knowledge learned from cell line data to tissue data, and separate confounding factors from them. Extensive studies on multiple data sets demonstrate that CODE-AE significantly improves the accuracy and robustness over state-of-the-art methods in both predicting patient drug response and deconfounding biological signals. Thus, CODE-AE provides a useful framework to take advantage of in vitro omics data for developing generalized patient predictive models. The source code is available at https://***/XieResearchGroup/CODE-AE. © 2021, CC BY.

关键词： Learning systems

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Hamiltonicity via cohomology of right-angled Artin groups

arXiv

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arXiv 2021年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York United Kingdom New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Department of Mathematics University of Virginia CharlottesvilleVA22904 United States

Let Γ be a finite graph and let A(Γ) be the corresponding right-angled Artin group. We characterize the Hamiltonicity of Γ via the structure of the cohomology algebra of A(Γ). In doing so, we define and develop a new canonical graph associated to a matrix, which as a consequence provides a novel perspective on the matrix determinant. Copyright © 2021, The Authors. All rights reserved.

关键词： Topology

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Determinant of importance-performance and willingness to participate in Komodo adaptive conservation programs

引用

Journal for Nature Conservation 2024年 81卷

作者： Sianipar, Imelda Masni Juniaty Lee, Chun-Hung Wang, Hurng-Jyuhn Kim, Doo-Chul Suryawan, I Wayan Koko PhD Student in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of International Relations Faculty of Social Science and Political Science Universitas Kristen Indonesia Department of Natural Resources and Environmental Studies College of Environmental Studies and Oceanography National Dong Hwa University Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Rural Environment Management Graduate School of Environmental and Life Science Okayama University Ph.D Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Environmental Engineering Faculty of Infrastructure Planning Universitas Pertamina Jalan Sinabung II Terusan Simprug

This study delves into the evaluation of the Komodo dragons Adaptive Conservation program (KACP) at Komodo National Park in Indonesia through Importance-Performance Analysis (IPA). It aims to understand how the programs components align with community priorities and assess its effectiveness. Utilizing a mixed-methods approach, this research analyzes community perceptions of KACP's critical elements, spanning eco-tourism initiatives to environmental protection strategies. The community is categorized into local advocates, eco-tourism enthusiasts, and eco-community harmonizers, identifying how each group perceives and interacts with various facets of KACP. The findings reveal significant differences in perceptions of importance and performance among these groups, providing insights into areas of strength and those needing improvement. Additionally, the study includes an analysis of demographic influences on these perceptions through Logit and Probit models, aiming to understand the impacts across different community segments. The results highlight the necessity for an adaptive and responsive design in conservation efforts, considering community expectations and ecological impacts. Despite offering comprehensive insights, the study acknowledges limitations in the demographic scope and suggests future research directions, such as longitudinal studies and an expanded analysis of variables. These findings offer critical guidance for policymakers and conservation practitioners to enhance conservation programs’ effectiveness and community alignment, emphasizing the need to adapt to the challenges and changes introduced by KACP. © 2024 Elsevier GmbH

关键词： Adaptive capacity Community perception Conservation strategy evaluation Importance-performance analysis Protected areas

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