检索结果-内蒙古大学图书馆

Parameter adaptive pulse coupled neural network-based saliency map fusion strategy for salient object detection

NEURAL COMPUTING & applications 2023年第21期35卷 15743-15757页

作者： Lad, Bhagyashree v. v. Hashmi, Mohammad Farukh Keskar, Avinash G. G. Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur 440010 Maharashtra India Natl Inst Technol Warangal Dept Elect & Commun Engn Warangal 506004 Telangana India

Today, salient object detection has caught the interest of numerous researchers for a variety of applications in computer vision. Most deep learning-based algorithms for SOD tasks produce excellent results but require a lot of data availability and large computational and structural complexities. Also, many methods provide excellent outcomes but are unable to preserve the complete boundaries of the objects in images. The paper discusses an innovative integration method for salient object detection of superpixel segmented images to address these issues. It deals with the integration of saliency maps generated by image decomposition based on non-sub-sampled contourlet transform (NSCT) and by machine learning technique based on the random forest regression using a parameter adaptive pulse coupled neural network (PA-PCNN). The PA-PCNN adaptively estimates each of the free parameters of the pulse-coupled neural network (PCNN). The utilization of PA-PCNN considers neighborhood pixel variances and aids in maintaining object details without fuzziness or distortions. The proposed method restores the edges and boundaries of the objects effectively as PA-PCNN aids in maintaining the perceptually similar attributes of the saliency maps. The results of this study are evaluated using three widely used datasets for detecting salient objects, which show the potential of the proposed system to precisely locate the salient objects in various imaging circumstances like complex background images, images with multiple objects, etc. The quantitative and qualitative experimental results validate a substantial advancement in various evaluation parameters for salient object detection with better boundary preservation of objects.

关键词： Salient object detection Parameter adaptive pulse coupled neural network Random forest regression Non sub-sampled contourlet transform

来源：评论

学校读者我要写书评

暂无评论

A Current-Mode Multiply-Accumulate Macro in Sensing-Computing Fusion System for Feature Extraction and Redundancy Reduction

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第5期72卷 798-802页

作者： Ren, Xu Li, Xinpeng Xue, Chang He, Yandong Du, Gang Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Peking Univ Beijing Adv Innovat Ctr Integrated Circuits Beijing 100871 Peoples R China

This brief presents a current-mode sensing-computing fusion system for advanced Internet of Things (IoT) machine vision. The key contributions of our work are: (a) a low-voltage of 0.9v multiply-accumulate (MAC) computing macro with reconfigurable weights is proposed, enabling efficient on-sensor feature extraction;(b) a weight-flipping method for processing negative signals is employed, reducing both power consumption and circuit complexity;(c) a convolutional horizontal shifting technique with fixed weights is equipped, eliminating power consumption associated with weight updates. A 16 x 16 CCIS prototype, fabricated using a 0.18 mu m CMOS process, achieves a power efficiency of 27.7pJ/frame-pixel at 2000fps. Experimental evaluations in edge feature extraction demonstrate a 32% reduction in power consumption, highlighting the efficiency gains of our approach.

关键词： Feature extraction Termination of employment Computational efficiency Switches Power demand Real-time systems Internet of Things image edge detection IP networks Convolution Sensing-computing fusion computing in/near sensor current mode computing edge detection feature extraction multiply-accumulate (MAC) macro low power design

来源：评论

学校读者我要写书评

暂无评论

Tomato leaf disease classification by exploiting transfer learning and feature concatenation

引用

IET image processing 2022年第3期16卷 913-925页

作者： Al-gaashani, Mehdhar S. A. M. Shang, Fengjun Muthanna, Mohammed S. A. Khayyat, Mashael Abd El-Latif, Ahmed A. Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China St Petersburg State Univ Dept Math St Petersburg 199034 Russia Univ Jeddah Coll Comp Sci & Engn Dept Informat Syst & Technol Jeddah Saudi Arabia Menoufia Univ Fac Sci Math & Comp Sci Dept Shibin Al Kawm Egypt

Tomato is one of the most important vegetables worldwide. It is considered a mainstay of many countries' economies. However, tomato crops are vulnerable to many diseases that lead to reducing or destroying production, and for this reason, early and accurate diagnosis of tomato diseases is very urgent. For this reason, many deep learning models have been developed to automate tomato leaf disease classification. Deep learning is far superior to traditional machine learning with loads of data, but traditional machine learning may outperform deep learning for limited training data. The authors propose a tomato leaf disease classification method by exploiting transfer learning and features concatenation. The authors extract features using pre-trained kernels (weights) from MobileNetv2 and NASNetMobile;then, they concatenate and reduce the dimensionality of these features using kernel principal component analysis. Following that, they feed these features into a conventional learning algorithm. The experimental results confirm the effectiveness of concatenated features for boosting the performance of classifiers. The authors have evaluated the three most popular traditional machine learning classifiers, random forest, support vector machine, and multinomial logistic regression;among them, multinomial logistic regression achieved the best performance with an average accuracy of 97%.

关键词： agricultural products Regression analysis machine learning (artificial intelligence) Neural nets concatenated features image recognition random forests transfer learning Computer vision and image processing techniques deep learning (artificial intelligence) Other topics in statistics feature extraction image classification pattern classification crops tomato leaf disease classification method Agriculture Data handling techniques popular traditional machine learning classifiers feature concatenation support vector machines Biology and medical computing Agriculture, forestry and fisheries computing deep learning models logistic regression learning (artificial intelligence) plant diseases regression analysis Support vector machines Information technology applications tomato crops principal component analysis conventional learning algorithm important vegetables worldwide diseases tomato diseases

来源：评论

学校读者我要写书评

暂无评论

Hearing Anything Anywhere

Hearing Anything Anywhere

引用

IEEE/CvF Conference on Computer vision and Pattern Recognition (CvPR)

作者： Wang, Mason Long Sawata, Ryosuke Clark, Samuel Gao, Ruohan Wu, Shangzhe Wu, Jiajun Stanford Univ Stanford CA 94305 USA Sony AI Tokyo Japan Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350353006

Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However, alongside immersive visual experiences, immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper, we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene, a setup that is easily achievable by ordinary users. To this end, we introduce DIFFRIR, a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene, including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method, we collect a dataset of RIR recordings and music in four diverse, real environments. We show that our model outperforms state-of-the-art baselines on rendering monaural and binaural RIRs and music at unseen locations, and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene.

关键词： Audio Binauralization Differentiable Audio Rendering image-Source Inverse Rendering machine Learning Physics-Based Models Room Acoustics Room Impulse Response Signal processing Sound Sound Dataset Soundfield Interpolation Spatial Audio

来源：评论

学校读者我要写书评

暂无评论

Defect Detection and Removal for Depth Map Quality Enhancement in Manufacturing with Deep Learning 12

Defect Detection and Removal for Depth Map Quality Enhanceme...

引用

Dimensional Optical Metrology and Inspection for Practical applications XII 2023

作者： Gapon, N. voronin, v. Sizyakin, R. Zhdanova, M. Semenishchev, E. Zelensky, A. Don State Technical University Rostov-on-Don Russia Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia

ISBN: (数字)9781510661639

ISBN: (纸本)9781510661622

A system for determination the distance from the robot to the scene is useful for object tracking, and 3-D reconstruction may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by incorrect sections on the depth map because of incorrect distance determination of objects. We present a two-stage approach combining defect detection and depth reconstruction algorithms. The first step is image defects detection based on convolutional auto-encoder (U-Net) and deep feature fusion network (DFFN-Net). The second step is a depth map reconstruction with the exemplar-based and the anisotropic gradient concepts. The proposed modified block fusion algorithm uses a local image descriptor obtained by an automatic encoder for image reconstruction, which extracts image features and depth maps using a decoding network. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on RGB-D benchmark for evaluating manufacturing vision systems. © 2023 SPIE.

关键词： Robots

来源：评论

学校读者我要写书评

暂无评论

Loop-mediated isothermal amplification assays for the detection of antimicrobial resistance elements in vibrio cholera

引用

BMC BIOINFORMATICS 2024年第1期25卷 1-13页

作者： Negron, Daniel Antonio Trivedi, Shipra Tolli, Nicholas Ashford, David Melton, Gabrielle Guertin, Stephanie Jennings, Katharine Necciai, Bryan D. Sozhamannan, Shanmuga Abramson, Bradley W. Noblis Inc 2002 Edmund Halley Dr Reston VA 20191 USA Noblis ESI 14425 Penrose Pl Chantilly VA 20151 USA Joint Project Lead CBRND Enabling Biotechnol JPL C Joint Program Execut Off Chem Biol Radiol & Nucl D Frederick MD 21702 USA Joint Res & Dev Inc Stafford VA 22556 USA

BackgroundThe bacterium vibrio cholerae causes diarrheal illness and can acquire genetic material leading to multiple drug resistance (MDR). Rapid detection of resistance-conferring mobile genetic elements helps avoid the prescription of ineffective antibiotics for specific strains. Colorimetric loop-mediated isothermal amplification (LAMP) assays provide a rapid and cost-effective means for detection at point-of-care since they do not require specialized equipment, require limited expertise to perform, and can take less than 30 min to perform in resource limited regions. LAMP output is a color change that can be viewed by eye, but it can be difficult to design primer sets, determine target specificity, and interpret subjective color *** developed an algorithm for the in silico design and evaluation of LAMP assays within the open-source PCR Signature Erosion Tool (PSET) and a computer vision application for the quantitative analysis of colorimetric outputs. First, Primer3 calculates LAMP primer sequence candidates with settings based on GC-content optimization. Next, PSET aligns the primer sequences of each assay against large sequence databases to calculate sufficient sequence similarity, coverage, and primer arrangement to the intended taxa, ultimately generating a confusion matrix. Finally, we tested assay candidates in the laboratory against synthetic *** an example, we generated new LAMP assays targeting drug resistance in v. cholerae and evaluated existing ones from the literature based on in silico target specificity and in vitro testing. Improvements in the design and testing of LAMP assays, with heightened target specificity and a simple analysis platform, increase utility for in-field applications. Overall, 9 of the 16 tested LAMP assays had positive signal through visual and computer vision-based detection methods developed here. Here we show LAMP assays tested on synthetic AMR gene targets for aph(6), varG, floR, qnrvC5, an

关键词： LAMP AMR Assay vibrio cholera Antimicrobial resistance image processing Computer vision Loop-mediated isothermal amplification Molecular diagnostic Colorimetric

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial Introduction to the Special Section on Communication-Efficient Distributed machine Learning

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第4期9卷 1949-1950页

作者： Chu, Xiaowen Giunchiglia, Fausto Neglia, Giovanni Gregg, David Liu, Jiangchuang Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511458 Peoples R China Univ Trento I-38122 Trento Italy INRIA F-06902 Sophia Antipolis France Trinity Coll Dublin Comp Sci Dublin Ireland Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

The papers in this special section focus on communication-efficient distributed machine learning. machine learning, especially deep learning, has been successfully applied in a wealth of practical AI applications in the field of computer vision, natural language processing, healthcare, finance, robotics, etc. With the increasing size of machine learning models and training data sets, training deep learning models requires significant amount of computations and may take days to months on a single GPU or TPU. Therefore, it becomes a common practice to exploit distributed machine learning to accelerate the training process with multiple processors. Distributed machine learning typically requires the processors to exchange information repeatedly throughout the training process. With the fast-growing computing power of the AI processors, the data communications among processors gradually become the performance bottleneck and excessively limit the system scalability due to Amdahl's law. The design of communication-efficient distributed machine learning systems has attracted great attention from both academia and industry.

关键词： Special issues and sections Deep learning machine learning Computational modeling Natural language processing Training data Communication systems Distributed processing

来源：评论

学校读者我要写书评

暂无评论

Digital image processing: Advanced Technologies and applications

引用

APPLIED SCIENCES-BASEL 2024年第14期14卷 6051页

作者： Mahmood, Zahid COMSATS Univ Islamabad Dept Elect & Comp Engn Abbottabad Campus Abbottabad 22060 Pakistan

A few decades ago, conventional image processing methods mostly focused on basic tasks such as image enhancement, registration, or edge detection. Early attempts to achieve these tasks mostly utilized grayscale images. Over time, simple methods to process grayscale images resulted in performance degradation for RGB images [1]. Ultimately, RGB image processing received more attention and subsequent advancements were made, including color preservation and fusion-based processing [2]. Currently, deep learning is used extensively in various fields, such as speech recognition and healthcare domains, with encouraging outcomes in image processing, such as image classification and segmentation [3]. A recent study showed that deep learning-based approaches significantly improve the performance of many image-related tasks, such as object detection, recognition, or segmentation compared, compared to conventional *** the past three decades, a large number of diverse methods have appeared in computer vision and machine learning. Many of them utilize conventional machine learning. However, the recent trend in deep learning has yielded encouraging results. This section gives a brief overview of the works collected in this Special *** contributions listed in this Special Issue can be combined into three major groups with the following key *** the rapid advancements in AI and machine learning, the use of deep learning in various applications has become obvious in many industries due to its ability to process complex patterns and make reliable predictions. Therefore, deep learning algorithms have found their place in crucial fields, including object detection and recognition, natural language processing, and medical imaging. For instance, CNNs are extensively employed for tasks such as image classification, object detection, segmentation, and medical imaging. Similarly, recurrent neural networks (RNNs) and transformers have advanced the capabilities of ma

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing 6

A Survey on Automated Disease Diagnosis and Classification o...

引用

6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Khandelwal, Ankit Shukla, Aasheesh Sain, Mangal GLA University Electronics and Communication Dept. Mathura India Dongseo University Information Engineering Dept. Busan Korea Republic of

ISBN: (纸本)9798350346961

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.. © 2023 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Detecting Functional Objects using Multi-Modal Data 5

Detecting Functional Objects using Multi-Modal Data

引用

Conference on Artificial Intelligence and machine Learning for Multi-Domain Operations applications v

作者： Ellis, Seth T. Harrison, Andre v. DEVCOM Army Res Lab Adelphi MD 20783 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

The output of a semantic segmentation model on an off-road dataset can provide an accurate description of the terrain and the obstacles contained within it. This output can be leveraged to determine the presence of barriers in an image. An obstacle is anything that may obstruct a portion of the region of traversal, while we define a barrier as something that will bisect the region of traversal to create two disjoint regions that would otherwise be connected if not for its presence. Detecting instances of barriers requires more than learning the correct label for a standard 2D semantic segmentation model. This paper will present an approach to detect the presence of barriers in the scene by utilizing the traversability of the semantic classes of non-traversal and the pose of that class(es) about other classes of non-traversal in the scene to define an object as a barrier. For this approach, semantic segmentation is leveraged to assign the classes within an image as "traversable" and "non-traversable". Our approach fuses visible camera segmentation models with LiDAR point cloud data to estimate the local environment's semantic classes and 3D geometry. To assess the algorithm's accuracy, it will be presented with a multitude of scenarios that either contain barriers or not, and its output will be compared to the intention of the environment it was placed in.

关键词： Obstacle detection scene understanding multi-modal fusion computer vision Barricade detection multi-modal perception

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：