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Classification of Water Apple Leaf Varieties using Transfer Learning

Classification of Water Apple Leaf Varieties using Transfer ...

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Artificial Intelligence Robotics, Signal and image processing (AIRoSIP), international conference on

作者： Agus Pratondo Toufan Tambunan Astri Novianty Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Water apple (Syzygium aqueum) is a fruit that comprises numerous variants, and precise identification of these variants is essential for distinguishing their leaves accurately. This research presents a study on water apple leaf variant classification utilizing the VGG16 deep learning model. The objective was to distinguish between seven distinct water apple leaf variants, namely bol, citra, dalhari, deli merah, king rose, kiojok, and susu. A comprehensive dataset was compiled, consisting of a balanced distribution of 99 to 100 leaf images per variant. The research involved two experiments: one with random weight initialization and the other with pre-trained weights from the imageNet dataset. The results demonstrated the significant impact of pre-trained weights initialization, as the model achieved an impressive accuracy of 91.69% compared to 47.79% with random weight initialization. The high accuracy, precision, recall, and F1-Score values indicated the model's effectiveness in accurately classifying the water apple leaf variants. This achievement holds promising potential for diverse practical applications in agriculture, botanical research, education, and conservation efforts.

关键词： Deep learning Accuracy image processing Transfer learning Education Crops Learning (artificial intelligence) Water conservation Reliability Robots

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Indoor 3D Visible Light Positioning Analysis with Channel Estimation Errors

Indoor 3D Visible Light Positioning Analysis with Channel Es...

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international conference on Systems, Signals and image processing, IWSSIP

作者： Dimitrios Bozanis Nikos G. Evgenidis Vasilis K. Papanikolaou Pavlos S. Bouzinis Sotiris A. Tegos Alexis A. Dowhuszko Panagiotis D. Diamantoulakis George K. Karagiannidis Department of Electrical and Computer Engineering Wireless Communications and Information Processing Group (WCIP) Aristotle University of Thessaloniki Thessaloniki Greece Institute for Digital Communications (IDC) Friedrich-Alexander Universitat Erlangen-Nurnberg (FAU) Germany Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Communications and Networking Aalto University Espoo Finland Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon

Since radio-frequency signals are affected signifi-cantly by multipath fading, the localization accuracy they offer cannot meet the sixth generations (6G) demanding specifications. On the other hand, visible light positioning (VLP) has emerged as a low cost alternative that takes into account the highly deterministic channel characteristics of visible light communications to provide cm-level accuracy provided that the investigated spaces are adequately covered by LEDs. However, imperfect channel state information and modeling can lead to positioning errors. In this paper, localization accuracy is quantified in terms of channel estimation errors via low complexity lateration algorithm. The channel estimation errors can be caused by a variety of issues and the provided analysis is general so that it can be easily applied to a plethora of applications. Finally, Monte Carlo simulations validate the proposed method and offer valuable insights for the design and performance bounds of VLP systems.

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Biomedical image Segmentation Using Integrated FCM Clustering Modified with Regularized Level Set Method

Biomedical Image Segmentation Using Integrated FCM Clusterin...

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Disruptive Technologies (ICDT), international conference on

作者： Annu Mishra Pankaj Gupta Peeyush Tewari Department of Computer Science BIT Mesra Ranchi Noida India Department of Computer Science BIT Mesra Ranchi Ranchi Indis Department of Mathematics BIT Mesra Ranchi Jaipur India

Biomedical image segmentation is used widely for various diagnosis of various diseases and other medicinal purposes and help the radiologist and doctor fraternity to reduce their work and help them concentrate more on their research for new diseases. Researchers and medical practitioners use applications based on image segmentation for detecting abnormalities as well as analyzing the effect of certain deformations or deviations quantitatively. However, there are various issues faced while carrying out this task. The primary reason is the presence of inherent noise, the non-uniform intensity of the pixels, and other artifacts. The presence of artifacts not only limits the process of image segmentation but also increases the computational time for the segmentation process. In biomedical images, the problem is more complicated and recurrent. This is due to the different anatomical structures and multi-modal systems available. In this paper, a new algorithm is proposed where a modified fuzzy C-means (MFCM) clustering algorithm is integrated with Regularized Level set method to enhance the efficiency of the image segmentation process which improves the analysis exercise of the image processing system. The approach encompasses two crucial steps. Initially, the image is segmented using the Modified FCM. The MFCM approach has two basic updates with respect to the conventional FCM [1]. Firstly, we introduce a factor to the conventional FCM and secondly, Euclidean distance is replaced with the kernel-dependent distance measure. The factor increases the speed of computation of the FCM algorithm. Replacing the Euclidean distance with a kernel-dependent distance measure makes the algorithm more robust. After the initial segmentation, the Regularized Level Set method was used to refine the result and track the variation boundaries. The regularized level set method solves the reinitialization problem faced in the conventional level set method and enhances the capability and effici

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Data Locality Aware Computation Offloading in Near Memory processing Architecture for Big Data applications

Data Locality Aware Computation Offloading in Near Memory Pr...

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international conference on High Performance Computing

作者： Satanu Maity Mayank Goel Manojit Ghose Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

The data-intensive applications of today's big data era often produce a large memory footprint. As a result, a significant volume of data needs to travel from memory to the CPU under the traditional Von-Neumann computing paradigm. Near-memory processing (NMP) or processing-in-memory (PIM) is a potential alternate computation framework where a computation unit is placed near the memory (or inside the memory) and a portion of an application is executed on it (termed computation offloading) aiming to reduce the amount of data movement and its consequences. Although a few computation offloading strategies have been proposed in recent times, the existing approaches do not consider the data locality offered by the last level cache and the overall execution time of the application while designing their policies. In this paper, we propose a data locality-aware computation offloading strategy for a hybrid computing system comprising the host processor and NMP-enabled 3D memory. After the application code is instrumented using the LLVM compiler framework, the strategy offloads a portion of an application to NMP if its estimated overall execution time is less. An extensive simulation performed on a set of standard simulators for a bunch of large graph-based application benchmarks reports the effectiveness of the proposed strategy by achieving a maximum speedup of 40% and 11.8% as compared to the host-only configuration and the state-of-art policy, respectively. The proposed strategy also reduces the off-chip data transfer and energy consumption by a significant margin as compared to the host-only configuration (avg 27%) and the state-of-art policy (avg 28%). Further, the proposed policy reduces the LLC miss rate by 57% as compared to the state-of-art policy.

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Cost-Effective image based Skin Disease Detection in Resource-Constrained Settings using Machine Learning

Cost-Effective Image based Skin Disease Detection in Resourc...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： G. S. Annie Grace Vimala E. Jones Merlin M. Amanullah E. Manigandan Bura Vijay Kumar S. Padmakala Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of CSE Sri Ramakrishna college of Engineering Perambalur India Department of CSE Saveetha School of Engineering SIMATS Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India

In regions like India, where skin diseases are prevalent due to hot weather and desert conditions, this research study presents a cost-effective solution for early detection and classification of skin diseases using image processing techniques. Leveraging digital images of affected skin areas, the proposed approach utilizes image analysis, including feature extraction through a pretrained Regional Convolutional Neural Network (RCNN), followed by Support Vector Machine (SVM) classification. The system achieves a commendable 82% accuracy rate in identifying Melanoma, a particularly dangerous form of skin disease. With the scarcity of medical knowledge among the general public and the potential delay in symptom recognition, this simple and efficient method, requiring only a camera and a computer, can bridge the diagnostic gap, aiding in timely disease identification and severity assessment. The dataset compilation adheres to ethical considerations, ensuring patient privacy. By addressing issues related to database creation and image dimensions, the research contributes to enhancing the performance of skin disease detection systems, ultimately offering an accessible and impactful tool for healthcare professionals and individuals alike.

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A Fast Approach for Generating Unidentifiable Adversarial Samples

A Fast Approach for Generating Unidentifiable Adversarial Sa...

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Frontiers Technology of Information and computer (ICFTIC), ieee international conference on

作者： Yunfei Jia Risheng Guo Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

In recent years, Deep Neural Networks (DNN) have been widely used to solve many complicated tasks. However, several literatures have reported small perturbations in the target can obviously injury the performance of DNN system. This hinders the wide applications of DNN system in some safety-critical scenarios. Previous studies on adversarial samples generation usually arbitrarily and blindly to change all pixels to some extent. Unfortunately, the results can be identified by naked eyes although they can fool the DNN system. But in some cases, DNN system is just an auxiliary tool, people will also be employed to validate the identified results. This paper proposed a fast approach to generating adversarial samples with slightly changed pixels, with the purpose of deceiving people’s eyes. Firstly, the important pixels which makes more impact on output are identified by layer-by-layer weight analysis; secondly, these pixels are changed slightly by a fast gradient ascent method to check the output of DNN system; finally, the indicator of imperceptibility is used to valuation the change into an eye unrecognizable extent. Two well-known datasets are employed to validate the effectiveness of our approach.

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Leveraging SuperGlue and DKM for Deep Learning-Based Robust image Matching Towards Efficient 3D Reconstruction 23

Leveraging SuperGlue and DKM for Deep Learning-Based Robust ...

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Proceedings of the 2023 5th international conference on Video, Signal and image processing

作者： Zhangwei Gan School of Computer Engineering and Science Shanghai University China

ISBN: (纸本)9798400709272

image matching, which is a cornerstone in 3D reconstruction, poses significant and considerable challenges when applied to diverse, unstructured image collections. This paper presents a robust methodology employing SuperGlue and DKM to address these challenges. Initially, EfficientNet is employed to retrieve matching image pairs based on global feature extraction. Subsequently, SuperGlue identifies correspondences between 2D features across these image pairs, while DKM manages large geometric deformations and appearance variations, enhancing the robustness and precision of the matching process. In the Kaggle competition image Matching Challenge 2023, our approach demonstrated its effectiveness, securing the 34th position among 494 teams and achieving a mean Average Accuracy (mAA) score of 0.470. This achievement not only underscores the potential of our method in real-world applications, particularly in 3D reconstruction from unstructured image collections, but also contributes to the advancement of efficient 3D reconstruction techniques. The implications of this work extend to various applications, including mapping services, cultural heritage preservation, and numerous online services, paving the way for future research in 3D vision tasks and emphasizing the importance of robust image matching techniques in the broader field of computer vision.

关键词： 3D reconstruction DKM Deep learning image matching SuperGlue

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Effective Whole-body Pose Estimation with Two-stages Distillation

Effective Whole-body Pose Estimation with Two-stages Distill...

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international conference on computer Vision Workshops (ICCV Workshops)

作者： Zhendong Yang Ailing Zeng Chun Yuan Yu Li Tsinghua Shenzhen International Graduate School International Digital Economy Academy (IDEA)

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an image. This task is challenging due to multi-scale body parts, fine-grained localization for low-resolution regions, and data scarcity. Meanwhile, applying a highly efficient and accurate pose estimator to widely human-centric understanding and generation tasks is urgent. In this work, we present a two-stage pose Distillation for Whole-body Pose estimators, named DWPose, to improve their effectiveness and efficiency. The first-stage distillation designs a weight-decay strategy while utilizing a teacher’s intermediate feature and final logits with both visible and invisible keypoints to supervise the student from scratch. The second stage distills the student model itself to further improve performance. Different from the previous self-knowledge distillation, this stage finetunes the student’s head with only 20% training time as a plug-and-play training strategy. For data limitations, we explore the UBody dataset that contains diverse facial expressions and hand gestures for real-life applications. Comprehensive experiments show the superiority of our proposed simple yet effective methods. We achieve new state-of-the-art performance on COCO-WholeBody, significantly boosting the whole-body AP of RTMPose-l from 64.8% to 66.5%, even surpassing RTMPose-x teacher with 65.3% AP. We release a series of models with different sizes, from tiny to large, for satisfying various downstream tasks. Our code and models are available at https://***/IDEA-Research/DWPose.

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Unveiling Network Anomalies: A Comparative Study of Real-time Log-Based Detection Approach

Unveiling Network Anomalies: A Comparative Study of Real-tim...

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ieee India conference (INDICON)

作者： Deepika Marella Roopa Navya Muthi Buchammagari Avinash Reddy Kaniganti Priyanka Saraswathi Vijender Busi Reddy Nenavath Srinivas Naik Computer Science and Engineering IIIT Naya Raipur Raipur India Data Science and Artificial Intelligence IIIT Naya Raipur Raipur India Advanced Data Processing Research Institute (ADRIN) Secunderabad Secunderabad India Computer Science and Engineering IIITDM Kurnool Kurnool India

In this paper, we address the issue of detecting anomalies in network log files. In laboratory setup, log files are generated by network devices such as routers, switches, firewalls, and servers. These log files contain information about network activities, such as network traffic, device configurations, user activity, and security events. Analyzing log files on computer networks is essential for identifying potential security threats and network issues. Researchers have sought to develop quick and effective methods to identify anomalies in a computer system by examining its log data as a result of this significant utility. In our work, we have used a machine-learning approach called Isolation Forest (iForest) and also examined clustering algorithms, Ordering Points To Identify Cluster Structure, referred to as OPTICS Clustering, and the Density-Based Spatial Clustering of applications with Noise (DBSCAN) algorithm, for detecting anomalies in real-time log datasets. The performance of each model is evaluated and compared. Finally, we conclude by presenting the experimental results that demonstrate the effectiveness of the suggested method and future directions in real-time log-based anomaly detection, highlighting the need for more advanced and efficient methods to deal with the increasing complexity and scale of modern networks.

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Atmospheric Transmission and Thermal Inertia Induced Blind Road Segmentation with a Large-Scale Dataset TBRSD

Atmospheric Transmission and Thermal Inertia Induced Blind R...

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international conference on computer Vision (ICCV)

作者： Junzhang Chen Xiangzhi Bai Image Processing Center Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Advanced Innovation Center for Biomedical Engineering Beihang University

computer vision-based walking assistants are prominent tools for aiding visually impaired people in navigation. Blind road segmentation is a key element in these walking assistant systems. However, most walking assistant systems rely on visual light images, which is dangerous in weak illumination environments such as darkness or fog. To address this issue and enhance the safety of vision-based walking assistant systems, we developed a thermal infrared blind road segmentation neural network (TINN). In contrast to conventional segmentation techniques that primarily concentrate on enhancing feature extraction and perception, our approach is geared towards preserving the inherent radiation characteristics within the thermal imaging process. Initially, we modelled two critical factors in thermal infrared imaging - thermal light atmospheric transmission and thermal inertia effect. Subsequently, we use an encoder-decoder architecture to fuse the feathers extracted by the two modules. Additionally, to train the network and evaluate the effectiveness of the proposed method, we constructed a large-scale thermal infrared blind road segmentation dataset named TBRSD consists 5180 pixel-level manual annotations. The experimental results demonstrate that our method outperforms existing techniques and achieves state-of-the-art performance in thermal blind road segmentation, as validated on benchmark thermal infrared semantic segmentation datasets such as MFNet and SODA. The dataset and our code are both publicly available in https://***/chenjzBUAA/TBRSD or http://***/***.

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