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Bringing Attention to image Anomaly Detection 21st

Bringing Attention to Image Anomaly Detection

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21st international conference on image Analysis and processing (ICIAP)

作者： de Nardin, Axel Mishra, Pankaj Piciarelli, Claudio Foresti, Gian Luca Univ Udine Via Sci 206 I-33100 Udine Italy

ISBN: (纸本)9783031133213;9783031133206

Detecting anomalies in images is a task with several relevant real-world applications, e.g. industrial inspection. Building on the existing RIAD (Reconstruction by Inpainting for visual Anomaly Detection) framework, we introduce an attention-based component to improve the model performance. Furthermore we propose a different approach to image masking which leverages the selection of multiple random patches at a single scale in the original images. Through the provided experimental results we show how the novelties introduced by this work consistently improve the performance of the baseline approach over the various classes of the heterogeneous MVTec benchmark dataset across all the metrics considered.

关键词： Deep anomaly localization Patch-based anomaly localization Attention-based anomaly localization Self-supervised learning

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A Review on Plant Leaf Disease Detection using Deep learning 1

A Review on Plant Leaf Disease Detection using Deep Learning

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1st international conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022

作者： Saifi, Suhail Mukund, Bal Alam, Danish Khanam, Ruqaiya Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Identification of plant disease plays an important role as it prevents stunted growth which causes bad effects on yields. As agriculture plays a vital role in the Indian economy and different countries of the world, so there is a need to prevent losses in terms of production, quality, and quantity of agriculture yields due to plant disease. Earlier farmers used to monitor plant disease with the naked eye which was time-consuming and requires a lot of expertise such as being able to identify a disease and disease-causing agent. But nowadays with advancements in technology, smart farming, and automatic techniques, plant disease can be easily identified and proper diagnosis can be done. It reduces a lot of work of monitoring such as in the case of big farms. Also at an early stage, it detects the symptoms of plant disease when they first appear on leaves. This paper reflects the potential of one such method-Plant disease detection using machine learning, using this one can detect plant disease. It includes the use of image processing techniques with the help of a machine learning algorithm to get a clear and defined image or to extract some useful insight from it. The application of machine learning in agriculture discussed in this paper and have shown the experimental results in the form of accuracy and practical useability. Also, Convolution Neural Network (CNN) is used for image classification. CNNs are equipped with input, output, and hidden layers which help in process and in image classification. © 2022 IEEE.

关键词： K-means clustering

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Vehicle Detection from Aerial imagery Using Principal Component Analysis and Deep learning 13th

Vehicle Detection from Aerial Imagery Using Principal Compo...

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13th international conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Ayush Kumar, C.S. Maharana, Advaith Das Krishnan, Srinath Murali Hanuma, Sannidhi Sri Sai Sowmya, V. Ravi, Vinayakumar Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia

ISBN: (纸本)9783031274985

The main goal of object detection is to identify and find one or more effective targets in still or video data. It covers a wide range of techniques, including image processing, pattern recognition, and machine learning. The scope of this study is to detect small vehicles in an uncontrolled environment from aerial photographs using effective pre-processing and deep learning algorithms. The model architecture is divided into two phases: Training and Detection. Cropping and extraction of the training samples, feature representation, and classification are all part of the training step. The detection phase comprises of extracting the regions of interest, feature extraction, and classification. The tests are carried out using the Vehicle Detection in Aerial imagery (VEDAI) dataset. In this paper, we explore the feasibility of dimensionality reduction as pre-processing through Principal Component Analysis (PCA) for effective detection of vehicles in aerial imagery;used to remove unwanted features to remove the common misclassifications caused due to ambiguity of small objects. A comparative study between Deep learning Models such as ResNet50 and MobileNetv1 based on the proficiency to detect small objects, were coupled with PCA pre-processing to provide the observations. ResNet50 gave a classification accuracy of 85%, whereas MobileNetv1 gave a classification accuracy of 76.25%. From the experimental results, when PCA pre-processing is coupled with architectures comprising skip connections like ResNet50, the misclassification rate of vehicles in aerial imagery was brought down drastically, providing a better detection rate comparable to existing benchmark. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Principal component analysis

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Out-of-Distribution Detection Using Outlier Detection Methods 21st

Out-of-Distribution Detection Using Outlier Detection Method...

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21st international conference on image Analysis and processing (ICIAP)

作者： Diers, Jan Pigorsch, Christian Friedrich Schiller Univ Jena Furstengraben 1 D-07743 Jena Germany

ISBN: (纸本)9783031064333;9783031064326

Out-of-distribution detection (OOD) deals with anomalous input to neural networks. In the past, specialized methods have been proposed to identify anomalous input. Similarly, it was shown that feature extraction models in combination with outlier detection algorithms are well suited to detect anomalous input. We use outlier detection algorithms to detect anomalous input as reliable as specialized methods from the field of OOD. No neural network adaptation is required;detection is based on the model's softmax score. Our approach works unsupervised using an Isolation Forest and can be further improved by using a supervised learning method such as Gradient Boosting.

关键词： Out-of-distribution detection Outlier detection Isolation Forest

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Fast Calculation of the Local Entropy of a Digital image Using machine learning 15th

Fast Calculation of the Local Entropy of a Digital Image Usi...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bavrina, Alina Sergeyev, Vladislav IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS Molodogvardeyskaya 151 Samara443001 Russia Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia

ISBN: (纸本)9783031810824

This paper proposes a method for fast construction of the entropy field calculated in a sliding local window. The method is based on the representation of the local histogram by a truncated series using cosine basis functions and approximation of the entropy value using regression models. Experiments show a gain in time compared to the direct calculation of the entropy field with a good enough quality of approximation. The method can be applied for other histogram features calculation (e.g., rank statistics). It is reasonable to use the proposed method for obtaining additional features when analyzing large size remote sensing images, where the processing speed is critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linear regression

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Fabric Defect Detection System Using image processing

Fabric Defect Detection System Using Image Processing

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Cognitive, Green and Ubiquitous Computing (IC-CGU), international conference on

作者： Mukund Kulkarni Puja Chavan Cholke Parimal Vasmatkar Shravani Tambe Samarth Anuse Siddhant Telgote Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

This study introduces a Textile Analysis and Quality Control System in response to the urgent need for innovation in the textile sector. The main objective is to tackle the shortcomings of the existing quality control techniques, which are mostly reactive and do not have the ability to monitor in real-time. Key goals of the research include developing sophisticated image processing algorithms for autonomous defect recognition, improving the accuracy and efficiency of defect classification, and incorporating advanced statistical analysis and machine learning techniques to assess textile material quality comprehensively. All of these efforts will contribute to an overall improvement in the accuracy and efficiency of quality control procedures. By creating an intuitive interface that maximizes administrator interactions, the authors make a substantial contribution. This interface makes it easier to input photos, evaluate findings quickly, and change parameters dynamically. In contrast to conventional reactive approaches, the system's proactive approach to quality control, enabled by real-time monitoring, emphasizes the significance of prompt remedial action for continuous quality control during production. The authors' methodology involves methodical steps such as thorough analysis, confirmation, and iterative improvements to demonstrate the system's efficacy and flexibility in a variety of production situations. The strong theoretical basis of the system is strengthened by this repeated process. The research has significant practical ramifications, establishing the suggested Textile Analysis and Quality Control System as an essential instrument for raising accuracy, productivity, and general industry standards for quality. The study creates the foundation for a future in which textile production embraces increased productivity and precision by fusing cutting-edge image processing techniques with time-honored approaches for revolutionary effects.

关键词： Productivity Technological innovation machine learning algorithms statistical analysis Quality control machine learning Ubiquitous computing

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DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection 21st

DroBoost: An Intelligent Score and Model Boosting Method for...

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21st international conference on image Analysis and processing (ICIAP)

作者： Eryuksel, Ogulcan Ozfuttu, Kamil Anil Akyon, Fatih Cagatay Sahin, Kadir Buyukborekci, Efe Cavusoglu, Devrim Altinuc, Sinan OBSS Technol OBSS AI 1606 Cad 4-1-307 Cyberpk Cyberplaza C Blok 3 Kat Ankara Turkey

ISBN: (纸本)9783031133244;9783031133237

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).

关键词： Drone detection Deep learning Object tracking Object detection Synthetic data

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Adults' Pain recognition via Facial Expressions Using CNN-Based AU Detection 21st

Adults' Pain Recognition via Facial Expressions Using CNN-Ba...

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21st international conference on image Analysis and processing (ICIAP)

作者： Vallez, Noelia Ruiz-Santaquiteria, Jesus Deniz, Oscar Hughes, Jeff Robertson, Scott Hoti, Kreshnik Bueno, Gloria Univ Castilla La Mancha VISILAB ETSI Ind Ciudad Real Spain Curtin Univ Curtin Med Sch Bentley WA Australia Pain Check Ltd Suite 40135 Lime St Sydney NSW 2001 Australia Univ Prishtina Fac Med Div Pharm Prishtina Kosovo

ISBN: (纸本)9783031133213;9783031133206

The identification of pain expression by facial analysis is a difficult problem with few effective solutions. Most of the existing facial expression recognition methods are based on the detection of different Action Units (AU) to identify simple expressions such as anger, joy or neutral. In this study, we propose using CNNs pre-trained on previously developed models for pain detection, such as the models included in PainChek, a point-of-care mobile application that uses automated facial assessment and analysis in the assessment of procedural pain in infants, to combine nine AUs identified in video images from the MORPH database without specific pain and from a proprietary database with joint pain. This work also analyses the variability of the AU detection method applied to images of individuals of different age, race and gender. For the database without specific pain, the mean amount of AUs considered as "not present" was 92.38% and 7.62% for "present", obtaining the worst results for the identification of AU6 and AU12 with 83.67% of "present" and 81.20% of "not present". For the joint pain database with ground truth labelled by AU and intensity, the mean accuracy for the 9 AUs was 84.98% with a sensitivity of up to 92% percent for AU25 and AU43.

关键词： Pain facial expression CNN Action unit recognition

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A machine learning Based Framework for Pre-processing and Classification of Medical images 1

A Machine Learning Based Framework for Pre-processing and Cl...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Beram, Shehab Mohamed Pallathadka, Harikumar Patra, Indrajit Prabhu, P. Department of Computing and Information Systems Sunway University Malaysia Manipur International University Manipur Imphal India NIT West Bengal Durgapur India Department of IT Directorate of Distance Education Alagappa University Karaikudi India

ISBN: (纸本)9781607685395

Disease diagnosis and therapy rely heavily on medical imaging. For example, image processing has been used to depict different cell types using textural aspects in the medical and biological fields, as well as to distinguish between living and dead cells using pictures. There are major difficulties in reproducibility and predictability since traditional techniques of microscopic image analysis rely on a labor-intensive approach with a restricted number of bone samples. To construct a novel technique, image processing in a digital system was used to include several steps, including pre-processing, object representation, feature extraction, classifying, and picture interpretation. It was found that the Wavelet denoising tool was a potent picture enhancing technique. Second-stage preprocessing of pictures is validated by the wavelet, which divides the image into subbands like near, horizontal and vertical estimations. To cure at the beginning stage, any bone fracture or abnormality (cancer) must be anticipated. Human osteosarcoma, a malignant primitive bone tumor, is the most common form of bone cancer in the human body. Metastasis is a term for the spread of stage condition to other sections of the body or to organs. Our proposed work was associated with preprocessing to eliminate noise and get smooth images. This procedure would aid in improving image quality and removing false segments. KNN, CNN, and ANN classification algorithms are used to classify image datasets. © The Electrochemical Society

关键词： Classification Deep learning K-Means machine learning Segmentation Wavelet Denoising

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Deep Transfer learning-Based Rice Leaves Disease Diagnosis and Classification model using InceptionV3 1

Deep Transfer Learning-Based Rice Leaves Disease Diagnosis a...

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1st international conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022

作者： Rukhsar Upadhyay, Santosh Kumar Ajay Kumar Garg Engineering College Department of CSE Ghaziabad India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Rice (Oryza sativa) is the globe's favorite eatable grain. It is the important diet for more than half of the globe's population as a source of energy. Abiotic and biotic elements such as weather, soil fertility, temperature, pests, pathogens, viruses, and others influence rice grain yields production amount and quality. Farmers invest a lot of time and energy in disease control, and they detect diseases with their penniless human eye strategy, which causes unhealthy cultivation. To avoid biased, incorrect, and inefficient manual detection, we are introducing an automatic plant disease recognition and classification method by using a transfer learning technique. The deep neural networks algorithm is a convolutional learning technique that has been successfully used for computer vision problems like as image processing, object identification, and image identification. InceptionV3 is a type of CNN architecture used in our research to recognize diseases in rice leaf images using a transfer learning approach. The developed model's results are obtained for the classification problem, and it achieved a high accuracy of 90.77 percent. © 2022 IEEE.

关键词： Grain (agricultural product)

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