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In-domain Self-supervised Learning for Plankton image Classification on a Budget

In-domain Self-supervised Learning for Plankton Image Classi...

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2025 IEEE/CVF Winter conference on applications of Computer vision Workshops, WACVW 2025

作者： Ciranni, Massimiliano Gjergji, Ani Maracani, Andrea Murino, vittorio Pastore, vito Paolo University of Genoa MaLGa Dibris Italy Istituto Italiano di Tecnologia Genoa Italy University of Verona Italy

ISBN: (纸本)9798331536626

In the last few years, the abundance of available plank-ton images has significantly increased due to advancements in acquisition system technology. Consequently, a growing interest in automatic plankton image classification has surged. machine learning algorithms have recently emerged to assist in the analysis of this vast quantity of data, supporting traditional manual processing. However, annotating such data is costly and demands significant time and resources, thus requiring data-efficient machine learning solutions. The typical framework for tackling this issue has been the adoption of supervised imageNet pre-trained models, and fine-tuning them on the plankton classification downstream task. Nonetheless, self-supervised pre-training protocols may provide an effective alternative to the supervised approaches using imageNet, while allowing the exploitation of the increasingly large amount of unanno-tated plankton data. To the best of our knowledge, no work systematically analyzes the impact of self-supervised pre-training protocols for plankton image classification. To fill this gap, in this paper, we present a thorough comparison between in-domain (plankton images) and out-of-domain (imageNet) supervised and self-supervised pre-training, in terms of the quality of the corresponding embeddings for plankton image classification. We believe that this work may pave the way for further research in self-supervised protocols for the plankton domain, providing a valuable alternative to imageNet, and exploiting the vast amount of unannotated available plankton images. © 2025 IEEE.

关键词： Self-supervised learning

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machine learning for scene 3D reconstruction using a single image 6

Machine learning for scene 3D reconstruction using a single ...

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conference on Optics, Photonics and Digital Technologies for Imaging applications vi

作者： Knyaz, Vladimir State Res Inst Aviat Syst GosNIIAS 7 Victorenko Str Moscow Russia Moscow Inst Phys & Technol MIPT Moscow Russia

ISBN: (数字)9781510634794

ISBN: (纸本)9781510634794

image-based scene 3D reconstruction is one of the key tasks for many machine vision applications such as scene understanding, object pose estimation, autonomous navigation. A set of reliable and accurate methods for multi-view scene 3D reconstruction has been developed last decades. But a significant drawback of such 3D reconstruction technique is the need for acquiring a large number of images in the processed sequence to obtain an acceptable 3D scene representation. Recently modern convolutional neural network (CNN) models achieve the best quality for object recognition, image segmentation, image translation and some other challenging computer vision problems. The paper proposes a convolutional neural network architecture and a technique for training data preparation which provide a prediction of voxel model of a 3D scene with several objects. For CNN training and evaluation a special dataset was collected and annotated. It contains image sequences of several scenes and corresponding depth images and 3D models of these scenes. The image sequence serves as the primary data used for further scene 3D reconstruction by SfM technique. Structure from Motion processing results in surface 3D models of all objects in the scene and camera positions and orientation for every image in a sequence. Then surface 3D model is transformed into voxel 3D model and segmented into separate objects. Conditional generative adversarial network architecture was developed for 3D reconstruction by single image. Its generative part translates an input color image into an output voxel model. The discriminative part distinguishes the correct output (close to real voxel model) from false output (wrong output voxel model). Both parts are trained simultaneously on the prepared dataset. Evaluation on the testing part of the prepared dataset has demonstrated the ability of prediction 3D models of previously unobserved complex scenes containing several objects. The proposed neural network architectur

关键词： image analysis scene 3D reconstruction voxel 3D model deep learning convolutional neural network generative adversarial neural network dataset

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843969

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications.

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Combining SIFT and BRISK Descriptors to Improve image Matching Accuracy

Combining SIFT and BRISK Descriptors to Improve Image Matchi...

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IEEE Pacific Rim conference on Communications, Computers and Signal processing

作者： Sina Ghaffari Kin Fun Li David W. Capson Department of Electrical and Computer Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

Local descriptor algorithms are foundational in computer vision applications such as image matching and image retrieval. Some local descriptor algorithms extract features containing similar information from images while others extract complementary information. In this work, we investigate the advantages of combining a binary and a non-binary local descriptor algorithm. We propose and compare three methods to combine SIFT and BRISK descriptor algorithms selected because they produce complementary descriptor vectors. First, we propose to combine SIFT and BRISK descriptors using a weighted summation of their individual descriptor distances with learned weights. Our second method converts SIFT into a binary descriptor and concatenates the binary SIFT vector with the BRISK descriptor. The third method is to scale the binary BRISK descriptor vector and concatenate it with the SIFT descriptor. Parameters for combining the descriptors are learned based on the HPatches data set for each of the three methods. Our proposed methods increase the mean Average Precision in the range of 3% to 15.8% over the original BRISK and in the range of 5.9% to 21.8% over the original SIFT algorithm in various evaluation conditions.

关键词： machine learning algorithms image matching Neural networks image retrieval Signal processing algorithms machine learning Signal processing Feature extraction Vectors Data mining

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Generating Caption From images Using Flickr image Dataset

Generating Caption From Images Using Flickr Image Dataset

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International conference on Computing and Networking Technology (ICCNT)

作者： Piyush Sharma Kiranpreet Kaur Aaryan Gandotra Pranay Patial Radhika Sehaj Gaba Department of CSE Chandigarh University Mohali India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond This project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language processing Computer vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results.

关键词： visualization Accuracy Recurrent neural networks image databases Computational modeling Parallel processing Feature extraction Web sites Multimedia communication Object recognition

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Research on Evaluation Method of Aerial image Segmentation Algorithm 7

Research on Evaluation Method of Aerial Image Segmentation A...

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7th International conference on Signal and image processing, ICSIP 2022

作者： Wang, Heng Zhuang, Chuangang Zhao, Jiamei Shi, Rui Jiang, Hai Yuan, Yanrong Guo, Xinyi Xue, Zhichao China Academy of Launch Vehicle Technology Science and Technology on Space Physics Laboratory Beijing100076 China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Aerial image segmentation algorithms are used in many scenarios such as traffic warning, road assistance, terrain mapping and military reconnaissance. It has been widely applied in both military and civilian applications. A lot of researches on target detection and recognition technology in aerial images have been carried out. In addition, technologies such as computer vision and machine learning have been introduced. Compared with the research on image segmentation algorithms, there are relatively few studies on evaluation method segmentation algorithms. In view of the above situation, the evaluation method of aerial image segmentation is established, and the performance of comprehensive evaluation of the medhod for image segmentation algorithms is verified. Specifically, it includes similarity index, misidentification similarity index and distance index. Selecting image data sources with different degrees of complexity are selected in order to verify the effectiveness of the above image segmentation evaluation method. The results show that the evaluation indicators prove that the algorithms can achieve effective target segmentation to a certain extent. The segmentation accuracy rate is above 95%. In view of the accuracy and effectiveness of the aerial image segmentation algorithm, it is expected to be further applied in the field of aerial images. © 2022 IEEE.

关键词： image segmentation

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A Qualitative Study on image Quality Enhancement, Object Detection Methods to Assist visually Impaired Users

A Qualitative Study on Image Quality Enhancement, Object Det...

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International conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： S. Sajini B. Pushpa Department of Computer and Information Science Faculty of Science Annamalai university Chidambaram Tamil Nadu India

Object Detection and Tracking with Recognition (ODTR) is a predominant field that finds significant applications in surveillance and assisting visually Impaired (vi) people. Though ODTR is possible with the implication of Computer vision (CV) models, the achievement of accuracy levels seemed to be meagre due to lack of clarity of images being captured. Hence a hybrid of image processing methods could improve the efficiency of the prediction. The significant objective of this research work is to study and explore the various Digital image processing (DIP) methods to refine the image and Computer vision methods to perform ODTR that could possibly improve the performance of prediction at the end of the detection. The various image processing methods like Filtering, Segmentation and Feature Extraction were studied with the technical methods from MATLAB interface and Object detection and Tracking evaluation methods with Confusion Matrix were analyzed and presented with expected outcomes and discussions. The evaluation methods were presented with expected models for supporting the proposed framework models.

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Object Detection Analysis Study in images based on Deep Learning Algorithm

Object Detection Analysis Study in Images based on Deep Lear...

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International conference on Data Science and Its applications (ICoDSA)

作者： Hary, Christian Mandala, Satria Telkom Univ Sch Comp Jl Telekomunikasi 1 Terusan Buah Batu Bandung 40257 Indonesia Telkom Univ Human Centr HUMIC Engn Jl Telekomunikasi 1 Terusan Buah Batu Bandung 40257 Indonesia

ISBN: (数字)9781665486651

ISBN: (纸本)9781665486651

Deep learning is a subfield of machine learning. Computer vision is one of the technological advances that utilizes deep learning in image processing, object classification, and object detection. In the Object Detection, there have been various models that can detect objects with different characteristics, and with so many models that have been developed, it takes longer to determine which model is suitable for the needs of a project because it requires comparisons between each model. In this study, an analysis was conducted by comparing three models that utilize Deep Learning to detect car and bus objects, namely Faster-RCNN with ResNet50, SSD with MobileNet, and EfficientDet with D0. Each model is run using TensorFlow Object Detection. The models will be trained using a custom dataset containing of 52 images and will be trained in 3000 steps. Based on experiments, it is known that from the comparison of mAP, Faster-RCNN ResNet50 has the highest score of 0.453, and the lowest is EfficientDet D0 with 0.274;for the comparison of Average Recall, Faster-RCNN ResNet50 has the highest score with 0.337, and the lowest is EfficientDet D0 with 0.190, as well as for model size comparison, EfficientDet D0 has the smallest size with 290 MB, and the largest is Faster-RCNN ResNet50 with 1280 MB.

关键词： deep learning object detection computer vision TensorFlow transfer learning

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Quantifying image naturalness using differential curvelet features 36

Quantifying image naturalness using differential curvelet fe...

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36th International conference on image and vision Computing New Zealand (IVCNZ)

作者： Nath, P. Shabari Gandhi, Harsh K. Chouhan, Rajlaxmi Indian Inst Technol Jodhpur Dept Elect Engn Jodhpur Rajasthan India

ISBN: (纸本)9781665406451

Distinguishing computer-generated (CG) images from natural images is an effortless job for the human eye but not for a machine. Artificial or CG image processing services are growing rapidly due to popular smartphone applications and filters in social media applications. The traditional image quality assessment (IQA) metrics are mostly defined for real-world images in terms of attributes describing noises and distortions. In contrast with traditional natural image content, artificial or CG image content has special characteristics that differentiate them from natural images. This difference opens new opportunities of research towards designing metrics that define 'naturalness' in terms of image attributes. In this paper, we investigate how curvelet features of a natural image can represent naturalness of the image. By training various classifiers using differential curvelet features of artwork-like images, we report a novel approach to estimate image naturalness and its performance for various levels of naturalness. The reported results are promising and show potential of improvement using detailed feature engineering.

关键词： Training Measurement image quality image recognition Social networking (online) image synthesis Estimation

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Semantic Segmentation of Multi-Channel Polycrystalline Structure Micrographs Using Convolutional Neural Networks 20

Semantic Segmentation of Multi-Channel Polycrystalline Struc...

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20th IEEE International conference on machine Learning and applications (ICMLA)

作者： Selmaier, Andreas Lutz, Benjamin Kisskalt, Dominik Boernicke, Simon Fuerst, Jens Franke, Joerg Friedrich Alexander Univ Erlangen Nurnberg Inst Factory Automat & Prod Syst Erlangen Germany Siemens Healthineers Technol Ctr Power & Vaccuum Components Erlangen Germany

ISBN: (纸本)9781665443371

Due to ever-increasing data availability, computational power, and algorithmic advances, machine learning enables various novel industrial applications. A field with particularly strong potential for machine learning is computer vision. As part of that area, semantic segmentation refers to a process that links each pixel of an image to a corresponding class. In this task, deep learning has outperformed traditional image processing techniques as well as other classical machine learning techniques and therefore has become the new standard approach. Convolutional neural networks (CNN) are a specific form of deep neural networks, which exploit spatial information for the classification of pixels. This paper presents an approach in which CNNs are optimized and applied to a semantic segmentation problem by using multichannel microscopic images of tungsten flat emitters to determine their surface conditions. These are of strong interest since they may reveal information about potential defects and the life expectancy of the emitter, which is an indispensable component of X-ray tubes as they are used in medical applications.

关键词： manufacturing machine learning computer vision semantic segmentation visual inspection X-ray flat emitters medical devices

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