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From One to Many Lorikeets: Discovering image Analogies in the CLIP Space 27th

From One to Many Lorikeets: Discovering Image Analogies in...

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27th international conference on pattern recognition, ICPR 2024

作者： Xing, Songlong Peruzzo, Elia Sangineto, Enver Sebe, Nicu University of Trento Trento Italy University of Modena and Reggio Emilia Modena Italy

ISBN: (纸本)9783031781889

Drawing analogies between two pairs of entities in the form of A:B::C:D (i.e. A is to B as C is to D) is a hallmark of human intelligence, as evidenced by sufficient findings in cognitive science for the last decades. In recent years, this property has been found far beyond cognitive science. Notable examples are word2vec and GloVe models in natural language processing. Recent research in computer vision also found the property of analogies in the feature space of a pretrained ConvNet feature extractor. However, analogy mining in the semantic space of recent strong foundation models such as CLIP is still understudied, despite the fact that they have been successfully applied to a wide range of downstream tasks. In this work, we show that CLIP possesses the similar ability of analogical reasoning in the latent space, and propose a novel strategy to extract analogies between pairs of images in the CLIP space. We compute all the difference vectors of a pair of any two images that belong to the same class in the CLIP space, and employ k-means clustering to group the difference vectors into clusters irrespective of their classes. this procedure results in cluster centroids representative of class-agnostic semantic analogies between images. through extensive analysis, we show that the property of drawing analogies between images also exists in the CLIP space, which are interpretable by humans through a visualisation of the learned clusters. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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SFMatting-800: A Multi-Scene Smoke and Fire image Matting Dataset for Fine-Grained Fire Detection 4

SFMatting-800: A Multi-Scene Smoke and Fire Image Matting Da...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Ma, Shihui Ding, Kun Yan, Hongping School of Information Engineering China University of Geosciences Beijing Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400708831

Fire is a disastrous event that causes immense dangers. Conventional fire detection methods use sensors in indoor settings. By contrast, computer vision-based methods have made substantial advancements in terms of detection accuracy, speed, and versatility. However, existing approaches often treat fire detection as image classification, image segmentation, and object detection problems, overlooking the characteristic of smoke, including its floatability, ambiguity, and transparency. To foster advancements in this field, we formalize fire detection as the image matting problem and introduce a novel smoke and fire matting dataset SFMatting-800. this dataset not only features high-quality annotations on transparency but also encompasses diverse information regarding scene types and fire attributes. these annotations serve as valuable resources for training, evaluating image matting models, and expanding data through compositing with a substantial background dataset. With SFMatting-800, we have conducted comprehensive evaluations using traditional image matting methods, trimap-based and trimap-free deep image matting methods. the experimental results not only demonstrate the value of our dataset for the fine-grained fire detection task, but also help to expose the problem of existing matting methods in processing extensive regions with high transparency and misidentification between background and targets. the dataset will be made publicly available. © 2023 Owner/Author.

关键词： Fires

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Detecting Background Dynamic Scenes using Naive Bayes Classifier Analysis Compared to CNN Analysis 4

Detecting Background Dynamic Scenes using Naive Bayes Classi...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Borah, Asha Rani New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300888

Surveillance, object tracking, and autonomous vehicles rely on background dynamic scene detection and analysis. this paper compares the Naive Bayes classifier and CNNs for background emotional scene recognition. Background passionate scenes provide obstacles with complicated and unpredictable motion patterns, and the study begins. A professionally managed dataset includes moving items, changing illumination, and environmental variations. Pre-processing and annotation help with training and evaluation. Background dynamic scene detection begins with Naive Bayes. Naive Bayes, a probabilistic approach that presupposes feature independence, is suitable for classification. Different feature extraction methods and Naive Bayes algorithms are used to evaluate background dynamics capture. Next, CNN algorithms consider the dataset. CNNs are great at detecting spatial connections in image and video data. To maximize background dynamic scene identification, CNN architectures are created and trained using different network depths, kernel sizes, and pooling algorithms. Accuracy, precision, recall, and F1-score are used to evaluate Naive Bayes and CNN algorithms. the results are extensively evaluated to assess each method's background dynamic scene-handling capabilities. © 2023 IEEE.

关键词： Feature extraction

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PyramidTabNet: Transformer-Based Table recognition in image-Based Documents 17th

PyramidTabNet: Transformer-Based Table Recognition in Image-...

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17th international conference on Document Analysis and recognition (ICDAR)

作者： Umer, Muhammad Mohsin, Muhammad Ahmed Ul-Hasan, Adnan Shafait, Faisal Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan Natl Ctr Artificial Intelligence NCAI Deep Learning Lab Islamabad Pakistan

ISBN: (纸本)9783031417337;9783031417344

Table detection and structure recognition is an important component of document analysis systems. Deep learning-based transformer models have recently demonstrated significant success in various computer vision and document analysis tasks. In this paper, we introduce PyramidTabNet (PTN), a method that builds upon Convolutionless Pyramid vision Transformer to detect tables in document images. Furthermore, we present a tabular image generative augmentation technique to effectively train the architecture. the proposed augmentation process consists of three steps, namely, clustering, fusion, and patching, for the generation of new document images containing tables. Our proposed pipeline demonstrates significant performance improvements for table detection on several standard datasets. Additionally, it achieves performance comparable to the state-of-the-art methods for structure recognition tasks.

关键词： deep learning image transformer image processing data augmentation table augmentation table segmentation table detection structure recognition

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Characterization of Background Surface Noise for Metrological Applications 4th

Characterization of Background Surface Noise for Metrologica...

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4th international conference on Innovations in Computational Intelligence and computer vision, ICICV 2024

作者： Ashok, Arjun K. Prasad, Jeevan Bhaskar, Sabareesh S. Sundaram, Ganesh Department of Physics Amrita Vishwa Vidyapeetham Amritapuri690525 India

ISBN: (纸本)9789819769940

Measurement of surface profiles of manufactured components are of critical importance and are traditionally carried out using contact probes and using a few optical methods in the past few decades. With advances in digital image processing techniques and computational power, it is advantageous to use digital optical metrology for non-contact measurements—a field still under active development. For instance, for two-dimensional form measurements, essential steps in roundness characterization include capturing a good digital image of top-view of a sample and processing the image to compute boundary-edge. Accuracy and difficulty of this computation is influenced by textural background noise—a problem of significant complexity in image segmentation and measurement, and methods specific to applications are being developed. For experimental setups with backlighting, the background surrounding the region of interest is noise-free. However, for images with reflected lighting, noise due to background surface and dust must be dealt with. Working with an image of a top-view of a cylinder, we define a new measure of background noise using Canny-edge output for this purpose and present a method of characterizing the background surface noise. We study the effect of variation in the Canny-edge parameters—Gaussian kernel width (sigma) and threshold—on this background noise and arrive at a transition threshold for which the background noise is zero for various sigma values. For the chosen experimental conditions, we find that the background noise is absent for thresholds above 0.4852 for any sigma, and there is a critical sigma of 2.5 at which the transition threshold is minimum at 0.0777. these values give valuable insights on the choice of these parameters and on the robustness of the estimated boundary-edge. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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Extracting Graphs from Plant Leaf Venations Using image processing 1

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4th international conference on image processing and Capsule Networks, ICIPCN 2023

作者： Balangcod, Ashlyn Kim D. Pabico, Jaderick P. Department of Mathematics and Computer Science University of the Philippines Baguio Baguio Philippines Institute of Computer Science University of the Philippines Los Baños College Laguna Philippines

ISBN: (数字)9789819970933

ISBN: (纸本)9789819970926

Leaf venation is an important characteristic of plant species that can lead to the identification of a plant. the study of leaf venation is laborious if done by manual inspection. this study proposes a leaf venations’ extraction process requiring minimal algorithms of image processing. Canny edge detection and Zhang-Suen thinning algorithm were used to extract the venation of the leaf images. Using ground truth tracing, the average values computed are 0.522 for correctness and 0.406 for completeness of the extracted venation. this study also presents the use of graph metrics to describe the leaf venations of plants. Using thirty images of five plant species, leaf venations up to the tertiary veins are compared using graph properties: size, volume, number of claws, number of wedges, and maximum degree. this study proposes these mentioned graph metrics as additional characteristics of leaf venations as results show a possible pattern for the five plant species. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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Visual Interface Design and Application of image processing System

Visual Interface Design and Application of Image Processing ...

引用

Electronic Information Engineering and computer Science (EIECS), 2021 international conference on

作者： Xiaoxue Sun Chen Chen Guoren Gao Qilu Shi Mechanical Engineering Department Jilin Agriculture Science and Technology University Jilin Jilin China State Grid Liaoning Extra High Voltage Company Liaoning Anshan China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

With the rapid development of computer technology, image processing technology, as an important branch in the field of computer vision, has been widely researched and applied. In order to facilitate users to perform image processing and analysis, an image processing system is designed based on Matlab-GUI. this system integrates functional modules such as color/brightness adjustment, basic functions, spatial filtering and denoising, image enhancement/restoration, adding noise, and edge detection. Combined with RGB waveform diagrams and RGB component waveform diagrams, it realizes visual analysis of image color distribution and tonal hierarchy. the system has a friendly interface and is easy to operate. It can provide strong support for tasks such as image recognition and promote the continuous innovation and development of image processing technology.

关键词： image transformation Filtering image color analysis image processing image edge detection Brightness Noise reduction Filtering algorithms MATLAB Colored noise

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Segment Anything in Medical images with nnUNet 1

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international Challenge on Segment Anything in Medical images on Laptop held in conjunction with the IEEE/CVF conference on computer vision and pattern recognition, CVPR 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. this indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Non-invasive Technique for Detecting Glycosuria through image processing and Deep Learning Approaches 5th

Non-invasive Technique for Detecting Glycosuria Through Imag...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Teja, Chitturi Siva Nitheesha, Gokaraju Kumar, Boyidi Ravi Krishna, Bandaru Radha Naik, Desavath Madhu Pareek, Prakash Singh, Lokendra Department of ECE Vishnu Institute of Technology Bhimavaram534202 India Dehradun248002 India

ISBN: (纸本)9783031770746

Glycosuria level monitoring is a fundamental aspect of diabetes management, enabling proactive and personalized care to prevent complications, improve overall health, and enhance the quality of life for individuals with diabetes. Conventional glycosuria monitoring methods often involve invasive procedures, potentially risking the immune system, especially with repeated injections. the research focuses on a non-invasive method utilizing urine samples for glycosuria testing to address these concerns. the proposed method integrates image processing techniques, such as Gaussian filtering and image resizing, to optimize input data with machine learning approaches. Deep learning, especially convolutional neural networks is employed for their ability in feature extraction and pattern recognition. the system aims to achieve high accuracy in categorizing glycosuria levels into groups such as diabetes, prediabetes, and normal, providing a reliable and non-invasive alternative to traditional glycosuria monitoring techniques. this approach shows promise in enhancing patient compliance and overall health monitoring for individuals requiring regular glucose assessment. the accuracy of the proposed method based on CNN achieves 98.54% where the existing method based on support vector machine (SVM) achieves only 88.46%. it is evident from simulation results, the proposed method attained 11.39% improved accuracy when compared to existing SVM. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Accuracy Classification Convolutional Neural Network Deep learning gaussian filter Glycosuria

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Automated computer Aided Detection of Diabetic Retinopathy using Machine Learning Hybrid Model 13

Automated Computer Aided Detection of Diabetic Retinopathy u...

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13th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kubde, Aishwarya Mohod, Sharad W. Dept. of Electronic Engineering Prof. Ram Meghe Institute of Technolgy & Research Badnera Amravati India

ISBN: (纸本)9781713857938

Diabetic retinopathy is spreading dangerously worldwide among people with diabetes, leading to reduced vision and completely blindness. In this paper, a technique is proposed to identify diabetic retinopathy using the fundus image obtained from the patient's retina. the method involves processing the digital image on the fundus image, which help the ophthalmologist in examine DR. Micro-aneurysm, which is considered as first stage of diabetic retinopathy has been diagnosed using Neural Network. the proposed Support Vector Machine has been compared to existing methods - Naive Bayes classifier. Experimental validation is done with MATLAB/SIMULINK software. the preprocess image has been applied as input data for neural network pattern recognition. A significant improvement has been observed in terms of Sensitivity, Specificity and Accuracy compared to the aforementioned existing methods. © Grenze Scientific Society, 2022.

关键词： pattern recognition

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