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Weakly-Supervised Gland Instance Segmentation based on Point Labeling 3

Weakly-Supervised Gland Instance Segmentation based on Point...

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3rd IEEE International conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Wang, Da-Han Ye, Haili Li, Jianmin Zhu, Shunzhi Zhu, Chenyan Fujian Key Labarotory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen China Medical Diagnostic Systems Co. Ltd. Fujian Xiamen China

ISBN: (纸本)9780738112152

Colon cancer is a common digestive tract cancer occurring in the colon. How to effectively segment the glands in the pathological image of the colon is a challenging task. As well as distinguishing the glands from the background, it is necessary to distinguish the boundaries between the different instances of the glands. Common instance segmentation methods require pixel-level instance annotation, which takes a lot of time and resources. The pixel-level weakly supervised instance segmentation method cannot provide sufficient supervision, which makes the quality of gland segmentation poor. In this paper, we propose a weakly supervised gland segmentation method based on point labeling. We trained a glandular point detection model to predict high confidence points in gland images using the supervision information of point labeling. Then, the high-confidence point-assisted training instance segmentation model is used to implement the instance segmentation of the glands. We tested our method on the 2015 MICCAI Gland Challenge dataset, and the experimental results show that our method can effectively segment the instance of the Gland, and its performance is even better than that part of the method by training using the fully supervised. © 2021 IEEE.

关键词： image analysis

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Increasing Accuracy of Crop Production and Yield Forecasting Using Hyper Spectral image analysis

Increasing Accuracy of Crop Production and Yield Forecasting...

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Innovation in Technology (INOCON), IEEE International conference for

作者： Devendra Singh Uttaranchal Institute of Technology Uttaranchal University Dehradun India

Hyper Spectral up-to-date evaluation has up to date be an increasing number of vital up-to-date in precision farming for predicting crop manufacturing and yield forecasting. The era offers an innovative and green way of reliably measuring the fitness of vegetation and crop production ability by providing high-resolution imaging information that allows estimates of crop biomass, vigor, and yield. This technology is mainly helpful in fast crop health assessment, with on-hand visuals that permit fast evaluation within the field. A significant gain of this generation is its capacity to date capture the spectral signature of flora appropriately and precisely, with date consideration advances in precision farming and precision agriculture. Using the technology, researchers have been up-to-date in creating specific spatial assessment maps of crop health, imparting local and nearby statistics approximately crop productiveness capacity. This information can be used up to date optimize management practices up to date increase productivity and ***, the generation has recently become even more accurate by improving synthetic Intelligence (AI) algorithms, such as up-to-date gadgets and hyperspectral up-to-date, enhancing the era’s accuracy and usual reliability in modeling crop fitness. Hyperspectral picture analysis is an increasing number of unique techniques for forecasting crop manufacturing and yield. It involves the spectral measurements of a pattern of plants throughout the visible and up-to-date infrared (NIR) spectrum, which is then utilized to date to create a hyperspectral profile of the sample. analysis of such profiles can reveal the updated presence of yield-figuring components, including updated water, nutrients, and up-to-date biomass. By comparing these components, predictive models of increase estimates may be created. The combination of high-resolution hyperspectral up-to-date analyses with airborne far-off sensing offers a remarkable level of temp

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An improved kernel density estimation with adaptive bandwidth selection for edge detection 3

An improved kernel density estimation with adaptive bandwidt...

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3rd International conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Malarvel, Muthukumaran Singh, Harjeet Nayak, Soumya Ranjan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity University Amity School of Engineering and Technology Uttarpradesh India

ISBN: (纸本)9780738111834

Edge detection has been one of the most active and challenging research aspects in the last three decades in the applications of pattern recognition and machine vision as well. Although, the recent research showed that there has been lots of work reported on the basics of Kernel density estimation (KDE), but the selection of proper bandwidth not yet reported in any literature work, because it was the most challenging issue on both under and over smoothed images in terms of non-parametric KDE. Realizing the aftermath, the current research delivers a well-defined KDE approach which allowed us an adaptive selection of bandwidth by the enactment of Shannon entropy for keeping the effect of low edge gradient in feature space. Furthermore, our adaptive bandwidth selection technique is validated through three measures like entropy, energy, and blur for more analytical analysis;along with the statistical control limit is applied to generated gradient images for appropriate edges. Finally, our proposed methodology is compared with state of art techniques in terms of the Figure of Merit (FOM) measure, and which confines a higher rate of accuracy in detecting edges. This is a more reliable and precise estimation. © 2021 IEEE.

关键词： image segmentation

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Explainable AI based Lung and Colon Cancer diagnosis using Enhanced Feature analysis

Explainable AI based Lung and Colon Cancer diagnosis using E...

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3rd International conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Kumari, Saumya Jha, Resham Ray, Aarushi Jena, Junali Jasmine Gourisaria, Mahendra Kumar Bandyopadhyay, Anjan School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751027 India

Histopathological and CT scan image analysis plays a critical role in the fast-developing area of medical diagnostics by helping to identify and categorize different types of cancer. Considering lung and colon cancers having high incidence and fatality rates, they are especially important. This work presents a strategy for using histopathological image analysis to identify and categorize these tumors. To extract relevant picture characteristics, advanced image processing techniques are employed across several color spaces, especially using Gabor features, Discrete Fourier Transform (DFT), Local Directional pattern (LDP), Local Binary pattern (LBP), and Discrete Wavelet Transform (DWT). For classification tasks, these characteristics are subjected to many machine learning classifier implementations. A methodical analysis of classifier performance in several color spaces is carried out, demonstrating how color space selection affects the efficacy of feature extraction techniques. Three datasets containing histopathological images and CT scan images have been used, where Random Forest and Gradient Boosting gave 100% accuracy and 89% accuracy in first and second dataset in greyscale images respectively and Random Forest gave 96% accuracy in third datasets in HSV colorspace. The results emphasize the relative advantages of each technique as well as the significance of color space in machine learning model optimization for cancer diagnosis in histopathology pictures. © 2025 The Author(s).

关键词： Feature Selection

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Identification of Banana Disease Using Color and Texture Feature 3rd

Identification of Banana Disease Using Color and Texture Fea...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

作者： Chaudhari, Vandana V. Patil, Manoj P. Smt.G.G. Khadse College Muktainagar India School of Computer Sciences Kaviyatri Bahinabai Chaudhari North Maharashtra University Jalgaon India

ISBN: (纸本)9789811604928

Plant diseases have grown-up in agriculture to be an impasse as it can cause dwindling in both quantity and quality of farming yield. This work explains a simple and adept method used to recognize leaf diseases by applying digital image processing and machine learning technology. In this study 24 color feature, 12 shape and 4 texture features were obtained from images of four kinds of diseases like, Sigatoka, Panama Wilt, Bunchy Top and Banana Streak Virus diseases. Principal component analysis (PCA) was achieved for reducing dimensions in features extracted and k-nearest neighbour classifiers used to identify banana diseases. The finest result was obtained when image identification was conducted based on PCA with K-nearest neighbour classifier. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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analysis of Shape Signature in First and Second Derivatives by Using Wavelet Transformation 3rd

Analysis of Shape Signature in First and Second Derivatives ...

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3rd International conference on Communications and Cyber Physical Engineering, ICCCE 2020

作者： Radhika Mani, M. Jagadesh, B.N. Satyanarayana, Ch. Potukuchi, D.M. Department of CSE Pragati Engineering College SurampalemAP India Department of CSE Srinivasa Institute of Engineering and Technology AmalapuramAP India Department of CSE JNTUK KakinadaAP India Department of Physics JNTUK KakinadaAP India

ISBN: (纸本)9789811579608

The object recognition techniques are popular in computer vision and pattern recognition research field. The present paper focuses on the design of a novel shape signature based on angular information. The Wavelet coefficients are also used to formulate the shape signature. Further, the angular information is captured at two different derivatives of the input image. The angular information is used to estimate the tangential measure for each of the representative point of the input image. The represented shape signature is described with the Fourier transformation. The Fourier descriptors are used for the classification stage. The classification stage uses Euclidean distance measure for the classification. The proposed approach is evaluated on the standard database. The estimated performance measures show the efficiency of the proposed approach. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object recognition

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Performance Evaluation of Capsule and Deep Convolutional Networks in Detecting CycleGAN-Generated images

Performance Evaluation of Capsule and Deep Convolutional Net...

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Advancement in Technology (ICONAT), International conference for

作者： Sakina Banu Salmani Anant V. Nimkar Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Generative Adversarial Networks (GANs) have achieved significant advancements in image manipulation and translation tasks in recent years. CycleGAN, in particular, has demonstrated remarkable performance in unpaired image-to-image translation across various domains such as season transformation (summer to winter), object appearance alteration (apple to orange), and pattern conversion (horse to zebra). This research evaluates the effectiveness of several deep learning models, including DenseNet, InceptionNet v3, XceptionNet, and CapsuleNet, in distinguishing real images from those generated by CycleGAN. Our results indicate that while traditional convolutional networks like DenseNet and InceptionNet v3 exhibit strong performance in standard image classification tasks, Capsule Networks (CapsuleNet) show promising potential in capturing and preserving spatial hierarchies, which is crucial for detecting subtle and complex image manipulations. CapsuleNet’s superior accuracy in the Summer2Winter dataset and competitive performance in the Apple2Orange dataset underscore its capability in handling intricate transformations. However, its lower performance on the Horse2Zebra dataset highlights the need for further optimization. This study underscores the importance of model selection in image manipulation detection and suggests that Capsule Networks offer a viable approach for tasks requiring detailed spatial feature analysis.

关键词： Deep learning image transformation image forensics Adaptation models Visualization Accuracy Generative adversarial networks Complexity theory Convolutional neural networks Optimization

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Can Representation Learning for Multimodal image Registration be Improved by Supervision of Intermediate Layers? 11th

Can Representation Learning for Multimodal Image Registrati...

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Wetzer, Elisabeth Lindblad, Joakim Sladoje, Nataša Centre for Image Analysis Department of Information Technology Uppsala University Uppsala Sweden

ISBN: (纸本)9783031366154

Multimodal imaging and correlative analysis typically require image alignment. Contrastive learning can generate representations of multimodal images, reducing the challenging task of multimodal image registration to a monomodal one. Previously, additional supervision on intermediate layers in contrastive learning has improved biomedical image classification. We evaluate if a similar approach improves representations learned for registration to boost registration performance. We explore three approaches to add contrastive supervision to the latent features of the bottleneck layer in the U-Nets encoding the multimodal images and evaluate three different critic functions. Our results show that representations learned without additional supervision on latent features perform best in the downstream task of registration on two public biomedical datasets. We investigate the performance drop by exploiting recent insights in contrastive learning in classification and self-supervised learning. We visualize the spatial relations of the learned representations by means of multidimensional scaling, and show that additional supervision on the bottleneck layer can lead to partial dimensional collapse of the intermediate embedding space. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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A Survey of Research on image Data Sources Forensics 20

A Survey of Research on Image Data Sources Forensics

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3rd International conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Meng, Xu Meng, Kun Qiao, Wenbao Beijing Informat Sci & Technol Univ Comp Sch Beijing Peoples R China

ISBN: (纸本)9781450375511

The development of technologies such as smart terminals and mobile Internet has made image data one of the most important forms of data in the Internet and personal storage media, and has grown at an alarming rate. As the most effective expression of information, image data can record various information when image content appears, and it can play an unparalleled role in restoring the truth of things. Therefore, the aim of efficiently and accurately identify the source of image is to determine the device that generated the data. It is an effective means of clustering data from the same device, and become a key step in helping to understand the full content. It is one of the core technologies for conducting electronic data forensic evidence. On the basis of summarizing and analyzing the image generation process, this paper analyzes the data shape and acquisition steps of the potential image generation device information, and then obtains the method of image data source identification. It also summarizes the existing related technologies and methods, comparative analysis of their applicability and potential development direction.

关键词： image data traceability Feature extraction Electronic data forensics CFA SPN image statistical features

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image processing and Grid-Based analysis for detection of non-uniform Heat Distribution: A comparative study

Image processing and Grid-Based analysis for detection of no...

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Digital Futures and Transformative Technologies (ICoDT2), International conference on

作者： Abdul Hakeem Khan Ali Javaid School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad Pakistan

This paper presents a detailed comparative analysis of detecting non-uniform heat distribution over a metal disk, which serves as a baffle for a custom-designed heat exchanger, using thermocouples and a thermal camera (FLIR Lepton 2.5, 60x80 pixels). The experimental setup involves placing K-Type thermocouples in a grid pattern over the surface and positioning the thermal camera at a fixed height/distance. The thermal camera has a stated accuracy of ±5 degrees Celsius. Controlled non-uniform heating is applied to the metal disk, and temperature data are recorded using both the thermocouples and the thermal camera. The study aims to assess the accuracy, spatial resolution, and effectiveness of each method for detecting non-uniform heat distribution over metal surfaces. The findings offer valuable insights into the strengths and limitations of thermocouples and thermal cameras in both industrial processes and academic research. In our case, we gained an in-depth understanding of the thermal distribution of the baffle, providing guidance for process optimization, design refinement, and quality control.

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