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proc 2014 inter conf contemporary comp informatics (ic3i)

作者： Mageswari, S. Umaa Mala, C. Natl Inst Technol Comp Sci & Engn Dept Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781479966295

image segmentation is a primary stage in image processing for identifying objects of interest. segmentation methods are classified into region based, transform based, edge based and clustering based segmentation. In this paper, segmentation methods including histogram, watershed, Canny edge detector and K-means clustering techniques are studied and analyzed. The experimental results obtained are compared with different evaluation measures including three standard image segmentation indices: rand index, globally consistency error and variation of information.

关键词： image classification image segmentation transforms Canny edge detector clustering based segmentation edge based segmentation histogram image segmentation methods k-means clustering technique region based segmentation transform based segmentation watershed Detectors Histograms image color analysis image edge detection image reconstruction image segmentation Mathematical model

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Adaptive image segmentation by using mean-shift and evolutionary optimisation

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IET image PROCESSING 2014年第6期8卷 327-333页

作者： Liu, Cong Zhou, Aimin Zhang, Qian Zhang, Guixu E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China

Undersegmentation or oversegmentation is a challenge faced in image segmentation methods, and it is extreme important to determine the optimal number of regions (clusters) of an image in real-world applications. In this study, we introduce an adaptive strategy to do so. The basic idea is to firstly oversegment an image by using the Mean-shift (MS) method, and then segment the obtained oversegmented results by using an evolutionary algorithm. In the second stage, a feature is extracted for each region obtained by the MS method, and a new fitness function is designed to determine the optimal number of clusters. The adaptive approach is applied to a variety of images, and the experimental results show that our method is both efficient and effective for image segmentation.

关键词： adaptive signal processing evolutionary computation image segmentation adaptive image segmentation mean-shift optimisation evolutionary optimisation undersegmentation oversegmentation image segmentation methods real-world applications adaptive strategy

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Signal-walking-driven active contour model

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IET image PROCESSING 2015年第12期9卷 1101-1106页

作者： Pi, Ming Hong Ma, Jun Zhang, Wei Zhou, Zhigang Wuhan Text Univ Sch Comp Sci Wuhan 430073 Hubei Peoples R China JiuJiang Univ Sch Mech & Mat Engn Jiujiang Jiangxi Peoples R China

Active contour models are effective image segmentation methods. However, they are very time-consuming, and their convergence depends upon the choice of initial contour. To overcome the two drawbacks, in the study, the authors suggest a signal-walking-driven active contour model. By walking a signal, they construct a forest of object evolution. Each tree grows from a root object, and child node contains its shrunk or/and split version. The merit value of an object is a composite metric from the colour, edge, or/and shape properties. The merit function plays an important role in tree construction and the goodness of object evolution. The objects are selected and added to the tree in the levels the merit function reaches the local maxima. After the forest of object evolution is constructed, by traversing each tree branch in post-order, the objects corresponding to maximum merit values are extracted as the final segmentation. Experimental results on a set of oil-sand images indicate the proposed signal-walking-driven active contour model outperforms Chan and Vese's model and adaptive thresholding.

关键词： image segmentation trees (mathematics) edge detection signal-walking-driven active contour model image segmentation methods colour property edge property shape property tree construction object evolution adaptive thresholding merit function oil-sand images Chan model Vese model

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Q-Seg: Quantum Annealing-Based Unsupervised image segmentation

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2024年第5期44卷 27-39页

作者： Venkatesh, Supreeth Mysore Macaluso, Antonio Nuske, Marlon Klusch, Matthias Dengel, Andreas German Res Ctr Artificial Intelligence D-66123 Saarbrucken Germany

We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixelwise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-wave advantage device, offering superior scalability over existing quantum approaches and outperforming several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime.

关键词： image segmentation Quantum Computing Annealing Quantum Annealing image Edge Detection Optimization Measurement image segmentation Unsupervised image segmentation Computer Vision Unsupervised methods segmentation Method Solution Space Edge Detection Earth Observation Quantum Computing Optimization Task segmentation Problem segmentation Quality image segmentation methods Flood Mapping Multi Object Tracking Special Case Training Data Intensity Values Similarity Measure Edge Weights Minimum Cut Noisy Labels Grid Graph Gradient Boosting Decision Tree Real Devices Synthetic Aperture Radar Binary segmentation Specific Use Case segmentation Task

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Entropy-optimal image segmentation model based on an improved arithmetic optimization algorithm

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JOURNAL OF OPTICS-INDIA 2024年第4期53卷 3108-3118页

作者： Xu, Zhi Zhu, Ping Hebei North Univ Coll Informat Sci & Engn Zhangjiakou 075000 Hebei Peoples R China Hebei North Univ Dept Sci Dept Sci Res Zhangjiakou 075000 Hebei Peoples R China

Entropy-optimal image segmentation model is a technology used for image segmentation, aiming at obtaining the optimal segmentation result by optimizing the algorithm. It is difficult to segment the entropy-optimal image target that users are interested in effectively because of the uneven illumination distribution and the occlusion of the target. Therefore, an entropy-optimal image segmentation model based on improved arithmetic optimization algorithm is proposed. The entropy-optimal image is preprocessed by local visual saliency, and the visual saliency region is extracted. The information of the entropy-optimal image is comprehensively processed by conditional random field, and an entropy-optimal image segmentation method is designed. According to the flow chart and structure diagram of the improved arithmetic optimization algorithm, the entropy-optimal image segmentation model is constructed. The experimental results show that the average segmentation accuracy of the model is 98.54%, which has good segmentation accuracy. The signal-to-noise ratio is always above 95 dB, which can effectively segment the original image, and the edge of the segmented image is clear, the noise points are effectively removed, and the definition is high.

关键词： Improved arithmetic optimization algorithm Entropy-optimal image segmentation model Visual saliency area Conditional random field Noise points image segmentation methods

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Enhanced image Edge Detection methods for Crab Species Identification

Enhanced Image Edge Detection Methods for Crab Species Ident...

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IEEE International Conference on Soft-Computing and Network Security (ICSNS)

作者： Prathusha, P. Jyothi, S. Mamatha, D. M. Sri Padmavathi Mahila Viswavidyalayam Tirupati Andhra Pradesh India

ISBN: (纸本)9781538645529

Automatic image Analysis, image Classification, Automatic Object Recognition are some of the aspiring research areas in various fields of Engineering. Many Industrial and biological applications demand image Analysis and image Classification. Sample images available for classification may be complex, image data may be inadequate or component regions in the image may have poor visibility. With the available information each Digital image Processing application has to analyze, classify and recognize the objects appropriately. Pre-processing, image segmentation, feature extraction and classification are the most common steps to follow for Classification of images. In this study we applied various existing edge detection methods like Robert, Sobel, Prewitt, Canny, Otsu and Laplacian of Guassian to crab images. From the conducted analysis of all edge detection operators, it is observed that Sobel, Prewitt, Robert operators are ideal for enhancement. The paper proposes Enhanced Sobel operator, Enhanced Prewitt operator and Enhanced Robert operator using morphological operations and masking. The novelty of the proposed approach is that it gives thick edges to the crab images and removes spurious edges with help of m-connectivity. Parameters which measure the accuracy of the results are employed to compare the existing edge detection operators with proposed edge detection operators. This approach shows better results than existing edge detection operators.

关键词： image segmentation methods Robert Operator Prewitt Operator Sobel operators Otsu segmentation Canny Edge Detection Edge Detection operators. Laplacian of Gaussian

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In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications

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CHEMICAL ENGINEERING JOURNAL 2022年第0期438卷 135554-135554页

作者： Liu, Jian Kuang, Wenjie Liu, Jiaqiang Gao, Zhenguo Rohani, Sohrab Gong, Junbo Tianjin Univ Coinnovat Ctr Chem & Chem Engn Tianjin Sch Chem Engn & Technol State Key Lab Chem Engn Tianjin 300072 Peoples R China Univ Western Ontario Dept Chem & Biochem Engn London ON N6A 5B9 Canada Tianjin Univ Inst Shaoxing Zhejiang 312300 Peoples R China

Real-time chemical process monitoring, analysis, and control have become increasingly important to multi-phase flow process research and development and attracted overt attention during the recent decades. In-situ imagebased process analytical technology which benefited from the fast development in imaging hardware and AIbased algorithms has achieved great progress in the area of multi-phase flow processes. This review work summarizes the advances of the imaging hardware for real-time image capturing and the algorithms for image processing. Insights based on the advanced examples in multi-phase flows (industrial crystallization, dissolution, fluidization, emulsification) are provided to inspire the applications and development of real-time process imaging analysis. It concludes that the recently developed imaging hardware can meet the demands (image field, resolution, magnification) in different scenarios, and the AI-based algorithms have superior abilities (accuracy, efficiency, migrating application capability) in image segmentation and classification. The usage of image-based in-situ process analytical technology provides intuitional tracking and data-driven process monitoring, analysis, and control of multi-phase flow processes. Finally, challenges and opportunities for the development of in-situ image-based process analytical technology in multi-phase flows are discussed.

关键词： Multi-phase flow Imaging hardware Process analytical technology (PAT) image segmentation methods Deep learning (DL)

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Artificial intelligence and classic methods to segment and characterize spherical objects in micrographs of industrial emulsions

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2024年 649卷 123633页

作者： Khosravi, Hanieh Thaker, Abhijeet H. Donovan, John Ranade, Vivek Unnikrishnan, Saritha Atlantic Technol Univ ATU Fac Engn & Design Sligo F91YW50 Ireland Atlantic Technol Univ ATU Ctr Precis Engn Mat & Mfg Res PEM Sligo Ireland Univ Limerick Fac Sci & Engn Dept Chem Sci Limerick Ireland

The stability of emulsions is a critical concern across multiple industries, including food products, agricultural formulations, petroleum, and pharmaceuticals. Achieving prolonged emulsion stability is challenging and depends on various factors, with particular emphasis on droplet size, shape, and spatial distribution. Addressing this issue necessitates an effective investigation of these parameters and finding solutions to enhance emulsion stability. image analysis offers a powerful tool for researchers to explore these characteristics and advance our understanding of emulsion instability in different industries. In this review, we highlight the potential of state-of-the-art deep learning-based approaches in computer vision and image analysis to extract relevant features from emulsion micrographs. A comprehensive summary of classic and cutting-edge techniques employed for characterizing spherical objects, including droplets and bubbles observed in micrographs of industrial emulsions, has been provided. This review reveals significant deficiencies in the existing literature regarding the investigation of highly concentrated emulsions. Despite the practical importance of these systems, limited research has been conducted to understand their unique characteristics and stability challenges. It has also been identified that there is a scarcity of publications in multimodal analysis and a lack of a complete automated in-line emulsion characterization system. This review critically evaluates the existing challenges and presents prospective directions for future advancements in the field, aiming to address the current gaps and contribute to the scientific progression in this area.

关键词： Industrial emulsions Micrographs image analysis methods image segmentation methods Deep learning

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Semi-automated object tracking methods in biological imaging

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Current Protocols in Cytometry 2015年第1期2015卷 12.38.1-12.38.21页

作者： Davis, Michael A. Pražský, Ondřej Sysko, Laura R. Nikon Instruments Melville NY United States Laboratory Imaging Prague Czech Republic

Time-lapse imaging is a rich data source offering potential kinetic information of cellular activity and behavior. Tracking and extracting measurements of objects from time-lapse datasets are challenges that result from the complexity and dynamics of each object's motion and intensity or the appearance of new objects in the field of view. A wide range of strategies for proper data sampling, object detection, image analysis, and post-analysis interpretation are available. Theory and methods for single-particle tracking, spot detection, and object linking are discussed in this unit, as well as examples with step-by-step procedures for utilizing semi-automated software and visualization tools for achieving tracking results and interpreting this output. © 2015 by John Wiley & Sons, Inc.

关键词： Digital imaging image analysis image segmentation methods Object linking Object tracking Sampling frequency Single-particle tracking

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