imagesegmentation is a primary stage in image processing for identifying objects of interest. segmentationmethods are classified into region based, transform based, edge based and clustering based segmentation. In t...
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ISBN:
(纸本)9781479966295
imagesegmentation is a primary stage in image processing for identifying objects of interest. segmentationmethods are classified into region based, transform based, edge based and clustering based segmentation. In this paper, segmentationmethods 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 imagesegmentation indices: rand index, globally consistency error and variation of information.
We present Q-Seg, a novel unsupervised imagesegmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixelwise segmentation problem, which assimilates spectral and spat...
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We present Q-Seg, a novel unsupervised imagesegmentation 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 imagesegmentation, 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.
Entropy-optimal imagesegmentation model is a technology used for imagesegmentation, aiming at obtaining the optimal segmentation result by optimizing the algorithm. It is difficult to segment the entropy-optimal ima...
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Entropy-optimal imagesegmentation model is a technology used for imagesegmentation, 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 imagesegmentation 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 imagesegmentation method is designed. According to the flow chart and structure diagram of the improved arithmetic optimization algorithm, the entropy-optimal imagesegmentation 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.
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 an...
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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.
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 ima...
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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 imagesegmentation 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.
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 ...
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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, imagesegmentation, 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.
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...
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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.
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 th...
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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 imagesegmentation.
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 fr...
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