A survey on matching strategies for boundary image comparison and evaluation

López-Molina, Carlos; Marco-Detchart, Cédric; Bustince, Humberto; Baets, Bernard De

doi:10.1016/j.patcog.2021.107883

Cited by 6 publications

(4 citation statements)

References 74 publications

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“…In the current dataset, as well as in most image processing tasks, most of the images contained no valid information, and so TN tended to be significantly larger than any other quantity in the confusion matrices. In order to avoid the distortion of the results by TN, we opted for Prec, Rec and F [54,58].…”

Section: Quantitative Comparison Measuresmentioning

confidence: 99%

Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

et al. 2021

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Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients.

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Section: Quantitative Comparison Measuresmentioning

confidence: 99%

Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

et al. 2021

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“…Gabor features have been successfully applied to biometrics due to their robustness to local distortions caused by differences in illumination, expression, and pose. The biometric field mainly includes face recognition [1]- [3], iris recognition [4], palmprint verification [5]- [7], and human gait recognition [8], [9]. Furthermore, Gabor filters are also commonly used in general image structure information catching, such as corner detection [10]- [13], texture analysis [14], [15], and image matching [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Corner Detection Based on Multi-directional Gabor Filters with Multi-scales

Wang¹,

Ji²,

Li³

et al. 2023

Preprint

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Gabor wavelet is an essential tool for image analysis and computer vision tasks. Local structure tensors with multiple scales are widely used in local feature extraction. Our research indicates that the current corner detection method based on Gabor wavelets can not effectively apply to complex scenes. In this work, the capability of the Gabor function to discriminate the intensity changes of step edges, L-shaped corners, Y-shaped or T-shaped corners, X-shaped corners, and star-shaped corners are investigated. The properties of Gabor wavelets to suppress affine image transformation are investigated and obtained. Many properties for edges and corners were discovered, which prompted us to propose a new corner extraction method. To fully use the structural information from the tuned Gabor filters, a novel multi-directional structure tensor is constructed for corner detection, and a multi-scale corner measurement function is proposed to remove false candidate corners. Furthermore, we compare the proposed method with twelve current state-of-theart methods, which exhibit optimal performance and practical application to 3D reconstruction with good application potential.

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“…Due to the advantages of small data volume, fast transmission speed, and ease of loading and playback in mobile network environments, micro animated videos have been widely used [5]. However, due to the large number of images in micro animated videos and the high similarity between images, efficient image matching is needed in the field of more detailed image processing [6] to ensure the quality and accuracy of micro animated videos in mobile network propagation. At present, many scholars have conducted research on image matching algorithms, such as Mousavi V et al proposing an image key point matching method based on information content selection [7], which uses entropy, spatial saliency, and texture coefficients to measure image quality.…”

Section: Introductionmentioning

confidence: 99%

High-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environment

Su,

Djenouri

2023

Preprint

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In the mobile network environment, the accuracy of related image matching algorithms is affected by factors such as bandwidth uncertainty and channel interference, resulting in significant limitations in image feature matching. This article designs a high-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environments. Fully denoise micro animation video images using 2D High Density Discrete Wavelet Transform (HD-DWT), and apply fixed block count segmentation to process micro animation video images; Using Harris algorithm to complete image corner detection and obtain corner features of sub images; In the K-means clustering algorithm, SIFT feature vectors are divided into clusters and paired with the nearest neighbor cluster in another sub image to form a sub image matching pair, completing block based sub image matching; Combine all sub image matching results to obtain video image matching results, and use the Improved Random Sampling Consistency (RANCAS) algorithm to remove incorrect matching during the matching process, improving matching accuracy. The experimental results show that the designed algorithm can effectively reduce image noise, improve image quality, and generate a large number of matching pairs in mobile network environments. After the application of the designed algorithm, the production effect of micro animated videos in mobile networks can be significantly improved.

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A survey on matching strategies for boundary image comparison and evaluation

Cited by 6 publications

References 74 publications

Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

Corner Detection Based on Multi-directional Gabor Filters with Multi-scales

High-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environment

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