18 papers · ranked by Valyu relevance
Hanae Moussaoui, Nabil El Akkad, Mohamed Benslimane, Walid El-Shafai + 3 more
'Abdullah Baihan' 'Chaminda Hewage' 'Rajkumar Singh Rathore'] Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and…
Luca Frigau, Claudio Conversano, Jaromír Antoch
Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called…
Soumen Sinha, Shahryar Rahnamayan, Azam Asilian Bidgoli
Efficient text embedding is crucial for large-scale natural language processing (NLP) applications, where storage and computational efficiency are key concerns. In this paper, we explore how using binary representations (barcodes) instead of real-valued features can be used for NLP embeddings derived from machine…
Jens Krog, Albertas Dvirnas, Oskar E. Ström, Jason P. Beech + 5 more
'Jonas O. Tegenfeldt' 'Vilhelm Müller' 'Fredrik Westerlund' 'Tobias Ambjörnsson' 'Kun Chen'] We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base…
Christoph Dalitz
Thresholding converts a greyscale image into a binary image, and is thus often a necessary segmentation step in image processing. For a human viewer however, thresholding usually has a negative impact on the legibility of document images. This report describes a simple method for "smearing out" the threshold and…
Nicholas Theis, Jonathan Rubin, Joshua Cape, Satish Iyengar + 1 more
Structural and functional brain connectomes built using macroscale data collected through magnetic resonance imaging (MRI) may contain noise that contributes to false-positive edges, which can obscure structure-function relationships with implications for data interpretation. Thresholding procedures are routinely…
Eslam Hegazy, Mohamed Gabr
Multilevel image thresholding is widely used for segmentation in applications ranging from medical imaging to remote sensing. Classical objective functions, such as Otsu's between-class variance and Kapur's entropy, are often optimized using metaheuristic algorithms, with performance evaluated via metrics like…
Eslam Hegazy, Mohamed Gabr
Multilevel Image thresholding is an important preprocessing algorithm in computer vision applications nowadays. Since most common thresholding methods take the desired count of thresholds as input by the user, thresholding methods that automatically determines a suitable count of thresholds from the input image itself…
Lars C. Reining, Thomas S. A. Wallis, Manuel Jimenez
Mooney images can contribute to our understanding of the processes involved in visual perception, because they allow a dissociation between image content and image understanding. Mooney images are generated by first smoothing and subsequently thresholding an image. In most previous studies this was performed manually…
Shamna Pootheri, Daniel Ellam, Thomas Grübl, Yang Liu + 1 more
Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels.…
Shubham Mahajan, Nitin Mittal, Rohit Salgotra, Mehedi Masud + 2 more
'Hesham A. Alhumyani' 'Amit Kant Pandit'] Salp swarm algorithm (SSA) is an innovative contribution to smart swarm algorithms and has shown its utility in a wide range of research domains. While it is an efficient algorithm, it is noted that SSA suffers from several issues, including weak exploitation, convergence, and…
Ylva Grønningsæter, Halvor S. Smørvik, Ole‐Christoffer Granmo
Composites Authors: ['Ylva Grønningsæter' 'Halvor S. Smørvik' 'Ole‐Christoffer Granmo'] Abstract—The Tsetlin Machine (TM) [1] has achieved competitive results on several image classification benchmarks, including MNIST, K-MNIST, F-MNIST, and CIFAR-2 [2], [3]. However, color image classification is arguably still in its…
Fusong Xiong, Zhiqiang Zhang, Yun Ling, Jian Zhang
Image segmentation by thresholding is an important and fundamental task in image processing and computer vision. In this paper, a new bi-level thresholding approach based on weighted Parzen-window and linear programming techniques is proposed to use in image thresholding segmentation. First, by proposing a weighted…
Lars C. Reining, Thomas S. A. Wallis
Mooney images can contribute to our understanding of the processes involved in visual perception, because they allow a dissociation between image content and image understanding. Mooney images are generated by first smoothing and subsequently thresholding an image. In most previous studies this was performed manually…
Zheng Zhang, Timothy G. Constandinou
This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used…
Mauro Silberberg, Hernán E. Grecco
Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…
Csaba Dávid, Kristóf Giber, Katalin Kerti-Szigeti, Mihaly Kollo + 2 more
Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background and noise in any type of…