20 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…
Seiichi Uchida
This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the…
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…
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…
Madhav Sigdel, Imren Dinc, Madhu S. Sigdel, Semih Dinc + 2 more
Background Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these…
M. Sumathi, T. Balaji
- Remote sensing image classification can be performed in many different ways to extract meaningful features. One common approach is to perform edge detection. A second approach is to try and detect whole shapes, given the fact that these shapes usually tend to have distinctive properties such as object foreground or…
Mohamed A. El-Sayed, S. Abdel‐Khalek, Eman Abdel-Aziz
a Mathematics department, Faculty of Science, Fayoum University, 63514 Fayoum, Egypt b Mathematics department, Faculty of Science, Sohag University, 82524 Sohag, Egypt c CS department, Faculty of Computers and Information Science , Taif Univesity, 21974 Taif, KSA d Mathematics department, Faculty of Science , Taif…
Priyotosh Sil, Olivier C. Martin, Areejit Samal
Among the various frameworks for modeling gene regulatory network (GRN) dynamics, Boolean modeling remains both powerful and accessible. Nevertheless, selecting update rules so that they capture the combinatorial control of various targets remains challenging due to limited quantitative data. Threshold majority rules…
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…
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…
Amit Gurung, Sangyal Lama Tamang
The segmentation of digital images is one of the essential steps in image processing or a computer vision system. It helps in separating the pixels into different regions according to their intensity level. A large number of segmentation techniques have been proposed, and a few of them use complex computational…
Garret Vo, Chiwoo Park
This paper presents a robust regression approach for image binarization under significant background variations and observation noises. The work is motivated by the need of identifying foreground regions in noisy microscopic image or degraded document images, where significant background variation and severe noise make…
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…
Wei Yang, Lulu Cai, Fei Wu, Lixiang Li
Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image’s brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a…
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…
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…
Zhiwei Ye, Juan Yang, Mingwei Wang, Xinlu Zong + 2 more
'Wei Liu'] Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information…
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…
Michael Doube
Sequential region labelling, also known as connected components labelling, is a standard image segmentation problem that joins contiguous foreground pixels into blobs. Despite its long development history and widespread use across diverse domains such as bone biology, materials science, and geology, connected…