13 papers · ranked by Valyu relevance
Perfilino Eugênio Ferreira Júnior, Vinícius Moreira Mello, Enzo P. Silva Ribeiro, Gilson Antonio Giraldi + 1 more
Entropy-based image thresholding is one of the most widely used segmentation techniques in image processing. The Tsallis and Masi entropies are information measures that can capture long-range interactions in various physical systems. On the other hand, Shannon entropy is more appropriate for short-range correlations.…
Steven H. Lee, Sean McDowell, Charles Leslie, Kristina McCreery + 4 more
High-throughput phenotyping has become essential for plant breeding programs, replacing traditional methods that rely on subjective scales influenced by human judgment. Machine learning (ML) computer vision systems have successfully used convolutional neural networks (CNNs) for image segmentation, providing greater…
Catherine Aurelia Christie Alexander, Vasileios Magoulianitis, Jiaxin Yang, C.-C. Jay Kuo + 1 more
Nuclei segmentation is a key task in digital histopathology, highlighting important aspects of nuclear morphology and topology in many cancer-related evaluations and studies. Variability in nuclear appearance both within and across different organs, stain heterogeneity, and inconsistencies in acquisition procedures…
Simrandeep Singh, Harbinder Singh, Seyed Jaleleddin Mousavirad, Diego Oliva + 2 more
Image thresholding is one of the fastest and easiest approach for image segmentation and serves as a preprocessing step in computer vision and image processing applications, such as surveillance, image perception, scene understanding, artificial intelligence, augmented reality, biomedical imaging, remote sensing, image…
Asmaa M. Khalid, Shimaa M. Abdel-Moniem, Nabil A. Lashin
Medical image segmentation is one of the most important processes in computer-aided diagnosis. It plays a critical role in detecting and analyzing diseases since it isolates the region of interest in medical scans. Out of many techniques, multilevel thresholding is capable of segmenting complex images. It isolates…
Hongwei Lin, Howard C. Gifford
This study advances task-based image quality assessment by developing an anthropomorphic thresholded visual-search model observer. The model is an ideal observer for thresholded data inspired by the human visual system, allowing selective processing of high-salience features to improve discrimination performance. By…
Usman Rauf, Anfal Zahid, Amina Qadeer, Adeel Zafar + 2 more
Stress has been recognized as a significant global health issue, affecting the majority of the population. Rapid and accurate detection of stress is critical for stress treatment. A considerable part of prior work has focused on classifying electroencephalography signals to enable preliminary detection of stress.…
Fenglin Ding, Yilin Zhao, Zongliang Li, Haibin Tang + 3 more
Anomaly detection and degradation trend prediction are two pivotal tasks in system health management. However, most existing approaches treat them as independent problems and fail to exploit their intrinsic interdependence. In addition, the scarcity of labeled data in real-world scenarios limits the applicability of…
András Telcs, Raúl Alcaraz
We develop a finite-resolution empirical framework for applying nonnegative Mages-Anastasiadi-Rohner partial information decomposition (MAR-PID) to continuous and non-binary discrete variables. The variables are represented by recursive quantile binarization. This provides a balanced binary-tree representation at each…
Yanping Cui, Xiaoxu He, Zhe Wu, Qiang Zhang + 2 more
Highlights What are the main findings?1. A joint denoising framework (ICFO-SVMD-improved wavelet thresholding) is proposed which adaptively optimizes SVMD parameters and sub-band thresholds for nonlinear, non-stationary vibration signals. 2. Simulation and experimental results on bearing and gearbox vibration data show…
Meriem Boumehed, Aykut Fatih Güven, Abderrahmane Naoum, Amir Merahi + 4 more
Accurate detection and segmentation of moving objects constitute a fundamental challenge in computer vision, particularly for intelligent video surveillance systems operating under variable illumination, dynamic backgrounds, and environmental noise. This paper presents a fully unsupervised dual-phase motion analysis…
Hayat Ullah, Sunil Gaire, Corey A. Graves, Xiaojun Yu
This paper presents a computationally efficient Multivariate Mixture Model Thresholding (MMMT) technique for sparse signal denoising and recovery, with the goal of improving data quality in modern sensing and biomedical systems. The proposed method extends classical thresholding approaches by modeling nonzero signal…
Aayush Kumar Tyagi, Vaibhav Mishra, Prathosh A.P., Mausam
Cell segmentation in histopathological images is vital for diagnosis, and treatment of several diseases. Annotating data is tedious, and requires medical expertise, making it difficult to employ supervised learning. Instead, we study a weakly supervised setting, where only bounding box supervision is available, and…