<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Signal Processing | Image Processing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/signal-processing/sections/image-processing</link>
        <description>RSS Feed for Image Processing section in the Frontiers in Signal Processing journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-04-05T01:30:33.53+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1727948</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1727948</link>
        <title><![CDATA[Implementation of selected ISO/IEC 29794-5 measures and proposing alternatives]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Paulina Otlik</author><author>Wojciech Wodo</author>
        <description><![CDATA[Face recognition is currently one of the most popular forms of biometric verification. As the effectiveness and security of this solution increase, so does its use in specialized fields. Considering that the verification process involves thousands of people, often under varying lighting conditions and with equipment of different parameters, biometric samples are of mixed quality. Therefore, there is a need to define the conditions under which a biometric sample is objectively good for a face recognition system. To address this, the international standard ISO/IEC 29794-5:2025 was developed, with defined quality measures, along with a description of suggested implementation where the majority of substantive work has already been completed. The aim of this work is to provide non-proprietary implementation of the ISO/IEC 29794-5:2023 standard for face image quality assessment and to compare its performance against OFIQ reference implementation. More broadly, this study examines the common challenge that biometric standards sometimes propose ideas that may not be top-effective in real-life operational scenarios. This paper includes the implementation of two systems for assessing face image quality based on selected standard’s measures. The first system follows the implementation suggested directly by the standard, while the second utilizes the latest scientific and commercial solutions. Ultimately, these systems are compared using a database of photographs differentiated by demographics and quality.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1761293</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1761293</link>
        <title><![CDATA[A graph generation pipeline for critical infrastructures based on heuristics, images and depth data]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mike Diessner</author><author>Yannick E. Tarant</author>
        <description><![CDATA[Virtual representations of physical critical infrastructures, such as water or energy plants, are used for simulations and digital twins to ensure resilience and continuity of their services. These models usually require 3D point clouds from laser scanners that are expensive to acquire and require specialist knowledge to use. In this article, we present a prototypical graph generation pipeline based on photogrammetry. The pipeline detects relevant objects and predicts their relation using RGB images and depth data generated by a stereo camera. This more cost-effective approach uses deep learning for object detection and instance segmentation of the objects, and employs user-defined heuristics or rules to infer their relations. Results of two hydraulic systems show that this strategy can produce graphs close to the ground truth. While this study focuses on hydraulic systems, the general process can be used to tailor the method to other types of infrastructures and applications. The user-defined rules create transparency qualifying the pipeline to be used in the high stakes decision-making that is required for critical infrastructures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1527975</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1527975</link>
        <title><![CDATA[MSWAFFNet: improved segmentation of nucleus using feature fusion of multi scale wavelet attention]]></title>
        <pubdate>2025-11-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jun Zhang</author><author>Yangsheng Hu</author><author>Zhenzhou An</author>
        <description><![CDATA[IntroductionNucleus segmentation plays an essential role in digital pathology,particularly in cancer diagnosis and the evaluation of treatment efficacy. Accurate nucleus segmentation provides critical guidance for pathologists. However, due to the wide variability instructure, color, and morphology of nuclei in histopathological images, automated segmentation remains highly challenging. Previous neural networks employing wavelet-guided, boundary-aware attention mechanisms have demonstrated certain advantages in delineating nuclear boundaries. However, their feature fusion strategies have been suboptimal, limiting overall segmentation accuracy.MethodsIn this study, we propose a novel architecture—the Multi-Scale Wavelet Fusion Attention Network (MSWAFFNet)—which incorporates an Attention Feature Fusion (AFF) mechanism to effectively integrate high-frequency features extracted via 2D Discrete Wavelet Transform (DWT) from different Unet scales. This approach enhances boundary perception and improves segmentation performance. To address the variation across datasets, we apply a series of preprocessing steps to normalize the color distribution and statistical characteristics, thereby ensuring training consistency.Results and DiscussionThe proposed method is evaluated on three public histopathology datasets (DSB, TNBC, CoNIC), achieving Dice coefficients of 91.33%, 80.56%, and 91.03%, respectively—demonstrating superior segmentation performance across diverse scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1672569</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1672569</link>
        <title><![CDATA[ASG-MammoNet: an attention-guided framework for streamlined and interpretable breast cancer classification from mammograms]]></title>
        <pubdate>2025-11-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hosameldin O. A. Ahmed</author><author>Asoke K. Nandi</author>
        <description><![CDATA[IntroductionBreast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related death among women globally, emphasising the urgent need for early, accurate, and interpretable diagnostic tools. While digital mammography serves as the cornerstone of breast cancer screening, its diagnostic performance is often hindered by image quality variability, dense breast tissue, and limited visual interpretability. Furthermore, conventional Computer-Aided Diagnostic (CAD) systems and deep learning models have struggled with clinical adoption due to high false-positive rates, difficult decision-making, and excessive computational demands.MethodsTo address these critical challenges, we introduce ASG-MammoNet, an Attention-Guided and Streamlined deep learning framework for robust, real-time, and explainable mammographic breast cancer classification. The framework is composed of three integrated stages: (1) Data Preparation and Balanced Feature Representation, which applies advanced preprocessing, augmentation, and weighted sampling to mitigate data imbalance and variations across the dataset; (2) Attention-Guided Streamlined Classification, where an EfficientNet-B0 backbone is enhanced by a dual-stage Convolutional Block Attention Module (CBAM) to selectively emphasise diagnostically relevant features; and (3) Explainable Inference, in which Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to provide class-specific visualisations of lesion regions, supporting interpretability and clinical decision-making.ResultsASG-MammoNet is thoroughly validated on three benchmark mammography datasets, CBIS-DDSM, INbreast, and MIAS, achieving accuracy above 99.1%, AUC scores exceeding 99.6%, and DIP (Distance from Ideal Position) scores above 0.99, with an average inference time under 14 milliseconds per image. The framework exhibits strong generalisability, consistent performance across data folds, and clinically relevant attention maps, highlighting its readiness for real-world deployment.DiscussionThe model consistently outperforms or matches recent state-of-the-art approaches while offering superior balance across sensitivity and specificity. Its robust generalisability, consistent fold-wise performance, and clinically meaningful attention visualisations support its practical utility. By addressing critical limitations such as high computational cost, limited interpretability, and precision, ASG-MammoNet represents a practical and reliable solution for AI-assisted breast cancer diagnosis in modern screening settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1585242</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1585242</link>
        <title><![CDATA[Event-based solutions for human-centered applications: a comprehensive review]]></title>
        <pubdate>2025-08-29T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Mira Adra</author><author>Simone Melcarne</author><author>Nelida Mirabet-Herranz</author><author>Jean-Luc Dugelay</author>
        <description><![CDATA[Event cameras, often referred to as dynamic vision sensors, are groundbreaking sensors capable of capturing changes in light intensity asynchronously, offering exceptional temporal resolution and energy efficiency. These attributes make them particularly suited for human-centered applications, as they capture both the most intricate details of facial expressions and the complex motion dynamics of the human body. Despite growing interest, research in human-centered applications of event cameras remains scattered, with no comprehensive overview encompassing both body and face tasks. This survey bridges that gap by being the first to unify these domains, presenting an extensive review of advancements, challenges, and opportunities. We also examine less-explored areas, including event compression techniques and simulation frameworks, which are essential for the broader adoption of event cameras. This survey is designed to serve as a foundational reference that helps both new and experienced researchers understand the current state of the field and identify promising directions for future work in human-centered event camera applications. A summary of this survey can be found at https://github.com/nmirabeth/event_human.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1628390</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1628390</link>
        <title><![CDATA[Editorial: Explainable, trustworthy, and responsible AI in image processing]]></title>
        <pubdate>2025-05-30T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Akshay Agarwal</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1581192</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1581192</link>
        <title><![CDATA[Editorial: Volumetric video processing]]></title>
        <pubdate>2025-04-03T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Emin Zerman</author><author>Pan Gao</author><author>Giuseppe Valenzise</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1405808</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1405808</link>
        <title><![CDATA[Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability]]></title>
        <pubdate>2025-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christopher Remde</author><author>Igor M. Sauer</author><author>Moritz Queisner</author>
        <description><![CDATA[IntroductionVolumetric video production in commercial studios is predominantly produced using a multi-view stereo process that relies on a high two-digit number of cameras to capture a scene. Due to the hardware requirements and associated processing costs, this workflow is resource-intensive and expensive, making it unattainable for creators and researchers with smaller budgets. Low-cost volumetric video systems using RGBD cameras offer an affordable alternative. As these small, mobile systems are a relatively new technology, the available software applications vary in terms of workflow and image quality. In this paper we provide an overview of the technical capabilities of sparse camera volumetric video capture applications and assess their visual fidelity and workflow.Materials and methodsWe selected volumetric video applications that are publicly available, support capture with multiple Microsoft Azure Kinect cameras and run on consumer-grade computer hardware. We compared the features, usability, and workflow of each application and benchmarked them in five different scenarios. Based on the benchmark footage, we analyzed spatial calibration accuracy, artifact occurrence and conducted a subjective perception study with 19 participants from a game design study program to assess the visual fidelity of the captures.ResultsWe evaluated three applications, Depthkit Studio, LiveScan3D and VolumetricCapture. We found Depthkit Studio to provide the best experience for novel users, while LiveScan3D and VolumetricCapture require advanced technical knowledge to be operated. The footage captured by Depthkit Studio showed the least amount of artifacts by a larger margin, followed by LiveScan3D and VolumetricCapture. These findings were confirmed by the participants who preferred Depthkit Studio over LiveScan3D and VolumetricCapture.DiscussionBased on the results, we recommend Depthkit Studio for the highest fidelity captures. LiveScan3D produces footage of only acceptable fidelity but is the only candidate that is available as open-source software. We therefore recommend it as a platform for research and experimentation. Due to the lower fidelity and high setup complexity, we recommend VolumetricCapture only for specific use-cases where its ability to handle a high number of sensors in a large capture volume is required.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1447841</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1447841</link>
        <title><![CDATA[Approximation properties relative to continuous scale space for hybrid discretisations of Gaussian derivative operators]]></title>
        <pubdate>2025-01-29T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Tony Lindeberg</author>
        <description><![CDATA[This paper presents an analysis of properties of two hybrid discretisation methods for Gaussian derivatives, based on convolutions with either the normalised sampled Gaussian kernel or the integrated Gaussian kernel followed by central differences. The motivation for studying these discretisation methods is that in situations when multiple spatial derivatives of different orders are needed at the same scale level, they can be computed significantly more efficiently, compared to more direct derivative approximations based on explicit convolutions with either sampled Gaussian derivative kernels or integrated Gaussian derivative kernels. We characterise the properties of these hybrid discretisation methods in terms of quantitative performance measures, concerning the amount of spatial smoothing that they imply, as well as the relative consistency of the scale estimates obtained from scale-invariant feature detectors with automatic scale selection, with an emphasis on the behaviour for very small values of the scale parameter, which may differ significantly from corresponding results obtained from the fully continuous scale-space theory, as well as between different types of discretisation methods. The presented results are intended as a guide, when designing as well as interpreting the experimental results of scale-space algorithms that operate at very fine scale levels.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1433388</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1433388</link>
        <title><![CDATA[An integrated framework for multi-granular explanation of video summarization]]></title>
        <pubdate>2024-12-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Konstantinos Tsigos</author><author>Evlampios Apostolidis</author><author>Vasileios Mezaris</author>
        <description><![CDATA[In this paper, we propose an integrated framework for multi-granular explanation of video summarization. This framework integrates methods for producing explanations both at the fragment level (indicating which video fragments influenced the most the decisions of the summarizer) and the more fine-grained visual object level (highlighting which visual objects were the most influential for the summarizer). To build this framework, we extend our previous work on this field, by investigating the use of a model-agnostic, perturbation-based approach for fragment-level explanation of the video summarization results, and introducing a new method that combines the results of video panoptic segmentation with an adaptation of a perturbation-based explanation approach to produce object-level explanations. The performance of the developed framework is evaluated using a state-of-the-art summarization method and two datasets for benchmarking video summarization. The findings of the conducted quantitative and qualitative evaluations demonstrate the ability of our framework to spot the most and least influential fragments and visual objects of the video for the summarizer, and to provide a comprehensive set of visual-based explanations about the output of the summarization process.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1523312</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1523312</link>
        <title><![CDATA[Editorial: Advances in biomedical image segmentation and analysis using deep learning]]></title>
        <pubdate>2024-11-22T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Michalis A. Savelonas</author><author>Eleonora Maggioni</author><author>Stavros A. Karkanis</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1420060</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1420060</link>
        <title><![CDATA[New challenges in point cloud visual quality assessment: a systematic review]]></title>
        <pubdate>2024-11-01T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Amar Tious</author><author>Toinon Vigier</author><author>Vincent Ricordel</author>
        <description><![CDATA[The compression, transmission and rendering of point clouds is essential for many use cases, notably immersive experience settings in eXtended Reality, telepresence and real-time communication where real world acquired 3D content is displayed in a virtual or real scene. Processing and display for these applications induce visual artifacts and the viewing conditions can impact the visual perception and Quality of Experience of users. Therefore, point cloud codecs, rendering methods, display settings and more need to be evaluated through visual Point Cloud Quality Assessment (PCQA) studies, both subjective and objective. However, the standardization of recommendations and methods to run such studies did not follow the evolution of the research field and new issues and challenges have emerged. In this paper, we make a systematic review of subjective and objective PCQA studies. We collected scientific papers from online libraries (IEEE Xplore, ACM DL, Scopus) and selected a set of relevant papers to analyze. From our observations, we discuss the progress and future challenges in PCQA toward efficient point cloud video coding and rendering for eXtended Reality. Main axes for development include the study of use case specific influential factors and the definition of new test conditions for subjective PCQA, and development of perceptual learning-based methods for objective PCQA metrics as well as more versatile evaluation of their performance and time complexity.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1417363</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1417363</link>
        <title><![CDATA[Towards robust visual odometry by motion blur recovery]]></title>
        <pubdate>2024-10-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Simin Luan</author><author>Cong Yang</author><author>Xue Qin</author><author>Dongfeng Chen</author><author>Wei Sui</author>
        <description><![CDATA[IntroductionMotion blur, primarily caused by rapid camera movements, significantly challenges the robustness of feature point tracking in visual odometry (VO).MethodsThis paper introduces a robust and efficient approach for motion blur detection and recovery in blur-prone environments (e.g., with rapid movements and uneven terrains). Notably, the Inertial Measurement Unit (IMU) is utilized for motion blur detection, followed by a blur selection and restoration strategy within the motion frame sequence. It marks a substantial improvement over traditional visual methods (typically slow and less effective, falling short in meeting VO’s realtime performance demands). To address the scarcity of datasets catering to the image blurring challenge in VO, we also present the BlurVO dataset. This publicly available dataset is richly annotated and encompasses diverse blurred scenes, providing an ideal environment for motion blur evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1385287</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1385287</link>
        <title><![CDATA[Optimized quantization parameter selection for video-based point cloud compression]]></title>
        <pubdate>2024-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hui Yuan</author><author>Raouf Hamzaoui</author><author>Ferrante Neri</author><author>Shengxiang Yang</author><author>Xin Lu</author><author>Linwei Zhu</author><author>Yun Zhang</author>
        <description><![CDATA[Point clouds are sets of points used to visualize three-dimensional (3D) objects. Point clouds can be static or dynamic. Each point is characterized by its 3D geometry coordinates and attributes such as color. High-quality visualizations often require millions of points, resulting in large storage and transmission costs, especially for dynamic point clouds. To address this problem, the moving picture experts group has recently developed a compression standard for dynamic point clouds called video-based point cloud compression (V-PCC). The standard generates two-dimensional videos from the geometry and color information of the point cloud sequence. Each video is then compressed with a video coder, which converts each frame into frequency coefficients and quantizes them using a quantization parameter (QP). Traditionally, the QPs are severely constrained. For example, in the low-delay configuration of the V-PCC reference software, the quantization parameter values of all the frames in a group of pictures are set to be equal. We show that the rate-distortion performance can be improved by relaxing this constraint and treating the QP selection problem as a multi-variable constrained combinatorial optimization problem, where the variables are the QPs. To solve the optimization problem, we propose a variant of the differential evolution (DE) algorithm. Differential evolution is an evolutionary algorithm that has been successfully applied to various optimization problems. In DE, an initial population of randomly generated candidate solutions is iteratively improved. At each iteration, mutants are generated from the population. Crossover between a mutant and a parent produces offspring. If the performance of the offspring is better than that of the parent, the offspring replaces the parent. While DE was initially introduced for continuous unconstrained optimization problems, we adapt it for our constrained combinatorial optimization problem. Also, unlike standard DE, we apply individual mutation to each variable. Furthermore, we use a variable crossover rate to balance exploration and exploitation. Experimental results for the low-delay configuration of the V-PCC reference software show that our method can reduce the average bitrate by up to 43% compared to a method that uses the same QP values for all frames and selects them according to an interior point method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1355573</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1355573</link>
        <title><![CDATA[Precision sketching with de-aging networks in forensics]]></title>
        <pubdate>2024-06-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jason Elroy Martis</author><author>M. S. Sannidhan</author><author>N. Pratheeksha Hegde</author><author>L. Sadananda</author>
        <description><![CDATA[Addressing the intricacies of facial aging in forensic facial recognition, traditional sketch portraits often fall short in precision. This study introduces a pioneering system that seamlessly integrates a de-aging module and a sketch generator module to overcome the limitations inherent in existing methodologies. The de-aging module utilizes a deepfake-based neural network to rejuvenate facial features, while the sketch generator module leverages a pix2pix-based Generative Adversarial Network (GAN) for the generation of lifelike sketches. Comprehensive evaluations on the CUHK and AR datasets underscore the system’s superior efficiency. Significantly, comprehensive testing reveals marked enhancements in realism during the training process, demonstrated by notable reductions in Frechet Inception Distance (FID) scores (41.7 for CUHK, 60.2 for AR), augmented Structural Similarity Index (SSIM) values (0.789 for CUHK, 0.692 for AR), and improved Peak Signal-to-Noise Ratio (PSNR) metrics (20.26 for CUHK, 19.42 for AR). These findings underscore substantial advancements in the accuracy and reliability of facial recognition applications. Importantly, the system, proficient in handling diverse facial characteristics across gender, race, and culture, produces both composite and hand-drawn sketches, surpassing the capabilities of current state-of-the-art methods. This research emphasizes the transformative potential arising from the integration of de-aging networks with sketch generation, particularly for age-invariant forensic applications, and highlights the ongoing necessity for innovative developments in de-aging technology with broader societal and technological implications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1308505</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1308505</link>
        <title><![CDATA[Child face recognition at scale: synthetic data generation and performance benchmark]]></title>
        <pubdate>2024-05-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Magnus Falkenberg</author><author>Anders Bensen Ottsen</author><author>Mathias Ibsen</author><author>Christian Rathgeb</author>
        <description><![CDATA[We address the need for a large-scale database of children’s faces by using generative adversarial networks (GANs) and face-age progression (FAP) models to synthesize a realistic dataset referred to as “HDA-SynChildFaces”. Hence, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which is subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, this pipeline allows the even distribution of the races of subjects, allowing the generation of a balanced and fair dataset with respect to race distribution. The resulting HDA-SynChildFaces consists of 1,652 subjects and 188,328 images, each subject being present at various ages and with many different intra-subject variations. We then evaluated the performance of various facial recognition systems on the generated database and compared the results of adults and children at different ages. The study reveals that children consistently perform worse than adults on all tested systems and that the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and black subjects and females performing worse than white and Latino-Hispanic subjects and males.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1356793</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1356793</link>
        <title><![CDATA[Salient object detection: a mini review]]></title>
        <pubdate>2024-05-10T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Xiuwenxin Wang</author><author>Siyue Yu</author><author>Eng Gee Lim</author><author>M. L. Dennis Wong</author>
        <description><![CDATA[This paper presents a mini-review of recent works in Salient Object Detection (SOD). First, We introduce SOD and its application in image processing tasks and applications. Following this, we discuss the conventional methods for SOD and present several recent works in this category. With the start of deep learning AI algorithms, SOD has also benefited from deep learning. Here, we present and discuss Deep learning-based SOD according to its training mechanism, i.e., fully supervised and weakly supervised. For the benefit of the readers, we have also included some standard data sets assembled for SOD research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1357892</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1357892</link>
        <title><![CDATA[Editorial: Signal processing in computational video and video streaming]]></title>
        <pubdate>2024-01-05T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Anil Kokaram</author><author>Anil Anthony Bharath</author><author>Feng Yang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1338890</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1338890</link>
        <title><![CDATA[Editorial: Editor’s challenge—image processing]]></title>
        <pubdate>2023-12-07T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Frédéric Dufaux</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2023.1271769</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2023.1271769</link>
        <title><![CDATA[Bounds for Haralick features in synthetic images with sinusoidal gradients]]></title>
        <pubdate>2023-11-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ana Oprisan</author><author>Sorinel Adrian Oprisan</author>
        <description><![CDATA[Introduction: The gray-level co-occurrence matrix (GLCM) reduces the dimension of an image to a square matrix determined by the number of gray-level intensities present in that image. Since GLCM only measures the co-occurrence frequency of pairs of gray levels at a given distance from each other, it also stores information regarding the gradients of gray-level intensities in the original image.Methods: The GLCM is a second-order statical method of encoding image information and dimensionality reduction. Image features are scalars that reduce GLCM dimensionality and allow fast texture classification. We used Haralick features to extract information regarding image gradients based on the GLCM.Results: We demonstrate that a gradient of k gray levels per pixel in an image generates GLCM entries on the kth parallel line to the main diagonal. We find that, for synthetic sinusoidal periodic gradients with different wavelengths, the number of gray levels due to intensity quantization follows a power law that also transpires in some Haralick features. We estimate bounds for four of the most often used Haralick features: energy, contrast, correlation, and entropy. We find good agreement between our analytically predicted values of Haralick features and the numerical results from synthetic images of sinusoidal periodic gradients.Discussion: This study opens the possibility of deriving bounds for Haralick features for targeted textures and provides a better selection mechanism for optimal features in texture analysis applications.]]></description>
      </item>
      </channel>
    </rss>