Current deep learning (DL)-based palmprint verification models rely on centralized training with large datasets, which raises significant privacy concerns due to biometric data's sensitive and immutable nature. Federated learning~(FL), a privacy-preserving distributed learning paradigm, offers a compelling alternative by enabling collaborative model training without the need for data sharing. However, FL-based palmprint verification faces critical challenges, including data heterogeneity from diverse identities and the absence of standardized evaluation benchmarks. This paper addresses these gaps by establishing a comprehensive benchmark for FL-based palmprint verification, which explicitly defines and evaluates two practical scenarios: closed-set and open-set verification. We propose FedPalm, a unified FL framework that balances local adaptability with global generalization. Each client trains a personalized textural expert tailored to local data and collaboratively contributes to a shared global textural expert for extracting generalized features. To further enhance verification performance, we introduce a Textural Expert Interaction Module that dynamically routes textural features among experts to generate refined side textural features. Learnable parameters are employed to model relationships between original and side features, fostering cross-texture-expert interaction and improving feature discrimination. Extensive experiments validate the effectiveness of FedPalm, demonstrating robust performance across both scenarios and providing a promising foundation for advancing FL-based palmprint verification research.

Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.

Abstract: In this paper, a novel perceptual image hashing scheme for color images is proposed based on ring-ribbon quadtree and color vector angle. First, original image is subjected to normalization and Gaussian low-pass filtering to produce a secondary image, which is divided into a series of ring-ribbons with different radii and the same number of pixels. Then, both textural and color features are extracted locally and globally. Quadtree decomposition (QD) is applied on luminance values of the ring-ribbons to extract local textural features, and the gray level co-occurrence matrix (GLCM) is used to extract global textural features. Local color features of significant corner points on outer boundaries of ring-ribbons are extracted through color vector angles (CVA), and color low-order moments (CLMs) is utilized to extract global color features.

Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crowns from UAV imagery; (b) machine learning of species based on spectral and textural features within individual tree crowns, and (c) automatic segmentation of orthomosaiced UAV imagery into 'superpixels', using Simple Linear Iterative Clustering (SLIC). Creating superpixels reduces the dataset's dimensionality and focuses prediction onto clusters of pixels, greatly improving accuracy. To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia. Random forests were most accurate at discriminating species for whole crowns, with accuracy ranging from 79.3% when mapping five common species, to 90.5% when mapping the three most visually-distinctive species. In contrast, support vector machines proved better for labelling automatically segmented superpixels, with accuracy ranging from 74.3% to 91.7% for the same species. Models were extended to map species across 100 hectares of forest. The study demonstrates the power of SLIC-UAV for mapping characteristic early-successional tree species as an indicator of successional stage within tropical forest restoration areas. Continued effort is needed to develop easy-to-implement and low-cost technology to improve the affordability of project management.

This paper presents a useful method to achieve classification in satellite imagery. The approach is based on pixel level study employing various features such as correlation, homogeneity, energy and contrast. In this study gray-scale images are used for training the classification model. For supervised classification, two classification techniques are employed namely the Support Vector Machine (SVM) and the Naive Bayes. With textural features used for gray-scale images, Naive Bayes performs better with an overall accuracy of 76% compared to 68% achieved by SVM. The computational time is evaluated while performing the experiment with two different window sizes i.e., 50x50 and 70x70. The required computational time on a single image is found to be 27 seconds for a window size of 70x70 and 45 seconds for a window size of 50x50.

During the millions of pregnancies that occur in the United States every year, expectant moms learn oodles about their developing fetuses over months of gestation. But the placenta, a vital and temporary organ that shelters the fetus--delivering life-sustaining nutrients and oxygen, getting rid of toxic by-products and modulating the immune system to protect the pregnancy--largely remains a mystery. A team of Children's National Health System research scientists is beginning to provide insights about the poorly understood placenta. Using three-dimensional (3D) magnetic resonance imaging (MRI), the research team characterized the shape, volume, morphometry and texture of placentas during pregnancy and, using a novel framework, predicted with high accuracy which pregnancies would be complicated by fetal growth restriction (FGR). "When the placenta fails to carry out its essential duties, both the health of the mother and fetus can suffer and, in extreme cases, the fetus can die. Because there are few non-invasive tools that reliably assess the health of the placenta during pregnancy, unfortunately, placental disease may not be discovered until too late--after impaired fetal growth already has occurred," says Catherine Limperopoulos, Ph.D., co-director of research in the Division of Neonatology at Children's National Health System and senior author of the study published online July 22 in Journal of Magnetic Resonance Imaging.