The synthetic aperture radar (SAR) imagery plays a critical role in flood mapping due to its ability to capture data under all-weather and day-and-night conditions. However, the existing SAR segmentation methods, including the state-of-the-art HisSegNet, face challenges, such as limited generalization, insufficient utilization of SAR-specific features, and suboptimal performance on diverse datasets. To address these limitations, we propose enhanced-HisSegNet, a multimodal fusion strategy that builds upon HisSegNet by integrating learnable histogram layers (HLs) tailored for SAR data with active contour models (ACMs) for precise boundary refinement. These components are embedded into fine-tuned deep segmentation neural networks (DSNNs) to improve segmentation accuracy. Our model was evaluated on real SAR datasets, employing cross-dataset validation for robustness. Experimental results demonstrate significant performance gains, with up to 10% improvement in intersection over union (IoU)—a key metric that measures segmentation accuracy by computing the ratio of intersection to union between the predicted and ground truth regions—on internal datasets and 4% on external datasets, showcasing enhanced accuracy, robustness, and applicability. The code for this work is available athttps://github.com/Mohsena1990/Enhanced-HistSegNet.

Predicting survival rates based on multi-gigapixel histopathology images is one of the most challenging tasks in digital pathology. Due to the computational complexities, Multiple Instance Learning (MIL) has become the conventional approach for this process as it breaks the image into smaller patches. However, this technique fails to account for the individual cells present in each patch, while they are the fundamental part of the tissue. In this work, we developed a novel dynamic and hierarchical point-cloud-based method (CO-PILOT) for the processing of cellular graphs extracted from routine histopathology images. By using bottom-up information propagation and top-down conditional attention, our model gains access to an adaptive focus across different levels of tissue hierarchy. Through comprehensive experiments, we demonstrate that our model can outperform all the state-of-the-art methods in survival prediction, including the hierarchical Vision Transformer (ViT), across three datasets and four metrics with only half of the parameters of the closest baseline. Importantly, our model is able to stratify the patients into different risk cohorts with statistically different outcomes across three large datasets, a task that was previously achievable only using genomic information. Furthermore, we publish a large dataset containing 873 cellular graphs from 188 patients, along with their survival information, making it one of the largest publicly available datasets in this context.

Hand gesture recognition (HGR) from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of the human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of keyframes of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatiotemporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient and local binary pattern are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D HGR. We also achieve competitive results on NTU action dataset.

The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.