IMAGE AND VISION COMPUTING(ISSN:0262-8856; indexed by SCIE; IF 4.2) Introduction:Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. Aims and scope:Image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases. Open Access:Hybrid. | Signal Processing: Image Communication(ISSN：0923-5965; Indexed by SCIE; IF 2.1) Introduction:Signal Processing: Image Communicationis an international journal for the development of the theory and practice of image communication. Aims and scope: ØMultimedia coding, 3D and multi-view compression for human and machine consumption Ø3D and multi-view representations, multimedia pre/post processing, video restoration and super-resolution, video synthesis ØPeer-to-peer video networking, wireless video broadcasting and communication, visual surveillance, video processing for automation and remote operation, architectures for image/video processing and communication ØInteractive visual communication, Virtual Reality (VR), Augmented Reality (AR) and eXtended Reality (XR) systems, point cloud processing and compression, light field capture and rendering, immersive media technologies, real-time 3D environment reconstruction ØMultimedia quality assessment and measures, models for QoS and QoE ØMulti-camera video analysis, motion analysis, content-based media indexing and retrieval, semantics, face and gesture processing Open Access:Hybrid. |
Signal Processing (ISSN：0165-1684; Indexed by SCIE; IF 3.7) Introduction:Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work covering novel signal processing tools as well as tutorial and review articles with a focus on the signal processing issues. It is intended for a rapid dissemination of knowledge to engineers and scientists working in the research, development or practical application of signal processing. Aims and scope: Open Access:Hybrid. | Pattern Recognition (ISSN：0031-3203; Indexed by SCIE; IF 9.1) Introduction: Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Aims and scope: ➤ Visual tracking, pose estimation and action understanding:single and multi-object tracking, video action recognition, online action detection and anticipation, temporal action segmentation, 3D human pose estimation, crowd counting, light field depth estimation, monocular 3D object detection
➤ Image restoration, enhancement and computational imaging:low-light enhancement, dehazing and deraining, super-resolution (image, video, face, infrared, light field), image fusion (infrared-visible, multi-exposure, multi-source), image and stereo compression, MRI reconstruction and sampling pattern design, deep priors and implicit neural representations, diffusion-model-driven restoration and generation ➤ Multimodal learning, vision-language and multimodal large models:vision-language alignment and prompt learning (CLIP/SAM adaptation), referring understanding and visual grounding, hallucination mitigation in multimodal LLMs, medical report generation, multimodal sentiment analysis, cross-modal retrieval and generation ➤ Clustering, graph learning and multi-view/multi-label learning:multi-view clustering and co-training, graph pooling and graph classification, hypergraph and graph neural networks, multi-label learning (including incomplete labels), multi-label feature selection, subspace and tensor representations, dimensionality reduction and feature extraction ➤ Learning paradigms and robustness:incremental/continual learning, domain adaptation and generalization, federated learning, semi-supervised and self-supervised learning, few-shot and zero-shot learning, active learning, out-of-distribution and adversarial robustness, model pruning and efficient inference ➤ Biomedical and bioinformatics pattern analysis:medical image segmentation, diagnosis and prognosis (ultrasound, CT, whole-slide pathology), physiological signal (EEG/ECG) classification, single-cell and omics data modelling, biomolecular association prediction and retrosynthesis prediction ➤ Remote sensing, hyperspectral and cross-domain vision:hyperspectral image classification (including cross-domain and class-incremental), remote sensing change detection, multi-source remote sensing fusion, remote sensing object detection and scene understanding ➤ 3D vision and scene understanding:point cloud semantic segmentation, neural radiance fields and Gaussian splatting, 3D scene localization and reconstruction, 3D object detection, 3D stylization and content generation ➤ Cross-disciplinary applications and emerging directions:document analysis and handwritten signature verification, face security and anonymization, deep steganography and information hiding, time series classification and event prediction, online learning over data streams, fuzzy systems and granular computing, evolutionary computation and pattern-mining-driven decision optimization Open Access:Hybrid. |