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- Dimitris N. MetaxasSuggested from co-authorship
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Works153 from public data
- A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI1,322
A novel region-based method for image segmentation, which is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction).
- Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion400
A novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors is proposed, which inherently improves SS optimization through fully end-to-end training.
- SmartEdit: Exploring Complex Instruction-Based Image Editing with Multimodal Large Language Models213
A Bidirectional Interaction Module (BIM) is proposed that enables comprehensive bidirectional information interactions between the input image and the MLLM output and demonstrates that a small amount of complex instruction editing data can effectively stimulate SmartEdit’ s editing capabilities for more complex instructions.
- A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity196
A variational level set approach to joint segmentation and bias correction of images with intensity inhomogeneity that is robust to initialization, and therefore allows automatic applications, and has been used for images of various modalities with promising results.
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- Person Search in a Scene by Jointly Modeling People Commonness and Person Uniqueness146
This paper proposes a unified framework which jointly models the commonness of people and the uniqueness of a person and demonstrates superior performance of this approach on public benchmarks compared with the sequential combination of the state-of-the-art detection and identification algorithms.
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- BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification120
Experimental results on multiple benchmarks show that BiCnet-TKS outperforms state-of-the-arts with about 50% less computations.
- Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models106
A novel image representation method that characterizes an image as a spatiogram--a generalized histogram--of colors quantized by Gaussian Mixture Models (GMMs) is proposed, which employs Gaussian color components instead of discrete color bins.
- AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing93
This paper proposes a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multi-level semantics while keeping the efficiency high, and achieves different levels of speed/accuracy trade-offs on Cityscapes.
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- Three-Dimensional 123 I- Meta -Iodobenzylguanidine Cardiac Innervation Maps to Assess Substrate and Successful Ablation Sites for Ventricular Tachycardia82
123I-mIBG innervation defects are larger than bipolar voltage–defined scar and cannot be detected with standard voltage criteria, which could improve understanding of VT substrate and facilitate VT ablation.
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- Fully Attentional Network for Semantic Segmentation65
This work proposes a new approach, namely Fully Attentional Network (FLANet), to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency.
- Learning Memory Augmented Cascading Network for Compressed Sensing of Images60
A cascading network for compressed sensing of images with progressive reconstruction by decomposing the complex reconstruction mapping into the cascade of incremental detail reconstruction (IDR) modules and measurement residual updating (MRU) modules.
- Accurate and robust facial expressions recognition by fusing multiple sparse representation based classifiers50
This paper presents an effective and efficient approach based on simulating the information processing procedure of the biological visual system to solve the occlusion problem in facial expression recognition that achieves better performance than many existing methods, and it is robust to both random occlusions and the major component Occlusions.
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- Image segmentation using spectral clustering of Gaussian mixture models46
A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper and the experimental evaluation on the IRIS dataset and the real-world image segmentsation problem demonstrates the effectiveness of the proposed approach.
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- Dynamic multi-channel metric network for joint pose-aware and identity-invariant facial expression recognition41
A dynamic multi-channel metric learning network for pose-aware and identity-invariant FER, called DML-Net, which can reduce the effects of pose and identity for robust FER performance and suppress overfitting and significantly improving recognition.
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- License plate detection using gradient information and cascade detectors37
The proposed method consists of three main modules: image preprocessing, license plate detection and license plate confirmation, which involves several heuristic judgment strategies and a voting-based method to verify the candidate license plates.
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- Hybrid SPECT/CT for Attenuation Correction of Stress Myocardial Perfusion Imaging34
Attenuation correction using Hybrid SPECT/CT improved specificity in the right coronary artery, but decreased specificity inThe left anterior descending, which is likely to be more useful in men than in women.
- HighEr-Resolution Network for Image Demosaicing and Enhancing29
A HighEr-Resolution Network (HERN) to fully learning global information in high-resolution image patches by combining global-aware features and multi-scale features, which achieves state-of-the-art performance on the AIM2019 RAW to RGB mapping challenge.
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- Using tri-training to exploit spectral and spatial information for hyperspectral data classification12
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- Spatial multi-scale gradient orientation consistency for place instance and Scene category recognition6
This paper presents a novel image feature called Gradient Orientation Consistency (GOC) and an image representation based on the spatial multi-scale GOC histogram for place instance and scene category recognition and shows that it outperforms the state of the art.
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- Biologically Inspired Scene Context for Object Detection Using a Single Instance6
The proposed object detection method is robust to large intra-class variations, occlusions, a minor variety of poses, low-revolution conditions, background clutter etc., and there is no off-line training.
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- EXEMPLAR-BASED LINEAR DISCRIMINANT ANALYSIS FOR ROBUST OBJECT TRACKING5
This paper proposes a novel tracking framework based on exemplar detector rather than category detector, an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors, which is quite specific and discriminative.
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- ClickAttention: Click Region Similarity Guided Interactive Segmentation3
This work proposes a click attention algorithm that expands the influence of positive clicks by leveraging the similarity between positively-clicked regions and the entire input, and introduces a discriminative affinity loss to reduce attention coupling between positive and negative click regions.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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