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- Azriel RosenfeldSuggested from co-authorship
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Works337 from public data
- 947
- Shape similarity measure based on correspondence of visual parts532
This work applied a cognitively motivated similarity measure to shape matching of object contours in various image databases and compared it to well-known approaches in the literature, justifying that the shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.
- 454
- 425
- Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation411
This paper presents LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation, which adopts a ResNet as backbone network to greatly reduce computation cost while maintaining higher segmentation accuracy.
- Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution409
A novel rule is obtained, called the hierarchical convexity rule, which states that visual parts are enclosed by maximal convex (with respect to the object) boundary arcs at different stages of the contour evolution.
- 377
- Outlier Detection with Kernel Density Functions328
A novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed, modifying a nonparametric density estimate with a variable kernel to yield a robust local density estimation.
- 307
- GIFT: A Real-Time and Scalable 3D Shape Search Engine298
The proposed 3D shape search engine, which combines GPU acceleration and Inverted File (Twice), is named as GIFT, which outperforms the state-of-the-art methods significantly in retrieval accuracy on various shape benchmarks and competitions.
- 219
- 204
- Shape matching and classification using height functions193
A novel shape descriptor for matching and recognizing 2D object silhouettes that is not only invariant to geometric transformations such as translation, rotation and scaling but also insensitive to nonlinear deformations due to noise and occlusion is proposed.
- DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks192
An accurate method for detecting the mitotic cells from histopathological slides using a novel multi‐stage deep learning framework and can achieve the highest F‐score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network.
- Bag of contour fragments for robust shape classification178
A new shape representation called Bag of Contour Fragments (BCF) inspired by classical Bag of Words (BoW) model is developed, which achieves the state-of-the-art performance on several well-known shape benchmarks, and can be applied to real image classification problem.
- Weakly supervised mitosis detection in breast histopathology images using concentric loss168
An automatic and accurate system for detecting mitosis in histopathology images using a deep segmentation network to produce segmentation map and a novel concentric loss function is proposed to train the semantic segmentsation network on weakly supervised mitosis data.
- 165
- Improving Shape Retrieval by Learning Graph Transduction155
This paper considers the existing shapes as a group, and study their similarity measures to the query shape in a graph structure, and learns a better metric through graph transduction by propagating the model through existing shapes, in a way similar to com- puting geodesics in shape manifold.
- 153
- Multi-scale deep context convolutional neural networks for semantic segmentation132
This paper designs a multi-scale deep context convolutional network (MDCCNet), which combines the feature maps from different levels of network in a holistic manner for semantic segmentation and demonstrates that the method outperforms or is comparable to state-of-the-art methods on PASCAL VOC 2012 and SIFTFlow semantic segmentations datasets.
- AGLNet: Towards real-time semantic segmentation of self-driving images via attention-guided lightweight network129
An attention-guided lightweight network, namely AGLNet, which employs an encoder–decoder architecture for real-time semantic segmentation, and achieves state-of-the-art results in terms of speed and accuracy on three self-driving datasets.
- Well-Composed Sets127
It is proved that the skeletons obtained are "one point thick" and the formal definition of this concept is given, and it is shown that these skeletons have a graph structure and what this means.
- Balancing Deformability and Discriminability for Shape Matching121
This work presents the aspect shape context, which extends shape context-based descriptors and adaptively selects the "best" aspect weight for shape comparison, and shows that this framework naturally unifies existing important shape and image representations by adjusting the aspect weight and the embedding.
- Application of planar shape comparison to object retrieval in image databases116
Experimental results show that the shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.
- 111
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 2026-10-15. 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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