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- Image annotation using SVM314
An innovative image annotation tool for classifying image regions in one of seven classes - sky, skin, vegetation, snow, water, ground, and buildings - or as unknown is described.
- 3D face detection using curvature analysis255
This work presents an innovative method that combines a feature-based approach with a holistic one for three-dimensional (3D) face detection, which has been tested, with good results, on some 150 3D faces acquired by a laser range scanner.
- Color constancy using CNNs232
Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods to accurately predict the scene illumination.
- Single and Multiple Illuminant Estimation Using Convolutional Neural Networks168
An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of this three-stage method.
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- Automatic color constancy algorithm selection and combination119
This work investigates how illuminant estimation techniques can be improved taking into account intrinsic, low level properties of the images, and shows how these properties can be used to drive the selection of the best algorithm for a given image.
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- Evaluating color texture descriptors under large variations of controlled lighting conditions67
An extensive comparison of old and new texture features, with and without a color normalization step, is reported, with a particular focus on how these features are affected by small and large variations in the lighting conditions.
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- Artistic photo filter removal using convolutional neural networks48
The method uses a convolutional neural network for the prediction of the coefficients of local polynomial transformations that are applied to the input image and shows that the quality of the restoration performed by the method is clearly superior to that of traditional color balancing and restoration procedures.
- Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces48
This paper presents an innovative three dimensional occlusion detection and restoration strategy for the recognition of three dimensional faces partially occluded by unforeseen, extraneous objects and demonstrates the robustness and feasibility of the approach.
- Combining local binary patterns and local color contrast for texture classification under varying illumination47
A texture descriptor for color texture classification specially designed to be robust against changes in the illumination conditions, which outperforms the original LBP approach and its color variants, even when these are computed after color normalization.
- Gappy PCA Classification for Occlusion Tolerant 3D Face Detection47
The proposed approach for the detection of faces in three dimensional scenes is tolerant against partial occlusions produced by the presence of any kind of object and can be used to improve the robustness of all those systems requiring a face detection stage in non-controlled scenarios.
- On the detection of pornographic digital images46
Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM) have been used to perform the classification of pornographic and non-pornographic photographs.
- Image orientation detection using LBP-based features and logistic regression41
An algorithm for the automatic detection of the image orientation that relies on the image content as described by Local Binary Patterns (LBP) is presented and its accuracy is comparable with that of human observers in detecting the correct orientation of a wide range of image contents.
- TreEnhance: A tree search method for low-light image enhancement40
The method combines tree search theory, and in particular the Monte Carlo Tree Search (MCTS) algorithm, with deep reinforcement learning and proposes a guided search strategy that reverses the enhancement procedure that a photo editor applied to a given input image.
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- Improving CNN-Based Texture Classification by Color Balancing40
It is shown how suitable color balancing models allow for a significant improvement in the accuracy in recognizing textures for many CNN architectures.
- AUTOMATIC CLASSIFICATION OF DIGITAL PHOTOGRAPHS BASED ON DECISION FORESTS37
It is shown here how low-level features can be related to semantic photo categories, such as indoor, outdoor and close-up, using decision forests consisting of trees constructed according to CART methodology.
- Combining multiple features for color texture classification34
It is shown that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions and how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy.
- Visual recognition of aircraft mechanical parts for smart maintenance33
A visual recognition module of aircraft mechanical parts that has been included in a prototype system designed for the smart maintenance of the Alenia-Aermacchi M346 confirms the feasibility of the proposed recognition module in a very challenging and realistic condition.
- Halfway through the semantic gap: Prosemantic features for image retrieval33
An image description approach based on prosemantic features, which are obtained through a two-level feature extraction process and used to index images in an image retrieval system where searches are performed using relevance feedback.
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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