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- Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods710
Some of the most recent methods that have been specifically developed for the underwater environment are reviewed, capable of extending the range of underwater imaging, improving image contrast and resolution.
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- Quantitative evaluation of color image segmentation results413
This paper considers the problem of the automatic evaluation of the results of color image segmentation, and identifies some limitations in this evaluation function, and proposes two enhanced functions that correspond more closely to visual judgment.
- On the use of deep learning for blind image quality assessment369
The best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image, having a linear correlation coefficient with human subjective scores of almost 0.91.
- 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.
- Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity285
A region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity, which outperforms the state of the art.
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- 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.
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- An innovative algorithm for key frame extraction in video summarization185
The algorithm, which escapes the complexity of existing methods based, for example, on clustering or optimization strategies, dynamically and rapidly selects a variable number of key frames within each sequence by analyzing the differences between two consecutive frames of a video sequence.
- Color-based image retrieval using spatial-chromatic histograms176
A new indexing methodology for image databases integrating color and spatial information for content-based image retrieval, called Spatial-Chromatic Histogram (SCH), can be more satisfactory than standard techniques when the user would like to retrieve from the database the images that actually resemble the query image selected in their color distribution characteristics.
- 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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- CNN-based features for retrieval and classification of food images145
Results demonstrate that the features extracted from the Food-475 database outperform the other ones showing that the authors need larger food databases in order to tackle the challenges in food recognition, and that the created database is a step forward toward this end.
- A segmentation algorithm for color images134
A method that combines clustering and region merging for color image segmentation using recursive one-dimensional histogram analysis and a criterion that takes into account color similarity and spatial proximity is presented.
- Deep learning for logo recognition126
The proposed recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network specifically trained for logo classification, even if they are not precisely localized.
- A survey on methods for colour image indexing and retrieval in image databases126
This paper provides a comprehensive survey of the methods for color image indexing and retrieval described in the literature, in particular, image preprocessing, the features used to represent color information, and the measures adopted to compute the similarity between the features of two images are critically analyzed.
- Color balancing of digital photos using simple image statistics123
This paper describes a method for detecting and removing a color cast from a digital photo without any a priori knowledge of its semantic content, and proposes a modified version of the white balance algorithm for this purpose.
- How Far Can You Get by Combining Change Detection Algorithms?121
Results demonstrate that starting from simple algorithms the proposed IUTIS combination strategy can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.
- 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.
- A relevance feedback mechanism for content-based image retrieval112
A new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the query in order to accurately represent the user's particular information needs.
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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 2026-10-14. 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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