Image Cropping
35 papers with code • 1 benchmarks • 4 datasets
Image Cropping is a common photo manipulation process, which improves the overall composition by removing unwanted regions. Image Cropping is widely used in photographic, film processing, graphic design, and printing businesses.
Libraries
Use these libraries to find Image Cropping models and implementations
AADB
CUHK Image Cropping
GNMC
Latest papers with no code
Learning Subject-Aware Cropping by Outpainting Professional Photos
How to frame (or crop) a photo often depends on the image subject and its context; e. g., a human portrait.
Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask
Various detector systems worldwide have been developed and implemented, with convolutional neural networks chosen for their superior performance accuracy and speed in object detection.
Image Cropping under Design Constraints
We explore two derived approaches, a proposal-based approach, and a heatmap-based approach, and we construct a dataset for evaluating the performance of the proposed approaches on image cropping under design constraints.
Challenges of building medical image datasets for development of deep learning software in stroke
Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited.
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection
Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images.
Construction of unbiased dental template and parametric dental model for precision digital dentistry
In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.
Lightweight High-Performance Blind Image Quality Assessment
Blind image quality assessment (BIQA) is a task that predicts the perceptual quality of an image without its reference.
Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression
Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, ie, similar composition patterns.
Image Cropping With Spatial-Aware Feature and Rank Consistency
To address the first issue, we propose spatial-aware feature to encode the spatial relationship between candidate crops and aesthetic elements, by feeding the concatenation of crop mask and selectively aggregated feature maps to a light-weighted encoder.
An Experience-based Direct Generation approach to Automatic Image Cropping
Prior approaches for automatic image cropping, did not enforce the aspect ratio of the outputs, likely due to a lack of datasets for this task.
