Automatic image enhancement using intrinsic geometrical information

Automatic image enhancement using intrinsic geometrical information

Towards Automatic Image Enhancement Using Genetic Algorithms

This paper introduces a new automatic image enhancement technique based on real-coded Genetic Algorithms (GAs). The task of the GA is to adapt the parameters of a novel extension to a local enhancement technique similar to statistical scaling, as to enhance the contrast and detail in the image according to an objective fitness criterion. We compared our method with other automatic enhancement t...

Image enhancement using color and spatial information

In this paper, a new filter that is performing color image enhancement is presented. The filter is achieving this through the minimization of a weighted cost function. The weights are determined using potential functions which are calculated in such a way as to convey spatial information. Application of the proposed filter on a real blurred and noisy color image is performed to verify its enhan...

Automatic Image Annotation Using Auxiliary Text Information

The availability of databases of images labeled with keywords is necessary for developing and evaluating image annotation models. Dataset collection is however a costly and time consuming task. In this paper we exploit the vast resource of images available on the web. We create a database of pictures that are naturally embedded into news articles and propose to use their captions as a proxy for...

Semi-Automatic Image Labelling Using Depth Information

Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a ne...