Detections of salient region by using fast pixelwise image saliency aggregation (F-PISA)
Rasika Kachore · 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · 2017
Salient object detection is the term that extract the foreground object from background. Saliency mostly occurs from the contrast between object and their neighborhood. Salient region is unique because they have low redundancy information value where other scene may highly redundant. Saliency is broadly classified into color contrast-based, structure contrast-based, a combination of both color and structure contrast-based measures. There are many effective algorithms to analyze different salient detection methods; however there is a trade-off between the algorithm accuracy and algorithm speed. Salient region detection is a more difficult problem in computer vision. It has a wide range of applications, such as object recognition and segmentation. A novel method is simple to determine salient region in the images. The aim of doing silent region detection is to measure the saliency and salience of each pixel in an icon. It too produces a threshold value and detecting foreground region. This paper analyzes the medical images (i.e, CT scan images, such as breast cancer, brain tumor, prostate, lung, and colon cancer) for naming and classifying them among the various lung diseases.