Despite the widespread use of deep learning methods for semantic segmentation of images that are acquired from a single source, clinicians often use multi-domain data for a detailed analysis.
Automatic segmentation of medical images is among most demanded works in the medical information field since it saves time of the experts in the field and avoids human error factors.
The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware.
The results validate that the proposed method obtains state-of-the-art hyperspectral unmixing performance particularly on the real datasets compared to the baseline techniques.
In this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo.
Moreover, the method is able to obtain accurate pixel-level segmentation and classification results from a set of noisy labeled RGB color images.
Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications.
Our results show that the system can respond to video queries on a large video database with fast query times, high recall rate and very low memory and disk requirements.