Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice.

Breast volume estimation from systematic series of CT scans using the Cavalieri principle and 3D reconstruction.

Objectives: Preoperative breast volume estimation is very important for the success of the breast surgery. In the present study, two different breast volume determination methods, Cavalieri principle and 3D reconstruction were compared.

Material And Methods: Consecutive sections were taken in slice thickness of 5 mm.

Evaluating the role of content in subjective video quality assessment.

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account.

MR image based approach for metal artifact reduction in X-ray CT.

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images.