Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

The research aims:
1- To know the intemperance use of the internet by the University's students.
2- To know the differences with a statistical indication in the intemperance use of the internet after the gender's inconstant ( Males-females).
3- To know the differences with a statistical indication in the intemperance use of the internet after the Major's inconstant ( scientific- humanistic)
This current research restricts a sample of the university students for both sexes.
Theoretical frame:
The research viewed several theories that explains intemperance use of the internet & the results.
Research measures :
1- Research has been adapted the scale ( Internet use) which was prepared by the Moroccan researcher (

Deep Learning Techniques For Skull Stripping of Brain MR Images

In the presence of deep submicron noise, providing reliable and energy‐efficient network on‐chip operation is becoming a challenging objective. In this study, the authors propose a hybrid automatic repeat request (HARQ)‐based coding scheme that simultaneously reduces the crosstalk induced bus delay and provides multi‐bit error protection while achieving high‐energy savings. This is achieved by calculating two‐dimensional parities and duplicating all the bits, which provide single error correction and six errors detection. The error correction reduces the performance degradation caused by retransmissions, which when combined with voltage swing reduction, due to its high error detection, high‐energy savings are achieved. The res

Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in disti

This study was carried out to assess genetic diversity of ten cultivars of Rice (Oryza sativa L.). One of DNA markers based on Polymerase Chain Reaction (PCR) was used namely DAF markers (DNA Amplification Fingerprint). Six primers were tested, the results showed, that no amplification products using the primers OPD.14 and OPM.5. Two primers (OPX.8 and OPT.2) produced monomorphic band across all cultivars, while only two primers generated polymorphic bands. The number of total bands produced from one of them (OPN.7) were sixteen. Also this primer produced ten polymorphic profiles (DAF patterns) which were unique to the ten cultivars that could be distinguished. The number of total bands generated by primer OPX.1 were thirteen and this prim

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati