NGC 6946 have been observed with BVRI filters, on October 15-18,
2012, with the Newtonian focus of the 1.88m telescope, Kottamia
observatory, of the National Research Institute of Astronomy and
Geophysics, Egypt (NRIAG), then we combine the BVRI filters to
obtain an astronomical image to the spiral galaxy NGC 6946 which
is regarded main source of information to discover the components of
this galaxy, where galaxies are considered the essential element of
the universe. To know the components of NGC 6946, we studied it
with the Variable Precision Rough Sets technique to determine the
contribution of the Bulge, disk, and arms of NGC 6946 according to
different color in the image. From image we can determined th

In this work, watershed transform method was implemented to detect and extract tumors and abnormalities in MRI brain skull stripped images. An adaptive technique has been proposed to improve the performance of this method.Watershed transform algorithm based on clustering techniques: K-Means and FCM were implemented to reduce the oversegmentation problem. The K-Means and FCM clustered images were utilized as input images to the watershed algorithm as well as of the original image. The relative surface area of the extracted tumor region was calculated for each application. The results showed that watershed trnsform algorithm succeedeed to detect and extract the brain tumor regions very well according to the consult of a specialist doctor a

Nowadays, the advances in information and communication technologies open the wide door to realize the digital world’s dream. Besides, within the clear scientific scope in all fields, especially the medical field, it has become necessary to harness all the scientific capabilities to serve people, especially in medical-related services. The medical images represent the basis of clinical diagnosis and the source of telehealth and teleconsultation processes. The exchange of these images can be subject to several challenges, such as transmission bandwidth, time delivery, fraud, tampering, modifying, privacy, and more. This paper will introduce an algorithm consisting a combination of compression and encryption techniques to meet such chall

    Multiple applications use offline handwritten signatures for human verification. This fact increases the need for building a computerized system for signature recognition and verification schemes to ensure the highest possible level of security from counterfeit signatures. This research is devoted to developing a system for offline signature verification based on a combination of local ridge features and other features obtained from applying two-level Haar wavelet transform. The proposed system involves many preprocessing steps that include a group of image processing techniques (including: many enhancement techniques, region of interest allocation, converting to a binary image, and Thinning). In feature extraction and

The need for a flexible and cost effective biometric security system is the inspired of this paper. Face recognition is a good contactless biometric and it is suitable and applicable for Wireless Sensor Network (WSN). Image processing and image communication is a challenges task in WSN due to the heavy processing and communication that reduce the life time of the network. This paper proposed a face recognition algorithm on WSN depending on the principles of the unique algorithm that hold the capacity of the network to the sink node and compress the communication data to 89.5%. An efficient hybrid method is introduced based upon the advantage of Zak transform to offprint the farthest different features of the face and Eigen face method to

     Recognizing cars is a highly difficult task due to the wide variety in the appearance of cars from the same car manufacturer. Therefore, the car logo is the most prominent indicator of the car manufacturer. The captured logo image suffers from several problems, such as a complex background, differences in size and shape, the appearance of noise, and lighting circumstances. To solve these problems, this paper presents an effective technique for extracting and recognizing a logo that identifies a car. Our proposed method includes four stages: First, we apply the k-medoids clustering method to extract the logo and remove the background and noise. Secondly, the logo image is converted to grayscale and also converted to a binary imag

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

NeighShrink is an efficient image denoising algorithm based on the discrete wavelet
transform (DWT). Its disadvantage is to use a suboptimal universal threshold and identical
neighbouring window size in all wavelet subbands. Dengwen and Wengang proposed an
improved method, which can determine an optimal threshold and neighbouring window size
for every subband by the Stein’s unbiased risk estimate (SURE). Its denoising performance is
considerably superior to NeighShrink and also outperforms SURE-LET, which is an up-todate
denoising algorithm based on the SURE. In this paper different wavelet transform
families are used with this improved method, the results show that Haar wavelet has the
lowest performance among

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are