Digital image processing plays a vital role in many applications to retrieve required information from the given image in a way that it has not affect the other features of the image. Image segmentation is one of the most important tasks in image processing which is used to partition an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image. The goal of image segmentation is to cluster pixels into salient image regions corresponding to individual surfaces, objects, or natural parts of objects. With the growing research on image segmentation many segmentation methods have been developed and interpreted differently towards content analysis and image understanding for different applications. Thus an organized review on image segmentation methods is essential and this paper provides a review on the various image segmentation techniques proposed in the literature.

A review on image segmentation techniques

Survey on Image Segmentation Techniques

Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown–Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

An improved Otsu method using the weighted object variance for defect detection

Image segmentation is a mechanism used to divide an image into multiple segments. It will make image smooth and easy to evaluate. Segmentation process also helps to find region of interest in a particular image. The main goal is to make image more simple and meaningful. Existing segmentation techniques can't satisfy all type of images. This survey addressed various image segmentation techniques, evaluates them and presents the issues related to those techniques. 

http://www.researchgate.net/profile/Waseem_Khan24/publication/270517604_Image_Segmentation_Techniques_A_Survey/links/5594fdcc08ae99aa62c5c393.pdf

Image Segmentation Techniques: A Survey

This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for threshold segmentation techniques image. These techniques applied on three satellite images to choose base guesses for threshold segmentation image.

Image Segmentation by Using Threshold Techniques

This paper attempts to undertake the study of three types of noise such as Salt and Pepper (SPN), Random variation Impulse Noise (RVIN), Speckle (SPKN). Different noise densities have been removed between 10% to 60% by using five types of filters as Mean Filter (MF), Adaptive Wiener Filter (AWF), Gaussian Filter (GF), Standard Median Filter (SMF) and Adaptive Median Filter (AMF). The same is applied to the Saturn remote sensing image and they are compared with one another. The comparative study is conducted with the help of Mean Square Errors (MSE) and PeakSignal to Noise Ratio (PSNR). So as to choose the base method for removal of noise from remote sensing image.

/pdf/a-comparative-study-of-removal-noise-from-remote-sensing-4gn2yet4hv.pdf

A Comparative Study of Removal Noise from Remote Sensing Image

This paper attempts to undertake the study of Restored Gaussian Blurred Images by using four types of techniques of deblurring image as Wiener filter, Regularized filter, Lucy Richardson deconvlutin algorithm and Blind deconvlution algorithm with an information of the Point Spread Function (PSF) corrupted blurred image with Different values of Size and Alfa and then corrupted by Gaussian noise The same is applied to the remote sensing image and they are compared with one another, So as to choose the base technique for restored or deblurring imageThis paper also attempts to undertake the study of restored Gaussian blurred image with no any information about the Point Spread Function (PSF) by using same four techniques after execute the guess of the PSF, the number of iterations and the weight threshold of it To choose the base guesses for restored or deblurring image of this techniques

Deblured Gaussian Blurred Images

Network penetration testing identifies the exploits and vulnerabilities those exist within computer network infrastructure and help to confirm the security measures. The objective of this paper is to explain methodology and methods behind penetration testing and illustrate remedies over it, which will provide substantial value for network security Penetration testing should model real world attacks as closely as possible. An authorized and scheduled penetration testing will probably detected by IDS (Intrusion Detection System). Network penetration testing is done by either or manual automated tools. Penetration test can gather evidence of vulnerability in the network. Successful testing provides indisputable evidence of the problem as well as starting point for prioritizing remediation. Penetration testing focuses on high severity vulnerabilities and there are no false positive.

Penetration Testing: A Roadmap to Network Security

The purposes of this paper have to discuss issues related to Network Traffic Management. A relatively new category of network management is fast becoming a necessity in converged business Networks. Mid-sized and large organizations are finding they must control network traffic behavior to assure that their strategic applications always get the resources they need to perform optimally. Controlling network traffic requires limiting bandwidth to certain applications, guaranteeing minimum bandwidth to others, and marking traffic with high or low priorities. This exercise is called Network Traffic Management.

Namdeo V. Kalyankar

Papers

Image Segmentation by Using Threshold Techniques

A Comparative Study of Removal Noise from Remote Sensing Image

Deblured Gaussian Blurred Images

Penetration Testing: A Roadmap to Network Security

Network Traffic Management