The world we live in is full of enormous masses of digital visual information. This enormous amount of digital visual information motivates us to develop robust and efficient object recognition technique. Most of the work reported in this paper focuses focus light upon efficient techniques that can be used for recognition of object and its applications. Here in this paper, various techniques for object recognition in an image are discussed.

In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

Network traffic analysis is a crucial step in developing efficient congestion control systems and identifying valid and malicious packets. Because network resources are apportioned based on predicted usage, these solutions reduce network congestion. For a variety of reasons, including dynamic bandwidth allocation, network security, and network planning, the ability to forecast network traffic is critical. Machine learning (ML) techniques to network traffic analysis have received a lot of interest. This article outlines an approach for analyzing network traffic. Three machine learning-based methodologies make up the methodology. The experimental investigation employed the NSL KDD data set. On the basis of accuracy and other criteria, KNN, Support vector machine, and nave bayes are compared.

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This paper deals with phase of designing a new approach of segmentation Global Region-based Model and Local Region-based Model. The brief description of proposed scheme is presented, followed by the design. The chapter begins with the introduction followed by discussion of experimental results. The proposed segmentation approach is detailed and followed by its evaluation and comparisons with some existing methods.