The Brain Tumor Segmentation Using Fuzzy C-Means Technique

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

A Multi-Stage Framework for Classification of Unconstrained Image Data From Mobile Phones

During the past decade, the number of mobile electronic devices equipped with cameras has increased dramatically and so has the number of real-world applications for image classification. In many of these applications, the image data is captured in an uncontrolled manner and in complex environments and conditions under which existing image classification techniques may not perform well. In this paper, the authors provide a detailed description of an efficient multi-stage image classification framework that is robust enough to remain effective also under challenging imaging conditions, and demonstrate its effectiveness in the context of classification of real-world images of dumpsters captured by mobile phones in the metropolitan city of Hyderabad. Their system is able to achieve accurate classification of the cleanliness state of the dumpsters by utilizing a multi-stage approach, where the first stage is the efficient detection of the dumpster and the second stage is the classification of its state. The authors provide a detailed analysis of the performance of the system as well as comprehensive experimental results on real-world image data.

Image Processing Including Medical Liver Imaging

Medical diagnosis has been gaining importance in everyday life. The diseases and their symptoms are highly varying and there is always a need for a continuous update of knowledge needed for the doctors. The diseases fall into different categories and a small variation of symptoms may leave to different categories of diseases. This is further supplemented by the medical analysts for a continuous treatment process. The treatment generally starts with a diagnosis and further goes through a set of procedures including X-ray, CT-scans, ultrasound imaging for qualitative analysis and diagnosis by doctors. A small level of error in disease identification introduces overhead in diagnosis and difficult in treatment. In such cases, an automated system that could retrieve medical images based on user's interest. This chapter deals with various techniques, methodologies that correspond to the classification problem in data analysis process and its methodological impacts to big data.

Theoretical Concepts and Technical Aspects on Image Segmentation

Image segmentation is the process of partitioning a digital image into multiple segments (super pixels). Segmentation is typically used to locate objects and boundaries in images. The result of segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property. Adjacent regions are significantly different with respect to the same characteristics. A predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image is defined. An important characteristic of the method is its ability to preserve detail in low-variability image regions and ignoring detail in high variability regions. This chapter discuss basic aspects of segmentation and an application and presents a detailed assessment on different methods in image segmentation and discusses a case study on it.

Face Recognition in Adverse Conditions

In this chapter, the authors discuss the main outcomes from both the most recent literature and the research activities summarized in this book. Of course, a complete review is not possible. It is evident that each issue related to face recognition in adverse conditions can be considered as a research topic in itself and would deserve a detailed survey of its own. However, it is interesting to provide a compass to orient one in the presently achieved results in order to identify open problems and promising research lines. In particular, the final chapter provides more detailed considerations about possible future developments.

Early Recognition of Suspicious Activity for Crime Prevention

Automatic identification and early prediction of suspicious human activities are of significant importance in video surveillance research. By recognizing and predicting a criminal activity at an early stage, regrettable incidents can be avoided. Initially, an action recognition framework is developed for identifying the suspicious actions using interest point based 2D and 3D features and transform based approaches. This is subsequently followed by a novel approach for predicting the suspicious actions for crime prevention in real-world scenario. The prediction problem is formulated probabilistically and a novel approach that employs the mixture models for prediction is introduced. The developed system yields promising results for predicting the actions in real-time.

Tradeoffs Between Forensics and Anti-Forensics of Digital Images

Over past few years, we are the spectators of the evolution in the field of information technology, telecommunication and networking. Due to the advancement of smart phones, easy and inexpensive access to the internet and popularity of social networking, capture and use of digital images has increased drastically. Image processing techniques are getting developed at rapidly and at the same time easy to use image tampering soft-wares are also getting readily available. If tampered images are misused, big troubles having deep moral, ethical and lawful allegations may arise. Due to high potential of visual media and the ease in their capture, distribution and storage, we rarely find a field where digital visual data is not used. The value of image as evidence of event must be carefully assessed and it is a call for from different fields of applications. Therefore, in this age of fantasy, image authentication has become an issue of utmost importance.

Human Face Region Detection Driving Activity Recognition in Video

Automatic recognition of human actions from video signals is probably one of the most salient research topics of computer vision with a tremendous impact for many applications. In this chapter, the authors introduce a new descriptor, the Human Constrained Pixel Change History (HC-PCH), which is based on PCH but focuses on the human body movements over time. They propose a modification of the conventional PCH that entails the calculation of two probabilistic maps based on human face and body detection, respectively. These HC-PCH features are used as input to an HMM-based classification framework, which exploits redundant information from multiple streams by employing sophisticated fusion methods, resulting in enhanced activity recognition rates.

Recognizing Human Actions in Basketball Video Sequences on the Basis of Global and Local Pairwise Representation

A feature-representation method for recognizing actions in sports videos on the basis of the relationship between human actions and camera motions is proposed. The method involves the following steps: First, keypoint trajectories are extracted as motion features in spatio-temporal sub-regions called “spatio-temporal multiscale bags” (STMBs). Global representations and local representations from one sub-region in the STMBs are then combined to create a “glocal pairwise representation” (GPR). The GPR considers the co-occurrence of camera motions and human actions. Finally, two-stage SVM classifiers are trained with STMB-based GPRs, and specified human actions in video sequences are identified. An experimental evaluation of the recognition accuracy of the proposed method (by using the public OSUPEL basketball video dataset and broadcast videos) demonstrated that the method can robustly detect specific human actions in both public and broadcast basketball video sequences.

Categorization of Plant and Insect Species via Shape Analysis

This chapter presents a graph-based approach to automatically categorize plant and insect species. In this approach, the plant leaf and insect objects are segmented from the background semi-automatically. For each object, the contour is then extracted, so that the contour points are used to form the vertices of a graph. We propose a vectorization method to recover clique histogram vectors from the graphs for classification. The clique histogram represents the distribution of one vertex with respect to its adjacent vertices. This treatment permits the use of a codebook approach to represent the graph in terms of a set of codewords that can be used for purposes of support vector machine classification. The experimental results show that the method is not only effective but also robust, and comparable with other methods in the literature for species recognition.