Download or read book Context driven Object Detection and Segmentation with Auxiliary Information written by Tao Wang and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One fundamental problem in computer vision and robotics is to localize objects of interest in an image. The task can either be formulated as an object detection problem if the objects are described by a set of pose parameters, or an object segmentation one if we recover object boundary precisely. A key issue in object detection and segmentation concerns exploiting the spatial context, as local evidence is often insufficient to determine object pose in the presence of heavy occlusions or large object appearance variations. This thesis addresses the object detection and segmentation problem in such adverse conditions with auxiliary depth data provided by RGBD cameras. We focus on four main issues in context-aware object detection and segmentation: 1) what are the effective context representations? 2) how can we work with limited and imperfect depth data? 3) how to design depth-aware features and integrate depth cues into conventional visual inference tasks? 4) how to make use of unlabeled data to relax the labeling requirements for training data? We discuss three object detection and segmentation scenarios based on varying amounts of available auxiliary information. In the first case, depth data are available for model training but not available for testing. We propose a structured Hough voting method for detecting objects with heavy occlusion in indoor environments, in which we extend the Hough hypothesis space to include both the object's location, and its visibility pattern. We design a new score function that accumulates votes for object detection and occlusion prediction. In addition, we explore the correlation between objects and their environment, building a depth-encoded object-context model based on RGBD data. In the second case, we address the problem of localizing glass objects with noisy and incomplete depth data. Our method integrates the intensity and depth information from a single view point, and builds a Markov Random Field that predicts glass boundary and region jointly. In addition, we propose a nonparametric, data-driven label transfer scheme for local glass boundary estimation. A weighted voting scheme based on a joint feature manifold is adopted to integrate depth and appearance cues, and we learn a distance metric on the depth-encoded feature manifold. In the third case, we make use of unlabeled data to relax the annotation requirements for object detection and segmentation, and propose a novel data-dependent margin distribution learning criterion for boosting, which utilizes the intrinsic geometric structure of datasets. One key aspect of this method is that it can seamlessly incorporate unlabeled data by including a graph Laplacian regularizer. We demonstrate the performance of our models and compare with baseline methods on several real-world object detection and segmentation tasks, including indoor object detection, glass object segmentation and foreground segmentation in video.

Download or read book Improving Object Detection and Segmentation by Utilizing Context written by Subarna Tripathi and published by . This book was released on 2018 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection and segmentation are important computer vision problems that have applications in several domains such as autonomous driving, virtual and augmented reality systems, human-computer interaction etc. In this dissertation, we study how to improve object detection and segmentation by utilizing different contexts. Context refers to one of many application scenarios such as (i) video frames for consistent prediction over time, (ii) specific domain knowledge such as human keypoints for person segmentation, and (iii) implementation context aiming for efficiency in embedded systems. Temporal Context of Videos: Video data understanding has drawn considerable interest in recent times as a result of access to huge amount of video data and success in image-based models for visual tasks. However, motion blur, compression artifacts cause apparently consistent video signals to produce high temporal variation on frame-level output for vision tasks such as object detection or semantic segmentation. We study and propose efficient early, and high-level visual processing algorithms by leveraging video content in a streaming fashion. We show how to fuse motion and color to achieve improved streaming hierarchical supervoxels. As a high-level visual task, we propose consistent and efficient video object detection using Convolutional Neural Network (CNN) by clustering video object proposals and propagating object class labels through the videos. Next, we propose an end-to-end framework for learning video object detection through Recurrent Neural Network (RNN) by posing video as a time series. We also present a post-processing framework for improving semantic segmentation in videos. Domain Knowledge Context for Segmentation: Person instance segmentation is a promising research frontier for a range of applications such as human-robot interaction, sports performance analysis, and action recognition. Human keypoints are a well-studied representation of people. We explore how to use keypoint models to improve instance-level person segmentation in constrained and unconstrained environments with or without training. Efficiency Context for Embedded Implementation: To make an object detector system amenable for embedded implementation, we propose a low-complexity fully convolutional neural network. Additionally, we employ 8-bit quantization on the learned weights. As a mobile use case, we choose face detection. The results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3x and memory-BW by 3-4x comparing with its strongest baseline.

Download or read book Toward Category Level Object Recognition written by Jean Ponce and published by Springer. This book was released on 2007-01-25 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation.

Download or read book Visual Object Tracking from Correlation Filter to Deep Learning written by Weiwei Xing and published by Springer Nature. This book was released on 2021-11-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

Download or read book How Humans Recognize Objects Segmentation Categorization and Individual Identification written by Chris Fields and published by Frontiers Media SA. This book was released on 2016-08-18 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human beings experience a world of objects: bounded entities that occupy space and persist through time. Our actions are directed toward objects, and our language describes objects. We categorize objects into kinds that have different typical properties and behaviors. We regard some kinds of objects – each other, for example – as animate agents capable of independent experience and action, while we regard other kinds of objects as inert. We re-identify objects, immediately and without conscious deliberation, after days or even years of non-observation, and often following changes in the features, locations, or contexts of the objects being re-identified. Comparative, developmental and adult observations using a variety of approaches and methods have yielded a detailed understanding of object detection and recognition by the visual system and an advancing understanding of haptic and auditory information processing. Many fundamental questions, however, remain unanswered. What, for example, physically constitutes an “object”? How do specific, classically-characterizable object boundaries emerge from the physical dynamics described by quantum theory, and can this emergence process be described independently of any assumptions regarding the perceptual capabilities of observers? How are visual motion and feature information combined to create object information? How are the object trajectories that indicate persistence to human observers implemented, and how are these trajectory representations bound to feature representations? How, for example, are point-light walkers recognized as single objects? How are conflicts between trajectory-driven and feature-driven identifications of objects resolved, for example in multiple-object tracking situations? Are there separate “what” and “where” processing streams for haptic and auditory perception? Are there haptic and/or auditory equivalents of the visual object file? Are there equivalents of the visual object token? How are object-identification conflicts between different perceptual systems resolved? Is the common assumption that “persistent object” is a fundamental innate category justified? How does the ability to identify and categorize objects relate to the ability to name and describe them using language? How are features that an individual object had in the past but does not have currently represented? How are categorical constraints on how objects move or act represented, and how do such constraints influence categorization and the re-identification of individuals? How do human beings re-identify objects, including each other, as persistent individuals across changes in location, context and features, even after gaps in observation lasting months or years? How do human capabilities for object categorization and re-identification over time relate to those of other species, and how do human infants develop these capabilities? What can modeling approaches such as cognitive robotics tell us about the answers to these questions? Primary research reports, reviews, and hypothesis and theory papers addressing questions relevant to the understanding of perceptual object segmentation, categorization and individual identification at any scale and from any experimental or modeling perspective are solicited for this Research Topic. Papers that review particular sets of issues from multiple disciplinary perspectives or that advance integrative hypotheses or models that take data from multiple experimental approaches into account are especially encouraged.

Download or read book Object Detection and Recognition in Digital Images written by Boguslaw Cyganek and published by John Wiley & Sons. This book was released on 2013-05-20 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications. Places an emphasis on tensor and statistical based approaches within object detection and recognition. Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods. Contains numerous case study examples of mainly automotive applications. Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.

Download or read book Deep Structured Models for Large Scale Object Co detection and Segmentation written by Zeeshan Hayder and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structured decisions are often required for a large variety of image and scene understanding tasks in computer vision, with few of them being object detection, localization, semantic segmentation and many more. Structured prediction deals with learning inherent structure by incorporating contextual information from several images and multiple tasks. However, it is very challenging when dealing with large scale image datasets where performance is limited by high computational costs and expressive power of the underlying representation learning techniques. In this thesis, we present efficient and effective deep structured models for context-aware object detection, co-localization and instance-level semantic segmentation. First, we introduce a principled formulation for object co-detection using a fully-connected conditional random field (CRF). We build an explicit graph whose vertices represent object candidates (instead of pixel values) and edges encode the object similarity via simple, yet effective pairwise potentials. More specifically, we design a weighted mixture of Gaussian kernels for class-specific object similarity, and formulate kernel weights estimation as a least-squares regression problem. Its solution can therefore be obtained in closed-form. Furthermore, in contrast with traditional co-detection approaches, it has been shown that inference in such fully-connected CRFs can be performed efficiently using an approximate mean-field method with high-dimensional Gaussian filtering. This lets us effectively leverage information in multiple images. Next, we extend our class-specific co-detection framework to multiple object categories. We model object candidates with rich, high-dimensional features learned using a deep convolutional neural network. In particular, our max-margin and directloss structural boosting algorithms enable us to learn the most suitable features that best encode pairwise similarity relationships within our CRF framework. Furthermore, it guarantees that the time and space complexity is O(n t) where n is the total number of candidate boxes in the pool and t the number of mean-field iterations. Moreover, our experiments evidence the importance of learning rich similarity measures to account for the contextual relations across object classes and instances. However, all these methods are based on precomputed object candidates (or proposals), thus localization performance is limited by the quality of bounding-boxes. To address this, we present an efficient object proposal co-generation technique that leverages the collective power of multiple images. In particular, we design a deep neural network layer that takes unary and pairwise features as input, builds a fully-connected CRF and produces mean-field marginals as output. It also lets us backpropagate the gradient through entire network by unrolling the iterations of CRF inference. Furthermore, this layer simplifies the end-to-end learning, thus effectively benefiting from multiple candidates to co-generate high-quality object proposals. Finally, we develop a multi-task strategy to jointly learn object detection, localization and instance-level semantic segmentation in a single network. In particular, we introduce a novel representation based on the distance transform of the object masks. To this end, we design a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. We show that the predicted masks can go beyond the scope of the bounding boxes and that the multiple tasks can benefit from each other. In summary, in this thesis, we exploit the joint power of multiple images as well as multiple tasks to improve generalization performance of structured learning. Our novel deep structured models, similarity learning techniques and residual-deconvolution architecture can be used to make accurate and reliable inference for key vision tasks. Furthermore, our quantitative and qualitative experiments on large scale challenging image datasets demonstrate the superiority of the proposed approaches over the state-of-the-art methods.

Download or read book Data Driven Low level Object Detection and Segmentation written by Guoyi Fu and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Object Detection by Stereo Vision Images written by R. Arokia Priya and published by John Wiley & Sons. This book was released on 2022-08-25 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: OBJECT DETECTION BY STEREO VISION IMAGES Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers. Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, machine learning, stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems. Audience Researchers in information technology looking at robotics, deep learning, machine learning, big data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

Download or read book Region Based Convolutional Neural Networks for Object Detection and Recognition in ADAS Application written by Sachit Kaul and published by . This book was released on 2017 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection and Recognition using Computer Vision has been a very interesting and a challenging field of study from past three decades. Recent advancements in Deep Learning and as well as increase in computational power has reignited the interest of researchers in this field in last decade. Implementing Machine Learning and Computer Vision techniques in scene classification and object localization particularly for automated driving purpose has been a topic of discussion in last half decade and we have seen some brilliant advancements in recent times as self-driving cars are becoming a reality. In this thesis we focus on Region based Convolutional Neural Networks (R-CNN) for object recognition and localizing for enabling Automated Driving Assistance Systems (ADAS). R-CNN combines two ideas: (1) one can apply high-capacity Convolutional Networks (CNN) to bottom-up region proposals in order to localize and segment objects and (2) when labelling data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific-finetuning, boosts performance significantly. In this thesis, inspired by the RCNN framework we describe an object detection and segmentation system that uses a multilayer convolutional network which computes highly discriminative, yet invariant features to classify image regions and outputs those regions as detected bounding boxes for specifically a driving scenario to detect objects which are generally on road such as traffic signs, cars, pedestrians etc. We also discuss different types of region based convolutional networks such as RCNN, Fast RCNN and Faster RCNN, describe their architecture and perform a time study to determine which of them leads to real-time object detection for a driving scenario when implemented on a regular PC architecture. Further we discuss how we can use such R-CNN for determining the distance of objects on road such as Cars, Traffic Signs, Pedestrians from a sensor (camera) mounted on the vehicle which shows how Computer Vision and Machine Learning techniques are useful in automated braking systems (ABS) and in perception algorithms such as Simultaneous Localization and Mapping (SLAM).

Download or read book Learning to Generate and Refine Object Proposals written by Haoyang Zhang and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual object recognition is a fundamental and challenging problem in computer vision. To build a practical recognition system, one is first confronted with high computation complexity due to an enormous search space from an image, which is caused by large variations in object appearance, pose and mutual occlusion, as well as other environmental factors. To reduce the search complexity, a moderate set of image regions that are likely to contain an object, regardless of its category, are usually first generated in modern object recognition subsystems. These possible object regions are called object proposals, object hypotheses or object candidates, which can be used for down-stream classification or global reasoning in many different vision tasks like object detection, segmentation and tracking, etc. This thesis addresses the problem of object proposal generation, including bounding box and segment proposal generation, in real-world scenarios. In particular, we investigate the representation learning in object proposal generation with 3D cues and contextual information, aiming to propose higher-quality object candidates which have higher object recall, better boundary coverage and lower number. We focus on three main issues: 1) how can we incorporate additional geometric and high-level semantic context information into the proposal generation for stereo images? 2) how do we generate object segment proposals for stereo images with learning representations and learning grouping process? and 3) how can we learn a context-driven representation to refine segment proposals efficiently? In this thesis, we propose a series of solutions to address each of the raised problems. We first propose a semantic context and depth-aware object proposal generation method. We design a set of new cues to encode the objectness, and then train an efficient random forest classifier to re-rank the initial proposals and linear regressors to fine-tune their locations. Next, we extend the task to the segment proposal generation in the same setting and develop a learning-based segment proposal generation method for stereo images. Our method makes use of learned deep features and designed geometric features to represent a region and learns a similarity network to guide the superpixel grouping process. We also learn a ranking network to predict the objectness score for each segment proposal. To address the third problem, we take a transformation-based approach to improve the quality of a given segment candidate pool based on context information. We propose an efficient deep network that learns affine transformations to warp an initial object mask towards nearby object region, based on a novel feature pooling strategy. Finally, we extend our affine warping approach to address the object-mask alignment problem and particularly the problem of refining a set of segment proposals. We design an end-to-end deep spatial transformer network that learns free-form deformations (FFDs) to non-rigidly warp the shape mask towards the ground truth, based on a multi-level dual mask feature pooling strategy. We evaluate all our approaches on several publicly available object recognition datasets and show superior performance.

Download or read book VOCUS A Visual Attention System for Object Detection and Goal Directed Search written by Simone Frintrop and published by Springer. This book was released on 2006-03-28 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a complete computational system for visual attention and object detection. VOCUS (Visual Object detection with a Computational attention System) represents a major step forward on integrating data-driven and model-driven information into a single framework. Additionally, the volume contains an extensive review of the literature on visual attention, detailed evaluations of VOCUS in different settings, and applications of the system.

Download or read book Deep Learning in Object Recognition Detection and Segmentation written by Xiaogang Wang and published by . This book was released on 2016 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.

Download or read book Shape Based Object Detection and Recognition in Silhouettes and Real Images written by Xingwei Yang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer and Information Science

Download or read book Visual Object Recognition written by Kristen Thielscher and published by Springer Nature. This book was released on 2022-05-31 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

Download or read book Image Segmentation and Contextual Modeling for Object Recognition written by Andrew Rabinovich and published by . This book was released on 2008 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognizing objects is an essential part of navigating through the visual world. Identifying objects and finding boundaries between them provides us with some of the richest sensory information. Similarly, image segmentation and object recognition are among the most fundamental problems in computer vision and machine intelligence. The potential interaction between these processes has been discussed for many years. The usefulness of recognition for segmentation was demonstrated with various top-down segmentation algorithms; however, the impact of bottom-up image segmentation for object recognition is not well understood. One impeding factor is the unsatisfactory quality of image segmentation algorithms. In this work, we take advantage of a recently proposed method for computing multiple stable segmentations and illustrate the application of bottom-up image segmentation as a preprocessing step for object recognition. In parallel to segmentation, the task of visual object recognition is often greatly facilitated by the objects' surroundings. Contextual information can play the very important role of reducing ambiguity in objects' visual appearance. In this dissertation, we propose a new model for object recognition that incorporates two types of context -- co-occurrence and relative location -- with local appearance-based features, thus named CoLA (for Co-occurrence, Location and Appearance). Since a number of contextual models for recognition have been proposed in the recent history, it is necessary to compare the newly proposed model to the existing ones. Over the years, two general kinds of such models have emerged: those with contextual inference based on the statistical summary of the scene, and models representing the context in terms of relationships among objects in the image. Understanding the theoretical and practical properties of such approaches is essential in designing object recognition systems. We provide an analytical analysis of these models and evaluate them empirically.

Download or read book Advancement of Deep Learning and its Applications in Object Detection and Recognition written by Roohie Naaz Mir and published by CRC Press. This book was released on 2023-05-10 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.