Project Object Recognition Using Deep Learning - 1478 Words

1. INTRODUCTION OF THE PROJECT: The title of the project is Object Recognition using Deep Learning. The advent of Deep Learning is motivated as a branch of machine learning that is advancing the state of the art for perceptual problems like vision and speech recognition. We can pose these tasks as mapping concrete inputs such as image pixels or audio waveforms to abstract outputs like the identity of a face or a spoken word. The â€Å"depth† of deep learning models comes from composing functions into a series of transformations from input, through intermediate representations, and on to output. The overall composition gives a deep, layered model, in which each layer encodes progress from low-level details to high-level concepts. This yields a rich, hierarchical representation of the perceptual problem. 2. REVIEW OF LITERATURE: Over the last two years, a sequence of results on benchmark visual recognition tasks has demonstrated that convolutional neural networks (CNNs) [3] will likely replace engineered features, such as SIFT and HOG, for a wide variety of problems. This sequence started with the breakthrough ImageNet classification results reported by Krizhevsky et al. [10]. Soon after, Donahue et al. [6] showed that the same network, trained for ImageNet classification, was an effective blackbox feature extractor. Using CNN features, they reported state-of-the-art results on several standard image classification datasets. At the same time, Girshick et al. [8] showed howShow MoreRelatedAn Effective Machine Learning Model1164 Words   |  5 PagesObject recognition is one of the most frontier and potentially revolutionary technologies in computer science and a central research topic in computer vision. Currently there are increasing number of researches targeting to give the meaning of our vision to computers. As we move deeper in understanding the image completely, having more exact and detailed object recognition becomes crucial. In this context, one cares not only about classifying images, but also about precisely estimating the classRead MoreQuestions On Augmented Reality Companion And Project Tango Kit Using Deep Learning Algorithm For Step Growth 1628 Words   |  7 PagesCASH PLAN OF PROJECT ïÆ'Ëœ BENEFITS ïÆ'Ëœ REFERENCES SYNOPSIS PROJECT TITLE: Augmented Reality Companion (Virtualbot) for Microsoft HoloLens and Project Tango Kit using Deep Learning Algorithm for Step Growth IN THIS PROPOSAL WE WOULD LIKE TO A VIRTUAL EXPERIENCE OF HUMAN INTERFERENCE TO REAL WORLD WITHOUT HARMING ANY CREATURE.AIM OF THIS PROJECT IS TO INTRODUCE ADVANCED TECHNOLOGY CALLEND VIRTUAL REALITY INTO PICTURE THROUGH HOLOLENSE AND GOOGLE TANGO PROJECT . 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Friedrich Frà ¶bel (1782 – 1852) created the concept of kindergarten through his recognition of children’s unique needs and capabilitiesRead MoreExample Of Hyperpectral Image Classification1730 Words   |  7 Pagesbased on dimensional reduction studies, Principal Component Analysis (PCA), Random Projection (RP) that can project the data matrix into another space which is lower dimensional rather than original space [18]. Structurally, in these methods we lose the structural information about original features and thus the projected features are new and often over-complete to describe certain objects. However, in hyperspectral data-which contains two specific materials; spectral spatial - the structure ofRead MoreImprovement Of K Means Clustering Algorithm1431 Words   |  6 PagesKopnar Department of Computer Engineering Pimpri Chinchwad College Of Engineering Savitribai Phule Pune University Pune, India mkopnar@gmail.com Abstract— The set of objects having same characteristics are organized in groups and clusters of these objects are formed known as Data Clustering.It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters.In this algorithm, n number of data points are dividedRead MoreI Am At The Final Phase Of Wrapping Up My Thesis1424 Words   |  6 Pageswould like to take the opportunity to share with you how I see my future contributions to Kindred. AI needs RL ideas (i.e., ideas regarding actions, long-term predictions, and decision-making), perhaps much more than it needed deep learning ideas (i.e., ideas regarding recognition but disregarding agent s behavior and its long-term consequences). There is a serious shortage of RL experts. We should take advantage of that by utilizing as much RL expertise as possible from our in-house experts and outsideRead MoreAnalyzing Cultural Relevance in Instruction1369 Words   |  6 PagesAnalyzing Cultural Relevance in instruction Rita Wraggs EDU 692 Creativity, Culture, and Global Contexts in Education Decision Making Instructor Gregory Sherman January 26, 2015 Description Everyone has their own perspective of what best way and how to learning children. The perspective of the student Jeff Bliss was tired and angry about the teacher going off on her student so he became the voice of his classmates. His stand up and speak out about the teacher Julie Phung lack of teaching her student byRead MoreThe Development Of Mass Spectra On Organic Chemical Computers821 Words   |  4 Pages(https://aitopics.org/misc/brief-history) In 1970, an interactive program for computer-aided instruction named Scholar was developed by Jaime Carbonell, â€Å"the basic methodology of Scholar was a tutorial dialogue using templates and keyword recognition.† (http://people.ischool.illinois.edu/~chip/projects/timeline/1969an.html) A year after the development of Scholar, Terry Winograd designed a robot named SHRDLU that demonstrated the ability for a machine to understand simple sentences in English that were