Project : Object Recognition Using Deep Learning

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

The training data contained both labeled data D_la={〖x_i,y_i}〗_(i=1)^kl and unlabeled data D_un= {〖x_j}〗_(j=kl+1)^(kl+u) where x_(i ) is the feature descriptor of image I and y_i={1,…,k} is its label .k is the number of categories. l is the number of labeled data in each category, and u is the number of unlabeled data. Our method aims to learn a high-level image representation S by exploiting the few labeled data D_land great quantities of unlabeled ones, which is then fed into different classifiers to obtain final classification results. The procedure of semisupervised feature learning by SSEP is shown in Fig. 1. First, a new sampling algorithm based on GNA [19] is proposed to produce T WT sets P^t={(〖s_i^t,c_i^t)}〗_(i=1)^kp , t ∈{1,…..,T}

The features trained for one task can be useful for related tasks. The MTL leverages this idea in a systematic manner. Models for all task of interest (POS,CHUNK,NER) are jointly trained.All models share the look-up table , first linear layer parameters along with certain few parameters..MTL could produce a single unified architecture that performs well for all tasks, no significant improvements(only marginal) were observed with this approach when compared to training separate architectures per task.[ http://arxiv.org/pdf/1103.0398v1.pdf

Dr. Ahrendt noted the huge advancements that have been made over the last decade, but made sure to note that the math behind AI and machine learning is quite old mathematics. “Now that we can compute things so quickly… we can see the bloom of AI and machine learning.”

Computers will soon know us better than we know ourselves. A recent study made by Quartz Media published findings that revealed the average American stares at a screen for about 7.5 hours a day. The more we interact with someone or something for that matter, the more they will know of us. As we head into an age of technological advancement, artificial intelligent is facilitating the devices to become our best friends. In Jeremy Howard’s Ted Talk, The wonderful and terrifying implications of computers that can learn, he explains the complicated algorithm that technology has with tracking and remembering your every click. We have the misconception that we can do everything that computers can, however, with software’s like “deep learning that

View-point dependence states that the way one recognized an object depends upon its viewpoint. It is more focused on remembering the image as a whole and the different ways it was viewed. It also states that how quickly or accurately one can recognize an object depends on its orientation, this point greatly differing with view-point invariance. This paper supports the statement, “object recognition is view-point dependent” and uses additional research to support it.

Even though we had a lot of success in our study, we ran into several limitations. Our first limitation was our choice of software. We chose KNIME as our software because anyone can access it without any real difficulty, and it is free and downloadable on any computer. With that being said, we first ran into the issue of having too much data. The software was incapable and too slow to use the majority of the data we had. The other limitation we had with KNIME was being unable to run our initial deep learning model, which was already discussed in the previous

The ultimate goal for a system of visual perception is representing visual scenes. It is generally assumed that this requires an initial ‘break-down’ of complex visual stimuli into some kind of “discrete subunits” (De Valois & De Valois, 1980, p.316) which can then be passed on and further processed by the brain. The task thus arises of identifying these subunits as well as the means by which the visual system interprets and processes sensory input. An approach to visual scene analysis that prevailed for many years was that of individual cortical cells being ‘feature detectors’ with particular response-criteria. Though not self-proclaimed, Hubel and Wiesel’s theory of a hierarchical visual system employs a form of such feature detectors. I

For more than thirty years, reaserchers have been working on handwritten recognition. Over the past few years, the number of companies involved in research on handwritten recognition has continually increased. The advance of handwritten processing results from a combination of various elements,

Ganetec produces facial recognition systems for business, government, and personal security, however, the systems are capable of not only recognizing an unknown person’s identity but tracking position, and patterns. Ganetec stores all video and picture data for future reference and efficiency of the system, which can be used for analysis of individuals’ decisions. Therefore Ganetec’s servers withhold large quantities of personal information, which may be used for criminal investigations, tracking, or invasion of

Within their jointly authored case study entitled Deep Smarts, Harvard professors Dorothy Leonard and Walter Swap seek to quantify the intangible set of intellectual abstractions which combine to form the foundation of extreme competence in an employee. Citing that rare but harmonious connection between "raw brainpower" and "emotional intelligence," Leonard and Swap posit the existence of "deep smarts," which they define as "the stuff that produces that mysterious quality, good judgment" (2004). Seemingly an amalgamation of human attributes which aid in decision making and critical judgment, deep smarts would appear to be the highly functioning union of intuition, instinct, intelligence and insight. The authors base their conception of deep smarts on the value of firsthand experience, by noting repeatedly that deep smarts are "based more on know-how than on facts; comprising a system view as well as expertise in individual areas" (Leonard & Swap, 2004). According to the authors, most corporations and large-scale organizations have members, from executives to temporary employees, who possess deep smarts through the "judgment and knowledge - both explicit and tacit - stored in their heads and hands" (Leonard and Swap, 2004), but these valuable assets are routinely overlooked and underutilized. The purpose of the case study is to present managers, and others responsible for maximizing an organization's efficiency, with viable methods for identifying those employees with deep

In this article, one of the grand challenges – memories for life is discussed. When the topic “memories for life” was first mentioned? Who mentioned it? What was the module for that challenge? What fields are related to this grand challenge? In this article, I pick out seven areas from computer science to talk about the questions and problems, and if available, what should we do in the future. Of course, that’s not complete solution, but we can discuss what effect should be done now or in the future. Then, I offer an example about OpenCV to illustrate what we can do on object identification and image processing in computer vision field based on today’s computing technologies. However, if we want to make challenges into real life, we still have too much research and study to do.

for the entire input, or per point segment labels for each point of the input. PointNet

 [PDF]Case Study: Transport Corporation of India Limitedsiteresources.worldbank.org/.../t...পাতাটিকে অনুবাদ করে দেখাও(TCI), as a major cargo transport company, recognized the importance ... The information in the TCI case study is based on personal interviews with TCI Foun- .... cess to medical records, it also supports analysis providing useful insights.