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Introduction   This blog introduces an interesting application of conditional generative adversarial network (cGAN) for face aging. That is, you can use this cGAN to synthesize the face images of one person at different ages. For research area, this method can be used to improve the performance of “cross-age facial recognition”. For daily application, except for entertainment, it can also be used for finding missing children. This blog...

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Machine vision is the incorporation of computer vision into industrial manufacturing processes, although it does differ substantially from computer vision. In general, computer vision revolves around image processing. Machine vision, on the other hand, uses digital input and output to manipulate mechanical components. Devices that depend on machine vision are often found at work in product inspection, where they often use digital cameras or other...

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There are many applications for machine learning, whether it be processing social media traffic and trying to surface actionable insights or targeting consumers based on past purchases.  In this article aimed at those interested in artificial intelligence, we look at 10 examples of machine vision in manufacturing which include the following: Predictive Maintenance Package Inspection Reading Barcodes Product and Components Assembly Defect Reduction 3d Vision...

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machine vision system acquires images of an object, and then uses computers to process, analyze and measure various characteristics of that object. The purpose may be to enhance the image to see characteristics undetected to the human eye, or to analyze image data for measurement purposes. The decisions made from this information are often related to quality. For example, production line parts can be qualified...

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This article will entail a framework for creating and discovering new bio-inspired algorithms both for machine learning and engineering purposes as well as looking at other potential fields that might offer solutions within a computational model. It is in a way a brainstorm for a possibility. As you might be familiar, Brain-inspired Computing applies to fields from Brain-inspired computer chips produced by companies like IBM(examples: EU-backed...

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Hi all, Hope you all are doing all. Today I will explain the face detection procedure used in opencv. Procedure : Step1: create cascaded classifier using the training algorithm provided by opencv and get the .xml file.   Step2: load the pre-made .xml file.   Step3: input frame from camera/ input image and convert it to grey scale image.   Step4: use opencv’s ‘CascadeClassifier:: detectMultiScale()’ function to detect faces of different sizes in the input image. Explanation of...

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What are computers for?   Historically, different answers to this question – that is, different visions of computing – have helped inspire and determine the computing systems humanity has ultimately built. Consider the early electronic computers. ENIAC, the world’s first general-purpose electronic computer, was commissioned to compute artillery firing tables for the United States Army. Other early computers were also used to solve numerical problems, such as...

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If the basic technical ideas behind Deep Learning are around for decades, why are they taking off today ?   The best thing to answer this question would be to show and explain you the picture below.     At the vertical axes of the diagram you can see the performance of an algorithm (e.g. it’s prediction accuracy) and at the horizontal axes you can see the amount of data...

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There may be a time, place, and appropriate use for OpenCV, but in the business of providing integration services involving machine vision software for industrial end-users, I have not yet found it.  There’s a lot to like about OpenCV – mainly the price which is “free”, and the breadth of variety of available algorithms.   But let’s consider some challenges.  There is no commercial support for...

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Today we will start our journey to the world of Artificial Intelligence(AI). We will learn the basic definition of Artificial Intelligence (AI), Machine Learning(ML), Deep Learning(DL), Natural Language Processing(NLP), Computer Vision and Image Processing. Later we will go deeper with the machine learning algorithms and how those algorithm works. This tutorial is for beginners, if you have an idea of AI skip this course and...

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