MARC Record from marc_columbia

LEADER: 05601cam a2200625Ii 4500
001 16066627
005 20220402224425.0
006 m o d
007 cr cnu---unuuu
008 201031s2020 xx o 000 0 eng d
035 $a(OCoLC)on1202456840
035 $a(NNC)16066627
040 $aEBLCP$beng$cEBLCP$dUKAHL$dUKMGB$dOCLCO$dYDX$dOCLCF$dN$T$dEBLCP$dVLB$dOCLCO
015 $aGBC0H4672$2bnb
016 7 $a020001993$2Uk
019 $a1202224810$a1202478593
020 $a1800201931
020 $a9781800201934$q(electronic bk.)
020 $z9781800203587 (pbk.)
035 $a(OCoLC)1202456840$z(OCoLC)1202224810$z(OCoLC)1202478593
037 $a9781800201934$bPackt Publishing
050 4 $aTL152.8$b.V46 2020eb
082 04 $a629.046028637$223
049 $aZCUA
100 1 $aVenturi, Luca.
245 10 $aHands-On Vision and Behavior for Self-Driving Cars$h[electronic resource] :$bExplore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4.
260 $aBirmingham :$bPackt Publishing, Limited,$c2020.
300 $a1 online resource (374 p.)
336 $atext$2rdacontent
337 $acomputer$2rdamedia
338 $aonline resource$2rdacarrier
500 $aDescription based upon print version of record.
520 $aThis book will give you insights into the technologies that drive the autonomous car revolution. To get started, all you need is basic knowledge of computer vision and Python.
505 0 $aCover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: OpenCV and Sensors and Signals -- Chapter 1: OpenCV Basics and Camera Calibration -- Technical requirements -- Introduction to OpenCV and NumPy -- OpenCV and NumPy -- Image size -- Grayscale images -- RGB images -- Working with image files -- Working with video files -- Working with webcams -- Manipulating images -- Flipping an image -- Blurring an image -- Changing contrast, brightness, and gamma -- Drawing rectangles and text -- Pedestrian detection using HOG -- Sliding window
505 8 $aUsing HOG with OpenCV -- Introduction to the camera -- Camera terminology -- The components of a camera -- Considerations for choosing a camera -- Strengths and weaknesses of cameras -- Camera calibration with OpenCV -- Distortion detection -- Calibration -- Summary -- Questions -- Chapter 2: Understanding and Working with Signals -- Technical requirements -- Understanding signal types -- Analog versus digital -- Serial versus parallel -- Universal Asynchronous Receive and Transmit (UART) -- Differential versus single-ended -- I2C -- SPI -- Framed-based serial protocols -- Understanding CAN
505 8 $aEthernet and internet protocols -- Understanding UDP -- Understanding TCP -- Summary -- Questions -- Further reading -- Open source protocol tools -- Chapter 3: Lane Detection -- Technical requirements -- How to perform thresholding -- How thresholding works on different color spaces -- RGB/BGR -- HLS -- HSV -- LAB -- YCbCr -- Our choice -- Perspective correction -- Edge detection -- Interpolated threshold -- Combined threshold -- Finding the lanes using histograms -- The sliding window algorithm -- Initialization -- Coordinates of the sliding windows -- Polynomial fitting -- Enhancing a video
505 8 $aPartial histogram -- Rolling average -- Summary -- Questions -- Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks -- Chapter 4: Deep Learning with Neural Networks -- Technical requirements -- Understanding machine learning and neural networks -- Neural networks -- Neurons -- Parameters -- The success of deep learning -- Learning about convolutional neural networks -- Convolutions -- Why are convolutions so great? -- Getting started with Keras and TensorFlow -- Requirements -- Detecting MNIST handwritten digits -- What did we just load?
505 8 $aTraining samples and labels -- One-hot encoding -- Training and testing datasets -- Defining the model of the neural network -- LeNet -- The code -- The architecture -- Training a neural network -- CIFAR-10 -- Summary -- Questions -- Further reading -- Chapter 5: Deep Learning Workflow -- Technical requirements -- Obtaining the dataset -- Datasets in the Keras module -- Existing datasets -- Your custom dataset -- Understanding the three datasets -- Splitting the dataset -- Understanding classifiers -- Creating a real-world dataset -- Data augmentation -- The model -- Tuning convolutional layers
650 0 $aAutomated vehicles$xComputer programs.
650 0 $aComputer vision.
650 0 $aPython (Computer program language)
650 0 $aOpenCV (Computer program language)
650 6 $aVéhicules autonomes$xLogiciels.
650 6 $aVision par ordinateur.
650 6 $aPython (Langage de programmation)
650 6 $aOpenCV (Langage de programmation)
650 7 $aComputer vision.$2fast$0(OCoLC)fst00872687
650 7 $aOpenCV (Computer program language)$2fast$0(OCoLC)fst01938441
650 7 $aPython (Computer program language)$2fast$0(OCoLC)fst01084736
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aKorda, Krishtof.
776 08 $iPrint version:$aVenturi, Luca$tHands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4$dBirmingham : Packt Publishing, Limited,c2020$z9781800203587
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio16066627$zACADEMIC - Electronics & Semiconductors
852 8 $blweb$hEBOOKS