{"product_id":"139206","title":"Computer Vision and Deep Learning ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBvmFLJxq6rLVyTrVI52tEKRq7J.png?v=1765073883\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Computer Vision and Deep Learning \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/116755317\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLearn Computer Vision with 85 Python Programs Using OpenCV and TensorFlow\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e First! Learn computer vision through a balanced mix of theory and practice! Learn computer vision theory using classical and deep learning methods, and then see firsthand how to implement it through 85 Python program exercises.\u003cbr\u003e Second, a computer vision textbook focused on deep learning! While introducing computer vision with a focus on deep learning, it also covers image processing and classical computer vision, ensuring a thorough study of computer vision. \u003cbr\u003eThird, build your foundational knowledge with the [Online Appendix]! The online appendix provides basic Python, linear algebra, and probability theory, allowing you to quickly acquire the foundational knowledge needed to study computer vision.\u003cbr\u003e\u003cbr\u003e * This book was developed as a textbook for university lectures, so it does not provide answers to practice problems.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eChapter 01 Computer Vision that Mimics Human Vision\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 1.1 Human vision\u003cbr\u003e 1.2 Why Computer Vision?\u003cbr\u003e 1.3 Why is computer vision difficult?\u003cbr\u003e 1.4 History of Computer Vision\u003cbr\u003e 1.5 Computer Vision Experience Service\u003cbr\u003e 1.6 Building Computer Vision\u003cbr\u003e 1.7 Things to Read and See\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 02 Computer Vision with OpenCV\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 2.1 Introduction to OpenCV\u003cbr\u003e 2.2 Programming Kickoff\u003cbr\u003e 2.3 Making Good Use of Object-Oriented Programming\u003cbr\u003e [Program 2-1] Creating an object of the numpy.ndarray class type and applying member functions \u003cbr\u003e2.4 [Programming Example 1] Reading and Displaying an Image\u003cbr\u003e [Program 2-2] Reading a video file and displaying it in a window\u003cbr\u003e 2.5 [Programming Example 2] Converting Image Format and Reducing Size\u003cbr\u003e [Program 2-3] Converting an image to a grayscale image and reducing its size by half\u003cbr\u003e 2.6 [Programming Example 3] Reading Video from a Webcam\u003cbr\u003e [Program 2-4] Capturing Video with a Webcam\u003cbr\u003e [Program 2-5] Joining images collected from a video\u003cbr\u003e 2.7 [Programming Example 4] Creating a Graphics Function and User Interface\u003cbr\u003e [Program 2-6] Drawing shapes and writing text on the video\u003cbr\u003e [Program 2-7] Drawing a rectangle where you click with the mouse\u003cbr\u003e [Program 2-8] Drawing a rectangle by dragging the mouse\u003cbr\u003e 2.8 [Programming Example 5] Creating a Painting Function\u003cbr\u003e [Program 2-9] Painting with red and blue brushes\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 03 Image Processing\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 3.1 Digital Image Basics \u003cbr\u003e[Program 3-1] Displaying RGB color images by channel\u003cbr\u003e 3.2 Binary images\u003cbr\u003e [Program 3-2] Obtaining a Histogram from an Actual Image\u003cbr\u003e [Program 3-3] Binarization using the Ochu algorithm\u003cbr\u003e [Program 3-4] Applying Morphological Operations\u003cbr\u003e 3.3 Point Operations\u003cbr\u003e [Program 3-5] Experimenting with Gamma Correction\u003cbr\u003e [Program 3-6] Histogram Equalization\u003cbr\u003e 3.4 Domain Operations\u003cbr\u003e [Program 3-7] Applying Convolution (Gaussian Smoothing and Embossing)\u003cbr\u003e 3.5 Geometric Operations\u003cbr\u003e [Program 3-8] Geometrically transforming an image using interpolation\u003cbr\u003e 3.6 Time Efficiency of OpenCV\u003cbr\u003e [Program 3-9] Comparing the times of a function written directly and a function provided by OpenCV\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 04 Edges and Regions\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 4.1 Edge Detection\u003cbr\u003e [Program 4-1] Sobel Edge Detection (Using the Sobel Function)\u003cbr\u003e 4.2 Canny Edge\u003cbr\u003e [Program 4-2] Experimenting with Canny Edge\u003cbr\u003e 4.3 Straight line detection\u003cbr\u003e [Program 4-3] Finding Boundaries in an Edge Map \u003cbr\u003e[Program 4-4] Detecting Apples Using the Hough Transform\u003cbr\u003e 4.4 Area division\u003cbr\u003e [Program 4-5] Super-pixel segmentation of input images using the SLIC algorithm\u003cbr\u003e [Program 4-6] Segmenting a Region Using the Normalization Cut Algorithm\u003cbr\u003e 4.5 Interactive Segmentation\u003cbr\u003e [Program 4-7] Segmenting objects using GrabCut\u003cbr\u003e 4.6 Area Features\u003cbr\u003e [Program 4-8] Using a function to extract features from a binary region\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 05 Regional Features\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 5.1 Idea\u003cbr\u003e 5.2 Translation and rotation invariant local features\u003cbr\u003e [Program 5-1] Implementing Harris Feature Detection\u003cbr\u003e 5.3 Scale-invariant local features\u003cbr\u003e 5.4 SIFT\u003cbr\u003e [Program 5-2] SIFT Detection\u003cbr\u003e 5.5 Matching\u003cbr\u003e [Program 5-3] SIFT Matching Using the FLANN Library\u003cbr\u003e 5.6 Homography estimation\u003cbr\u003e [Program 5-4] Estimating Homography Using RANSAC\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 06 Vision Agent\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 6.1 Vision Agent as an Intelligent Agent\u003cbr\u003e 6.2 User Interface Using PyQt \u003cbr\u003e[Program 6-1] Creating a simple GUI with PyQt (making a beep sound when clicking a button)\u003cbr\u003e [Program 6-2] Attaching PyQt's GUI to OpenCV (Capturing and Saving Frames from a Video)\u003cbr\u003e 6.3 [Vision Agent 1] Orim\u003cbr\u003e [Program 6-3] Using GrabCut to Cut Out Objects of Interest\u003cbr\u003e 6.4 [Vision Agent 2] Notification of Protection Zones for the Vulnerable\u003cbr\u003e [Program 6-4] Implementing a Traffic Vulnerability Protection Zone Notification\u003cbr\u003e 6.5 [Vision Agent 3] Panoramic Video Production\u003cbr\u003e [Program 6-5] Creating a panoramic image by stitching together images collected from a video\u003cbr\u003e 6.6 [Vision Agent 4] Special Effects\u003cbr\u003e [Program 6-6] Applying special effects to photo and video\u003cbr\u003e [Program 6-7] Applying special effects to video footage\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 07 Deep Learning Vision\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 7.1 A major shift in methodology\u003cbr\u003e 7.2 Machine Learning Fundamentals\u003cbr\u003e 7.3 A Taste of Deep Learning Software\u003cbr\u003e [Program 7-1] Checking Data with TensorFlow\u003cbr\u003e 7.4 The Birth of Artificial Neural Networks\u003cbr\u003e 7.5 Deep Multilayer Perceptron\u003cbr\u003e 7.6 Learning Algorithms \u003cbr\u003e7.7 Implementing a Multilayer Perceptron\u003cbr\u003e [Program 7-2] Recognizing MNIST with a Multilayer Perceptron (SGD Optimizer)\u003cbr\u003e [Program 7-3] Recognizing MNIST with a Multilayer Perceptron (Adam Optimizer)\u003cbr\u003e [Program 7-4] Recognizing MNIST with a Multilayer Perceptron (Comparing SGD and Adam Performance Graphs)\u003cbr\u003e [Program 7-5] Recognizing MNIST with a Deep Multilayer Perceptron\u003cbr\u003e [Program 7-6] Recognizing CIFAR-10 with a Deep Multilayer Perceptron\u003cbr\u003e 7.8 [Vision Agent 5] Zip Code Recognizer v.1\u003cbr\u003e [Program 7-7] Implementing the Postal Code Recognition Tool v.1 (DMLP Version)\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 08 Convolutional Neural Networks\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 8.1 Ideation and Development\u003cbr\u003e 8.2 Structure of a convolutional neural network\u003cbr\u003e 8.3 Training Convolutional Neural Networks\u003cbr\u003e 8.4 Convolutional Neural Network Implementation\u003cbr\u003e [Program 8-1] Recognizing MNIST with LeNet-5\u003cbr\u003e [Program 8-2] Recognizing Natural Images with Convolutional Neural Networks\u003cbr\u003e 8.5 [Vision Agent 6] Zip Code Recognizer v.2\u003cbr\u003e [Program 8-3] Improving Handwritten Number Recognition Performance \u003cbr\u003e[Program 8-4] Zip Code Recognizer v.2 (CNN Version)\u003cbr\u003e 8.6 Improving Deep Learning Algorithms\u003cbr\u003e [Program 8-5] Checking the Augmented Image\u003cbr\u003e 8.7 Transfer Learning\u003cbr\u003e [Program 8-6] Recognizing Natural Images with ResNet50\u003cbr\u003e [Program 8-7] Dog Breed Recognition with DenseNet121\u003cbr\u003e 8.8 [Vision Agent 7] Dog Breed Recognition Program\u003cbr\u003e [Program 8-8] Implementing a Dog Breed Recognition Program\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 09 Perception\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 9.1 What is awareness?\u003cbr\u003e 9.2 Classification\u003cbr\u003e 9.3 Detection\u003cbr\u003e [Program 9-1] Detecting Objects in Still Images with YOLO v3\u003cbr\u003e [Program 9-2] Detecting Objects in Videos with YOLO v3\u003cbr\u003e [Program 9-3] Measuring Video Throughput of YOLO v3\u003cbr\u003e 9.4 Split\u003cbr\u003e [Program 9-4] Training U-net with the Oxford pets dataset\u003cbr\u003e [Program 9-5] Semantic segmentation of still images using the pixellib library\u003cbr\u003e [Program 9-6] Semantic segmentation of video using the pixellib library\u003cbr\u003e [Program 9-7] Segmenting still images using the pixellib library \u003cbr\u003e[Program 9-8] Segmenting a Video Using the Pixellib Library\u003cbr\u003e 9.5 [Vision Agent 8] Change the background as you like\u003cbr\u003e [Program 9-9] Changing the background as you like using the pixellib library\u003cbr\u003e 9.6 People Recognition\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10 Dynamic Vision\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 10.1 Motion Analysis\u003cbr\u003e [Program 10-1] Estimating Optical Flow Using the Farneback Algorithm\u003cbr\u003e [Program 10-2] Tracking Objects with the KLT Tracking Algorithm\u003cbr\u003e 10.2 Tracking\u003cbr\u003e [Program 10-3] Tracking People with SORT\u003cbr\u003e 10.3 Recognizing People in Video Using MediaPipe\u003cbr\u003e [Program 10-4] Detecting Faces with BlazeFace\u003cbr\u003e [Program 10-5] Detecting Faces in Video\u003cbr\u003e [Program 10-6] Implementing Augmented Reality to Decorate Your Face\u003cbr\u003e [Program 10-7] Detecting Face Meshes with FaceMesh\u003cbr\u003e [Program 10-8] Detecting Hand Landmarks\u003cbr\u003e 10.4 Posture Estimation and Action Classification\u003cbr\u003e [Program 10-9] Pose Estimation Using BlazePose\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11 Vision Transformer\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 11.1 Note \u003cbr\u003e11.2 Recurrent Neural Networks and Attention\u003cbr\u003e 11.3 Transformer\u003cbr\u003e 11.4 Vision Transformer\u003cbr\u003e [Program 11-1] Implementing a Vision Transformer to Classify CIFAR-10\u003cbr\u003e [Program 11-2] Vision Transformer Classifying CIFAR-10: Improving Performance with Image Augmentation and Data Augmentation\u003cbr\u003e 11.5 Vision Transformer Programming Practice\u003cbr\u003e [Program 11-3] Video Classification Using Hugging Face's ViT\u003cbr\u003e [Program 11-4] Object Detection Using Hugging Face's DETR\u003cbr\u003e [Program 11-5] Explaining Videos Using Hugging Face's CLIP\u003cbr\u003e 11.6 Characteristics of Transformers\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 12 Three-Dimensional Vision\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 12.1 3D Geometry and Calibration\u003cbr\u003e 12.2 Depth estimation\u003cbr\u003e 12.3 RGB-D Image Recognition\u003cbr\u003e 12.4 Point Cloud Recognition\u003cbr\u003e [Program 12-1] Generating and Displaying Point Clouds from the ModelNet Dataset\u003cbr\u003e [Program 12-2] Classifying Point Cloud Images Using PointNet\u003cbr\u003e Practice problems\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 13 Creation Vision\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 13.1 Generative Model Basics \u003cbr\u003e[Program 13-1] Creating a generative model that generates (height, weight)\u003cbr\u003e [Program 13-2] Gaussian Modeling of MNIST and Generating Samples\u003cbr\u003e [Program 13-3] Modeling MNIST with GMM and Generating Samples\u003cbr\u003e 13.2 Generative models using autoencoders\u003cbr\u003e [Program 13-4] Modeling MNIST with an Autoencoder and Generating Samples\u003cbr\u003e [Program 13-5] Modeling MNIST with a Variational Autoencoder and Generating Samples\u003cbr\u003e 13.3 Generative Adversarial Networks\u003cbr\u003e [Program 13-6] Modeling fashion MNIST with a GAN and generating samples\u003cbr\u003e [Program 13-7] Modeling CIFAR-10 Natural Images with a GAN and Generating Samples\u003cbr\u003e 13.4 Diffusion Model\u003cbr\u003e 13.5 Evaluation of the generative model\u003cbr\u003e 13.6 Multimodal Generative Models: Combining Language and Vision\u003cbr\u003e [Program 13-8] Generating Samples with Stable Diffusion\u003cbr\u003e 13.7 Can Generative Models Be Art?\u003cbr\u003e Practice problems\u003cbr\u003e References\u003cbr\u003e\u003cbr\u003e [Online Appendix](Download address: http:\/\/www.hanbit.co.kr\/src\/4548)\u003cbr\u003e Appendix A: Python Programming Fundamentals \u003cbr\u003eAppendix B: Basic Linear Algebra\u003cbr\u003e Appendix C Probability Basics\u003cbr\u003e Appendix D Diffusion Model Programming Practice\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4069\/MidCate001\/406804927.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eComputer Vision: A Balanced Study of Classical and Deep Learning Methods, Theory and Practice\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e It provides a balanced mix of theory and practice, from rule-based classical computer vision to data-driven deep learning computer vision.\u003cbr\u003e We implement 85 Python programs using OpenCV, which supports classical computer vision, and TensorFlow, which supports deep learning computer vision.\u003cbr\u003e This book will serve as a solid guide for learning computer vision, introducing Transformers, which boast excellent performance with outstanding scalability, and 3D vision and generative vision, which are essential for interfacing with robots. \u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e January 5, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 664 pages | 188*235*35mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791156645481\u003c\/div\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1156645484 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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