Angebote zu "Spaces" (28 Treffer)

Kategorien

Shops

Paint by Sticker Kids: Zoo Animals
9,49 € *
ggf. zzgl. Versand

Move over, coloring books! Paint by Sticker Kids is back with a second book of amazing art for kids to make, one sticker at a time.Paint by Sticker Kids: Zoo Animals includes everything you need to create 10 bright, playful, full-color illustrations of zoo animals--illustrated templates printed on perforated card stock, and 10 pages of stickers to fill in and create the artwork. The fun, vibrant images are rendered in "low-poly," a computer graphics style using geometric polygon shapes to create a 3-D effect. As in paint-by-number, each template is divided into hundreds of spaces, each with a number that corresponds to a particular sticker. Find the sticker, peel it, and place it in the right space. Add the next, and the next, and the next and voila! Animals include a koala, frog, elephant, red panda, puffin, peacock, snake, girafe, tiger, and gorilla. Kids will love watching these pictures come to life. Plus each is suitable for framing or the fridge.

Anbieter: buecher
Stand: 23.11.2020
Zum Angebot
Computer Vision: A Modern Approach
54,99 € *
ggf. zzgl. Versand

Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. Features + Benefits Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs. Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics. Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance. Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications. Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth. While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge. Application surveys—Describe numerous important application areas such as image based rendering and digital libraries. Teaches students about practical use of techniques and helps them gain insight into the demands of applications. Many important algorithms broken down and illustrated in pseudo code. Enables students to build working systems easily as they can understand the construction of the final application. Excellent pedagogy throughout the text—Includes numerous worked examples, exercises, programming assignments, and extensive illustrations. Provides students with ample opportunity to apply the concepts in the text. I IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14 1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14 1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16 1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19 1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20 1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23 1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27 1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Light and Shading 32 2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33 2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34 2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36 2.1.4 Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2 Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.2.1 Radiometric Calibration and High Dynamic Range Images . . 38 2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . 40 2.2.3 Inferring Lightness and Illumination . . . . . . . . . . . . . . 43 2.2.4 Photometric Stereo: Shape from Multiple Shaded Images . . 46 2.3 Modelling Interreflection . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3.1 The Illumination at a Patch Due to an Area Source . . . . . 52 2.3.2 Radiosity and Exitance . . . . . . . . . . . . . . . . . . . . . 54 2.3.3 An Interreflection Model . . . . . . . . . . . . . . . . . . . . . 55 2.3.4 Qualitative Properties of Interreflections . . . . . . . . . . . . 56 2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59 2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3 Color 68 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73 3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76 3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83 3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90 3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92 3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95 3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filters 107 4.1 Linear Filters and Convolution . . . . . . . . . . . . . . . . . . . . . 107 4.1.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.2 Shift Invariant Linear Systems . . . . . . . . . . . . . . . . . . . . . 112 4.2.1 Discrete Convolution . . . . . . . . . . . . . . . . . . . . . . . 113 4.2.2 Continuous Convolution . . . . . . . . . . . . . . . . . . . . . 115 4.2.3 Edge Effects in Discrete Convolutions . . . . . . . . . . . . . 118 4.3 Spatial Frequency and Fourier Transforms . . . . . . . . . . . . . . . 118 4.3.1 Fourier Transforms . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4 Sampling and Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.4.2 Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.4.3 Smoothing and Resampling . . . . . . . . . . . . . . . . . . . 126 4.5 Filters as Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.5.1 Convolution as a Dot Product . . . . . . . . . . . . . . . . . 131 4.5.2 Changing Basis . . . . . . . . . . . . . . . . . . . . . . . . . . 132 4.6 Technique: Normalized Correlation and Finding Patterns . . . . . . 132 4.6.1 Controlling the Television by Finding Hands by Normalized Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 4.7 Technique: Scale and Image Pyramids . . . . . . . . . . . . . . . . . 134 4.7.1 The Gaussian Pyramid . . . . . . . . . . . . . . . . . . . . . 135 4.7.2 Applications of Scaled Representations . . . . . . . . . . . . . 136 4.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5 Local Image Features 141 5.1 Computing the Image Gradient . . . . . . . . . . . . . . . . . . . . . 141 5.1.1 Derivative of Gaussian Filters . . . . . . . . . . . . . . . . . . 142 5.2 Representing the Image Gradient . . . . . . . . . . . . . . . . . . . . 144 5.2.1 Gradient-Based Edge Detectors . . . . . . . . . . . . . . . . . 145 5.2.2 Orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.3 Finding Corners and Building Neighborhoods . . . . . . . . . . . . . 148 5.3.1 Finding Corners . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.2 Using Scale and Orientation to Build a Neighborhood . . . . 151 5.4 Describing Neighborhoods with SIFT and HOG Features . . . . . . 155 5.4.1 SIFT Features . . . . . . . . . . . . . . . . . . . . . . . . . . 157 5.4.2 HOG Features . . . . . . . . . . . . . . . . . . . . . . . . . . 159 5.5 Computing Local Features in Practice . . . . . . . . . . . . . . . . . 160 5.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 6 Texture 164 6.1 Local Texture Representations Using Filters . . . . . . . . . . . . . . 166 6.1.1 Spots and Bars . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.1.2 From Filter Outputs to Texture Representation . . . . . . . . 168 6.1.3 Local Texture Representations in Practice . . . . . . . . . . . 170 6.2 Pooled Texture Representations by Discovering Textons . . . . . . . 171 6.2.1 Vector Quantization and Textons . . . . . . . . . . . . . . . . 172 6.2.2 K-means Clustering for Vector Quantization . . . . . . . . . . 172 6.3 Synthesizing Textures and Filling Holes in Images . . . . . . . . . . 176 6.3.1 Synthesis by Sampling Local Models . . . . . . . . . . . . . . 176 6.3.2 Filling in Holes in Images . . . . . . . . . . . . . . . . . . . . 179 6.4 Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 6.4.1 Non-local Means . . . . . . . . . . . . . . . . . . . . . . . . . 183 6.4.2 Block Matching 3D (BM3D) . . . . . . . . . . . . . . . . . . 183 6.4.3 Learned Sparse Coding . . . . . . . . . . . . . . . . . . . . . 184 6.4.4 Results . . . . . . . . . . . . . . . . . . . . .Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

Anbieter: buecher
Stand: 23.11.2020
Zum Angebot
Computer Vision: A Modern Approach
54,99 € *
ggf. zzgl. Versand

Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. Features + Benefits Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs. Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics. Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance. Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications. Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth. While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge. Application surveys—Describe numerous important application areas such as image based rendering and digital libraries. Teaches students about practical use of techniques and helps them gain insight into the demands of applications. Many important algorithms broken down and illustrated in pseudo code. Enables students to build working systems easily as they can understand the construction of the final application. Excellent pedagogy throughout the text—Includes numerous worked examples, exercises, programming assignments, and extensive illustrations. Provides students with ample opportunity to apply the concepts in the text. I IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14 1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14 1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16 1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19 1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20 1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23 1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27 1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Light and Shading 32 2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33 2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34 2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36 2.1.4 Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2 Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.2.1 Radiometric Calibration and High Dynamic Range Images . . 38 2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . 40 2.2.3 Inferring Lightness and Illumination . . . . . . . . . . . . . . 43 2.2.4 Photometric Stereo: Shape from Multiple Shaded Images . . 46 2.3 Modelling Interreflection . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3.1 The Illumination at a Patch Due to an Area Source . . . . . 52 2.3.2 Radiosity and Exitance . . . . . . . . . . . . . . . . . . . . . 54 2.3.3 An Interreflection Model . . . . . . . . . . . . . . . . . . . . . 55 2.3.4 Qualitative Properties of Interreflections . . . . . . . . . . . . 56 2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59 2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3 Color 68 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73 3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76 3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83 3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90 3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92 3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95 3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filters 107 4.1 Linear Filters and Convolution . . . . . . . . . . . . . . . . . . . . . 107 4.1.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.2 Shift Invariant Linear Systems . . . . . . . . . . . . . . . . . . . . . 112 4.2.1 Discrete Convolution . . . . . . . . . . . . . . . . . . . . . . . 113 4.2.2 Continuous Convolution . . . . . . . . . . . . . . . . . . . . . 115 4.2.3 Edge Effects in Discrete Convolutions . . . . . . . . . . . . . 118 4.3 Spatial Frequency and Fourier Transforms . . . . . . . . . . . . . . . 118 4.3.1 Fourier Transforms . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4 Sampling and Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.4.2 Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.4.3 Smoothing and Resampling . . . . . . . . . . . . . . . . . . . 126 4.5 Filters as Templates . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.5.1 Convolution as a Dot Product . . . . . . . . . . . . . . . . . 131 4.5.2 Changing Basis . . . . . . . . . . . . . . . . . . . . . . . . . . 132 4.6 Technique: Normalized Correlation and Finding Patterns . . . . . . 132 4.6.1 Controlling the Television by Finding Hands by Normalized Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 4.7 Technique: Scale and Image Pyramids . . . . . . . . . . . . . . . . . 134 4.7.1 The Gaussian Pyramid . . . . . . . . . . . . . . . . . . . . . 135 4.7.2 Applications of Scaled Representations . . . . . . . . . . . . . 136 4.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5 Local Image Features 141 5.1 Computing the Image Gradient . . . . . . . . . . . . . . . . . . . . . 141 5.1.1 Derivative of Gaussian Filters . . . . . . . . . . . . . . . . . . 142 5.2 Representing the Image Gradient . . . . . . . . . . . . . . . . . . . . 144 5.2.1 Gradient-Based Edge Detectors . . . . . . . . . . . . . . . . . 145 5.2.2 Orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.3 Finding Corners and Building Neighborhoods . . . . . . . . . . . . . 148 5.3.1 Finding Corners . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.2 Using Scale and Orientation to Build a Neighborhood . . . . 151 5.4 Describing Neighborhoods with SIFT and HOG Features . . . . . . 155 5.4.1 SIFT Features . . . . . . . . . . . . . . . . . . . . . . . . . . 157 5.4.2 HOG Features . . . . . . . . . . . . . . . . . . . . . . . . . . 159 5.5 Computing Local Features in Practice . . . . . . . . . . . . . . . . . 160 5.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 6 Texture 164 6.1 Local Texture Representations Using Filters . . . . . . . . . . . . . . 166 6.1.1 Spots and Bars . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.1.2 From Filter Outputs to Texture Representation . . . . . . . . 168 6.1.3 Local Texture Representations in Practice . . . . . . . . . . . 170 6.2 Pooled Texture Representations by Discovering Textons . . . . . . . 171 6.2.1 Vector Quantization and Textons . . . . . . . . . . . . . . . . 172 6.2.2 K-means Clustering for Vector Quantization . . . . . . . . . . 172 6.3 Synthesizing Textures and Filling Holes in Images . . . . . . . . . . 176 6.3.1 Synthesis by Sampling Local Models . . . . . . . . . . . . . . 176 6.3.2 Filling in Holes in Images . . . . . . . . . . . . . . . . . . . . 179 6.4 Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 6.4.1 Non-local Means . . . . . . . . . . . . . . . . . . . . . . . . . 183 6.4.2 Block Matching 3D (BM3D) . . . . . . . . . . . . . . . . . . 183 6.4.3 Learned Sparse Coding . . . . . . . . . . . . . . . . . . . . . 184 6.4.4 Results . . . . . . . . . . . . . . . . . . . . .Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

Anbieter: buecher
Stand: 23.11.2020
Zum Angebot
Implementing the Group-Based Early Start Denver...
90,28 € *
ggf. zzgl. Versand

This book examines a group-based adaptation of the Early Start Denver Model (ESDM) designed for use with preschoolers with autism spectrum disorder (ASD). It describes the principles and procedures of the Group-Based Early Start Denver Model (G-ESDM) and provides practical and empirical guidelines for implementing effective, affordable programs across public healthcare and educational settings. Chapters offer rationales and strategies for designing and evaluating interventions, building interdisciplinary teams, and organizing learning spaces to engage student interest. Examples discuss the social interactions in groups that provide opportunities for learning, improving interpersonal skills, and reducing problem behaviors. In addition, the book offers ideas for retooling teaching strategies when an individual child lags behind the rest of the group. Featured topics include:Creating treatment objectives in the G-ESDM.Setting up the G-ESDM team and learning environment. Development of the G-ESDM classroom curriculum. Practical tools such as decision-making trees, teaching templates, and fidelity systems. Facilitating learning through peer interactions and social participation. Implementing the Group-Based Early Start Denver Model for Preschoolers with Autism is a must-have resource for clinicians and practitioners as well as researchers, professors, and graduate students in the fields of child and school psychology, behavioral therapy, and social work along with psychiatry, pediatrics, and educational and healthcare policy.

Anbieter: Dodax
Stand: 23.11.2020
Zum Angebot
Implementing the Group-Based Early Start Denver...
90,28 € *
ggf. zzgl. Versand

This book examines a group-based adaptation of the Early Start Denver Model (ESDM) designed for use with preschoolers with autism spectrum disorder (ASD). It describes the principles and procedures of the Group-Based Early Start Denver Model (G-ESDM) and provides practical and empirical guidelines for implementing effective, affordable programs across public healthcare and educational settings. Chapters offer rationales and strategies for designing and evaluating interventions, building interdisciplinary teams, and organizing learning spaces to engage student interest. Examples discuss the social interactions in groups that provide opportunities for learning, improving interpersonal skills, and reducing problem behaviors. In addition, the book offers ideas for retooling teaching strategies when an individual child lags behind the rest of the group. Featured topics include:Creating treatment objectives in the G-ESDM.Setting up the G-ESDM team and learning environment. Development of the G-ESDM classroom curriculum. Practical tools such as decision-making trees, teaching templates, and fidelity systems. Facilitating learning through peer interactions and social participation. Implementing the Group-Based Early Start Denver Model for Preschoolers with Autism is a must-have resource for clinicians and practitioners as well as researchers, professors, and graduate students in the fields of child and school psychology, behavioral therapy, and social work along with psychiatry, pediatrics, and educational and healthcare policy.

Anbieter: Dodax
Stand: 23.11.2020
Zum Angebot
Lukas Salzmann
51,90 CHF *
ggf. zzgl. Versand

As a painter Lukas Salzmann is committed to representationalism while creating art-historical and media-related references in many of his works. He overpaints photographic templates, thus freeing the images from their functional fixedness. Deploying vehement brushwork and powerful handling, he dissolves content that is seemingly unequivocal and converts it into a polysemy that valorises the mystical as much as it does the obvious. Salzmann’s atmospherically dense pictures open up emotional spaces for viewers, leading them into a state of oscillation between recognisability and recondite unidentifiability, in which the transformative powers of painting can develop their full impact. In the Viewer’s Eyes – the Unknown comprises Lukas Salzmann's oeuvre from 1995 to 2018 and includes an accompanying essay by scholar Rudolf Velhagen, which locates the painter's work in its art historical context.

Anbieter: Orell Fuessli CH
Stand: 23.11.2020
Zum Angebot
Brain-Compatible Mathematics
12,90 CHF *
zzgl. 3,50 CHF Versand

Students’ brains are wired to make them natural, curious learners. The mathematical world around them offers a vast classroom, filled with shapes, spaces, quantities, and experiences to discover and explore, all leading to the construction of understanding. Teachers can use this natural curiosity to tap the inborn neural mechanisms that motivate students to learn—to make relevance and meaning of their surroundings. Brain-Compatible Mathematics, Second Edition bridges the findings from the realms of brain research and improved mathematics instruction through updated teaching samples, connections to the most recent standards, newest research findings, and integration to other content areas. Each brain is different, and when teachers teach problem-solving skills to help students arrive at their own solution paths, students go beyond mere memorization of facts and algorithms to being an actual participant in the development of mathematical understanding. In an informative and relevant approach, Diane Ronis presents teachers and math leaders with an emphasis on thinking, mathematical representation, and construction of ideas and an abundance of: Sample lessons, units, and strategies linked to 2000 NCTM standards Brain-friendly strategies for math teaching that meet NCLB requirements How-to guides for creating more brain-tuned math teaching Ideas for incorporating technology into the math curriculum Planning templates for immediate use By integrating math learning into real-world applications, students can actively practice what they learn, make meaning out of their everyday experiences, and think mathematically for success within today’s information age.

Anbieter: Orell Fuessli CH
Stand: 23.11.2020
Zum Angebot
Human Simulation for Nursing and Health Profess...
130,00 CHF *
ggf. zzgl. Versand

'This book could easily become the 'go to' text for standardized patient utilization and the backbone for implementation strategies in learning programs...It is a must-have for all disciplines interested in adding the human simulation experience to their programs.'--Nursing Education Perspectives 'Today there is an explosion in the use of simulation in nursing and health professions education. The contributors to this text are experts in this format of teaching. They are the designers of the learning spaces, the authors of simulation cases and evaluation methods, the experts who program the human patient simulators and who teach the patient actors to enact the clinical scenarios I consider this a 'handbook' on the design, evaluation and practice of simulation for clinical education. If you are a faculty member with concerns about how your students will make the transition from student to professional, use simulation in your curriculum and learn for yourself that pretending is simulation for life but simulation is pretending for the delivery of exquisite clinical care.' Gloria F. Donnelly, PhD, RN, FAAN Dean and Professor Drexel University College of Nursing and Health Professions Human simulation is changing the face of clinical education in the health professions. Its use has expanded beyond medical school to encompass nursing and mental health clinical education. This comprehensive guide to establishing and managing a human simulation lab has been written by nationally acclaimed simulation experts and is geared for undergraduate, graduate, and professional settings. The text takes the reader step-by-step through the process of planning, organizing, implementing, and maintaining a simulation lab. It describes the required technology, how to train standardized patients, how to implement a simulation, evaluation and analysis of the simulation experience, and how to develop a business plan. The guide details simulation in undergraduate and graduate nursing programs, physician's assistant programs, and mental health education, as well as the use of simulation with critically ill patients, and in perioperative, perianasthesia, women's health, and rehabilitation science settings. Key Features: Offers a blueprint for developing, implementing, and managing a human simulation lab Details use of simulation in numerous nursing and mental health settings along with case studies Provides tools for evaluation and analysis of the simulation experience Presents undergraduate and graduate nursing simulation scenarios and pedagogical strategies Discusses simulation training and required technology Includes templates for writing cases for BSN and MSN levels

Anbieter: Orell Fuessli CH
Stand: 23.11.2020
Zum Angebot
Socialist Spaces
48,90 CHF *
ggf. zzgl. Versand

What were Socialist Spaces? The Eastern Bloc produced distinctive spaces, some of which were fashioned from ideological templates, such as the monumental parade grounds and Red Squares where communist leaders could receive tributes, or new factory cities with towering chimneys and glittering palaces of culture. But what of the grimy toilet in the communal apartment or the forlorn ruins left after the Second World War? This book explores the representation, meanings and uses of space in the socialist countries of Eastern Europe and the Soviet Union between 1947 and 1991. The essays n written from different disciplinary perspectives n investigate the extent to which actual spaces conformed to the dominant political order in the region. Should, for instance, the creation of private spaces, such as the Russian dacha and the Czech chata, be understood as acts of appropriation in which lives were fashioned against the collective or, alternatively, as 'gifts' given by the State in return for quiescence? Whilst monuments and public spaces were designed to relay official ideology, one of the most notable features of the events that marked the end of the Bloc was the way that they became sites of dissent. Examining the myriad ways in which space was used and conceived within socialist society, this book makes an essential contribution to Eastern European and Soviet Studies and provides significant new angles on the factors that underpinned socialism's eventual downfall.

Anbieter: Orell Fuessli CH
Stand: 23.11.2020
Zum Angebot