Convolutional neural networks (CNNs) are a type of deep learning algorithm used for image recognition and natural language processing. CNNs take an image as input and identify features to predict and classify the image. The key steps in a CNN include convolution, ReLU activation, pooling, flattening, and fully connected layers. Convolution extracts features using filters, pooling reduces dimensionality while preserving important information, and fully connected layers integrate the CNN with traditional neural networks for classification. Yann LeCun is credited as the father of CNNs, which are now widely used for applications like computer vision, facial recognition and sentiment analysis.
Tweening and morphing are techniques used in animation to generate intermediate frames between key frames. Tweening uses linear interpolation to create smooth transitions between frames by interpolating point positions. Morphing transitions between full color images by simultaneously warping and dissolving regions of images using tweening techniques applied to mesh grids overlaid on images. Both tweening and morphing require careful setup by artists and are used in hand-drawn animation as well as digital effects in movies.
Computer animation involves creating animation sequences through object definition, path specification, key frames, and in-betweening. There are two main methods for displaying animation sequences: raster animation and color-table animation. Raster animation involves copying frames from memory to the display very quickly, while color-table animation uses a color lookup table to convert logical color numbers in each pixel to physical colors. The document discusses techniques for designing animation sequences like storyboarding, defining objects and paths, specifying key frames, and generating in-between frames. It also covers topics like motion specification using direct motion, goal-directed systems, kinematics, dynamics, and inverse kinematics. Morphing and tweening are introduced as techniques for warping one image into
The document defines various motion graphics and animation terminology used in programs like After Effects. It provides descriptions of terms related to 2D/3D space, layers, effects, keyframing, camera movements, compositing, and other animation and video editing concepts. Terms covered include things like adjustment layers, alpha channels, parenting, expressions, motion blur, precomposing, and trimming. The document acts as a glossary to explain technical terms for those working in motion graphics.
This document provides details on course work completed as part of a Computer Vision course. It includes source images and summaries of edge detection algorithms applied to the images. Edge detection was performed using Roberts, Sobel, Prewitt and Robinson operators, as well as Laplacian of Gaussian. Thresholding techniques are discussed for binarizing the edge detection outputs. The effects of mask size and sigma values on Laplacian of Gaussian are demonstrated. Pseudocode is provided for the convolution operations.
The Image Panorama is a technique of stitching more images to create a more broader view which our normal eye does in a wider angle rather than that of the view which is restricted by the camera
Forgery in digital images can be done by manipulating the digital image to conceal some meaningful or useful information of the image. It can be much difficult to identify the edited region from the original image in various cases. In order to maintain the integrity and authenticity of the image, the detection of forgery in the image is necessary. Adaption of modern lifestyle and advanced photography equipment has made tempering of digital image easy with the help of image editing soft wares. It is thus important to detect such image tempering operations. Different methods exist in literature that divide the suspicious image into overlapped blocks and extract some features from the images to detect the type of forgery that exist in the image. The image forgery detection can be done based on object removal, object addition, unusual color modifications in the image. Many existing techniques are available to overcome this problem but most of these techniques have many limitations. Images are one of the powerful media for communication. In this paper a survey of different types of forgery and digital image forgery detection has been focused.
Computer animation involves creating moving images using computer technology. There are two main categories: computer-generated animation created solely using animation software, and computer-assisted animation where traditional animation is computerized. Animation is created by displaying a series of pictures or frames in quick succession to simulate movement. There are four main components to constructing an animation sequence: storyboard layout, object definition, keyframe specification, and generation of in-between frames to show smooth movement between keyframes. Motion in animation can be controlled through geometric, physical, or behavioral methods.
Face morphing is an interpolation technique that creates a series of intermediate objects between two objects. It was proposed as a way to automatically morph faces by extracting feature points and warping images. The process involves pre-processing images, finding features like eyes and mouth, partitioning the images into regions based on features, performing coordinate transformations between images, and cross-dissolving the images to create the morph. Examples show it can morph between human and animal faces or different expressions of the same person. Feature extraction is key to automatic face morphing and more feature points typically produce better results.
Tweening and morphing are techniques used in animation to generate intermediate frames between key frames. Tweening uses linear interpolation to create smooth transitions between frames by interpolating point positions. Morphing transitions between full color images by simultaneously warping and dissolving regions of images using tweening techniques applied to mesh grids overlaid on images. Both tweening and morphing require careful setup by artists and are used in hand-drawn animation as well as digital effects in movies.
Computer animation involves creating animation sequences through object definition, path specification, key frames, and in-betweening. There are two main methods for displaying animation sequences: raster animation and color-table animation. Raster animation involves copying frames from memory to the display very quickly, while color-table animation uses a color lookup table to convert logical color numbers in each pixel to physical colors. The document discusses techniques for designing animation sequences like storyboarding, defining objects and paths, specifying key frames, and generating in-between frames. It also covers topics like motion specification using direct motion, goal-directed systems, kinematics, dynamics, and inverse kinematics. Morphing and tweening are introduced as techniques for warping one image into
The document defines various motion graphics and animation terminology used in programs like After Effects. It provides descriptions of terms related to 2D/3D space, layers, effects, keyframing, camera movements, compositing, and other animation and video editing concepts. Terms covered include things like adjustment layers, alpha channels, parenting, expressions, motion blur, precomposing, and trimming. The document acts as a glossary to explain technical terms for those working in motion graphics.
This document provides details on course work completed as part of a Computer Vision course. It includes source images and summaries of edge detection algorithms applied to the images. Edge detection was performed using Roberts, Sobel, Prewitt and Robinson operators, as well as Laplacian of Gaussian. Thresholding techniques are discussed for binarizing the edge detection outputs. The effects of mask size and sigma values on Laplacian of Gaussian are demonstrated. Pseudocode is provided for the convolution operations.
The Image Panorama is a technique of stitching more images to create a more broader view which our normal eye does in a wider angle rather than that of the view which is restricted by the camera
Forgery in digital images can be done by manipulating the digital image to conceal some meaningful or useful information of the image. It can be much difficult to identify the edited region from the original image in various cases. In order to maintain the integrity and authenticity of the image, the detection of forgery in the image is necessary. Adaption of modern lifestyle and advanced photography equipment has made tempering of digital image easy with the help of image editing soft wares. It is thus important to detect such image tempering operations. Different methods exist in literature that divide the suspicious image into overlapped blocks and extract some features from the images to detect the type of forgery that exist in the image. The image forgery detection can be done based on object removal, object addition, unusual color modifications in the image. Many existing techniques are available to overcome this problem but most of these techniques have many limitations. Images are one of the powerful media for communication. In this paper a survey of different types of forgery and digital image forgery detection has been focused.
Computer animation involves creating moving images using computer technology. There are two main categories: computer-generated animation created solely using animation software, and computer-assisted animation where traditional animation is computerized. Animation is created by displaying a series of pictures or frames in quick succession to simulate movement. There are four main components to constructing an animation sequence: storyboard layout, object definition, keyframe specification, and generation of in-between frames to show smooth movement between keyframes. Motion in animation can be controlled through geometric, physical, or behavioral methods.
Face morphing is an interpolation technique that creates a series of intermediate objects between two objects. It was proposed as a way to automatically morph faces by extracting feature points and warping images. The process involves pre-processing images, finding features like eyes and mouth, partitioning the images into regions based on features, performing coordinate transformations between images, and cross-dissolving the images to create the morph. Examples show it can morph between human and animal faces or different expressions of the same person. Feature extraction is key to automatic face morphing and more feature points typically produce better results.
This document describes an algorithm to identify cigarette butts in images. The algorithm uses color segmentation, edge detection, and enhancement techniques in Matlab. It turns the original image into a binary image segmented by the color of cigarette butts. Color and edge detection are used to create a binary mask. Enhancement techniques like dilation and hole filling are applied to smooth edges before labeling objects with random colors for visualization. While the algorithm identifies most cigarette butts, it does not fully eliminate background noise.
a collection of terminologies used in the game development industry, from my point of view any one who intends to work in that business should understand them.
This document provides an overview of 3D rendering concepts. It discusses the differences between real-time rendering used for video games and offline rendering used for film and television. Real-time rendering approximates effects for speed while offline rendering can simulate effects like reflections and global illumination more accurately. It also covers rendering techniques like textures, bump mapping, shadows, reflections, refractions, and indirect illumination. Camera properties like depth of field, focal length, and film gate size are also explained. Finally, it briefly introduces Maya's built-in CPU renderer.
The document discusses various 3D animation and modeling workflows and file formats, including OBJ, FBX, Collada, and Alembic formats. It also covers motion capture techniques from low to high budget options as well as cleaning up motion capture data. The document then discusses the free and open source 3D software Blender and its Cycles renderer. It also mentions the Luxrender, Radeon Pro, Unity, and Unreal game engines.
This paper describes an experimental extension to the Mondrian system that uses voice input to disambiguate a user's intent when performing actions with a mouse. Users can issue voice commands like "Align-left" or "Length-50" while performing mouse actions to modify how the system interprets and executes the action. This allows users to customize general mouse operations into highly specialized ones and convey their precise intent to the system. The system is then able to learn procedures from user demonstrations and correctly apply them to new tasks by taking both mouse input and accompanying voice commands into account.
This document discusses various computer animation techniques. It begins with an introduction to animation and the concept of frame rate. There are three main types of animation discussed: traditional/hand-drawn animation where drawings are traced onto sheets and photographed, stop-motion animation which manipulates real-world objects, and computer animation which can be 2D or 3D. Computer animation techniques include raster animation where images are redrawn and moved pixel by pixel, and morphing where shapes are transformed between key frames. Motion in animation can be specified through direct parameters, paths, inverse kinematics, or motion capture of real movements. Computer animation has applications in movies, games, simulation, and more.
A game is a structured activity involving goals, rules, conflict, interaction and rewards. There are different types of video games like arcade, computer, console and mobile games. Common game genres include action, adventure, puzzle, role playing, strategy and simulation games. The document then provides examples and guidelines for modeling, texturing and other aspects of the game development process.
Textures allow for adding detail to 3D models without increasing polycount. UV mapping involves projecting a 2D texture onto a 3D mesh using UV coordinates. Common texture types include diffuse maps for color and bump/normal maps for simulated surface detail without changing geometry. Displacement maps can actually modify the mesh geometry.
This document provides an introduction to computer graphics. It discusses how 3D scenes are represented internally with geometric models like polygons, primitives, and smooth patches. These models are projected using linear perspective to generate 2D images. Pixels are used to represent digital images. Rendering involves visibility processing, shading based on lighting models, and texture mapping. It allows for realistic images through techniques like shadows, reflections, and global lighting simulations.
The document describes a student project on face morphing. It includes an abstract, introduction, and literature review sections. The introduction provides an overview of digital image processing and defines the problem of face morphing as developing software to combine parts of different faces into a new composite face. It also discusses expanding/contracting images, blurring edges during morphing, and averaging filter operations. The literature review covers mosaicking images, morphing techniques, and dealing with color images. The overall goal of the project is to develop a program that allows users to edit and combine facial features from a database to generate new composite faces.
The document provides an overview of the modeling and texturing process for a 3D model. It discusses using references to help with scale, dimensions, and later textures. For modeling, the objective was low poly count with multiple approaches. Texture creation involved unwrapping the 3D model and flattening it onto a 2D plane with overlapping to keep the texture size small. The final steps were attaching models to one mesh, applying the final texture, and exporting to a game engine for rendering with a total poly count of 2400.
Vector graphics use mathematical formulas to define images as objects made of points and paths, allowing resolution-independent scaling. Raster graphics are composed of pixels arranged in a grid to form images. Key factors that determine raster image quality include resolution, color depth, and file format. Common file formats like JPEG, PNG, and GIF vary in their compression algorithms and support for animation and transparency.
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Deep computer vision uses deep learning and machine learning techniques to build powerful vision systems that can analyze raw visual inputs and understand what objects are present and where they are located. Convolutional neural networks (CNNs) are well-suited for computer vision tasks as they can learn visual features and hierarchies directly from data through operations like convolution, non-linearity, and pooling. CNNs apply filters to extract features, introduce non-linearity, and use pooling to reduce dimensionality while preserving spatial data. This repeating structure allows CNNs to learn increasingly complex features to perform tasks like image classification, object detection, semantic segmentation, and continuous control from raw pixels.
A graphic library and an application for simple curve manipolationgraphitech
The project consists in a software that uses a developed library in the Intermediate project to construct complex functionalities.
The functional asked requisites are:
1. Load a picture in background.
2. Generate different kinds of curves using points generated by mouse:
a. Hermite Spline,
b. Bezier Spline,
c. BSpline,
d. Lagrange.
In this way we can isolate the perimeter of the previous loaded picture.
3. Move single points using the mouse drag property.
4. Select multi points and move them together.
5. Curves must be connected to each other.
6. Save the composition of curves in a file.
7. Load the composition of curves saved before.
8. Load a point file ad interpolate the points using the available curves. In this way we can observe the differences generated when same points are interpolated by different kind curves.
9. Change in real time the kind of curve that interpolates a set of points.
The model explains how we can Automate System using Artificial Intelligence.
It broadly concerns about:-
1. Lane Detection.
2. Traffic Sign Classification.
3. Behavioural Cloning.
This document provides an introduction to convolutional neural networks (CNNs) in 3 paragraphs:
1. It explains the principles behind CNNs including convolution, ReLU activation, and max pooling. Convolution extracts features from images using kernels, ReLU introduces non-linearity, and max pooling reduces data size and processing time.
2. It describes how CNN stacks work with a fully connected layer at the end to calculate probabilities for each label. The feature maps from CNN layers are input to the neural network and a softmax activation assigns decimal probabilities.
3. It discusses techniques for avoiding overfitting like data augmentation, dropout regularization, and transfer learning. Data augmentation artificially increases data variety, dropout removes activations during training,
Designing a neural network architecture for image recognitionShandukaniVhulondo
The document discusses the design of a basic neural network architecture for image recognition. It begins by outlining a simple design with dense layers but notes this does not work well for images. Convolutional layers are introduced to help detect patterns regardless of location. Max pooling and dropout layers are also discussed to make the network more efficient and robust. The document provides examples of how these various layer types work and combines them into a basic convolutional block that can be stacked for more complex images.
Real Time Sign Language Recognition Using Deep LearningIRJET Journal
The document describes a study that used the YOLOv5 deep learning model to perform real-time sign language recognition. The researchers trained and tested the model on the Roboflow dataset along with additional images. They achieved 88.4% accuracy, 76.6% precision, and 81.2% recall. For comparison, they also trained a CNN model which achieved lower accuracy of 52.98%. The YOLOv5 model was able to detect signs in complex environments and perform accurate real-time detection, demonstrating its advantages over CNN for this task.
Vincent gives an introductory presentation on convolutional neural networks (CNNs) for image recognition. He covers:
1) The principles of CNNs including convolution, ReLU activation, and max pooling for extracting features from images.
2) How CNN stacks are used along with a fully connected layer to generate predictions from feature maps.
3) Techniques for avoiding overfitting like data augmentation, dropout, and transfer learning by leveraging pretrained models.
This document describes an algorithm to identify cigarette butts in images. The algorithm uses color segmentation, edge detection, and enhancement techniques in Matlab. It turns the original image into a binary image segmented by the color of cigarette butts. Color and edge detection are used to create a binary mask. Enhancement techniques like dilation and hole filling are applied to smooth edges before labeling objects with random colors for visualization. While the algorithm identifies most cigarette butts, it does not fully eliminate background noise.
a collection of terminologies used in the game development industry, from my point of view any one who intends to work in that business should understand them.
This document provides an overview of 3D rendering concepts. It discusses the differences between real-time rendering used for video games and offline rendering used for film and television. Real-time rendering approximates effects for speed while offline rendering can simulate effects like reflections and global illumination more accurately. It also covers rendering techniques like textures, bump mapping, shadows, reflections, refractions, and indirect illumination. Camera properties like depth of field, focal length, and film gate size are also explained. Finally, it briefly introduces Maya's built-in CPU renderer.
The document discusses various 3D animation and modeling workflows and file formats, including OBJ, FBX, Collada, and Alembic formats. It also covers motion capture techniques from low to high budget options as well as cleaning up motion capture data. The document then discusses the free and open source 3D software Blender and its Cycles renderer. It also mentions the Luxrender, Radeon Pro, Unity, and Unreal game engines.
This paper describes an experimental extension to the Mondrian system that uses voice input to disambiguate a user's intent when performing actions with a mouse. Users can issue voice commands like "Align-left" or "Length-50" while performing mouse actions to modify how the system interprets and executes the action. This allows users to customize general mouse operations into highly specialized ones and convey their precise intent to the system. The system is then able to learn procedures from user demonstrations and correctly apply them to new tasks by taking both mouse input and accompanying voice commands into account.
This document discusses various computer animation techniques. It begins with an introduction to animation and the concept of frame rate. There are three main types of animation discussed: traditional/hand-drawn animation where drawings are traced onto sheets and photographed, stop-motion animation which manipulates real-world objects, and computer animation which can be 2D or 3D. Computer animation techniques include raster animation where images are redrawn and moved pixel by pixel, and morphing where shapes are transformed between key frames. Motion in animation can be specified through direct parameters, paths, inverse kinematics, or motion capture of real movements. Computer animation has applications in movies, games, simulation, and more.
A game is a structured activity involving goals, rules, conflict, interaction and rewards. There are different types of video games like arcade, computer, console and mobile games. Common game genres include action, adventure, puzzle, role playing, strategy and simulation games. The document then provides examples and guidelines for modeling, texturing and other aspects of the game development process.
Textures allow for adding detail to 3D models without increasing polycount. UV mapping involves projecting a 2D texture onto a 3D mesh using UV coordinates. Common texture types include diffuse maps for color and bump/normal maps for simulated surface detail without changing geometry. Displacement maps can actually modify the mesh geometry.
This document provides an introduction to computer graphics. It discusses how 3D scenes are represented internally with geometric models like polygons, primitives, and smooth patches. These models are projected using linear perspective to generate 2D images. Pixels are used to represent digital images. Rendering involves visibility processing, shading based on lighting models, and texture mapping. It allows for realistic images through techniques like shadows, reflections, and global lighting simulations.
The document describes a student project on face morphing. It includes an abstract, introduction, and literature review sections. The introduction provides an overview of digital image processing and defines the problem of face morphing as developing software to combine parts of different faces into a new composite face. It also discusses expanding/contracting images, blurring edges during morphing, and averaging filter operations. The literature review covers mosaicking images, morphing techniques, and dealing with color images. The overall goal of the project is to develop a program that allows users to edit and combine facial features from a database to generate new composite faces.
The document provides an overview of the modeling and texturing process for a 3D model. It discusses using references to help with scale, dimensions, and later textures. For modeling, the objective was low poly count with multiple approaches. Texture creation involved unwrapping the 3D model and flattening it onto a 2D plane with overlapping to keep the texture size small. The final steps were attaching models to one mesh, applying the final texture, and exporting to a game engine for rendering with a total poly count of 2400.
Vector graphics use mathematical formulas to define images as objects made of points and paths, allowing resolution-independent scaling. Raster graphics are composed of pixels arranged in a grid to form images. Key factors that determine raster image quality include resolution, color depth, and file format. Common file formats like JPEG, PNG, and GIF vary in their compression algorithms and support for animation and transparency.
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Deep computer vision uses deep learning and machine learning techniques to build powerful vision systems that can analyze raw visual inputs and understand what objects are present and where they are located. Convolutional neural networks (CNNs) are well-suited for computer vision tasks as they can learn visual features and hierarchies directly from data through operations like convolution, non-linearity, and pooling. CNNs apply filters to extract features, introduce non-linearity, and use pooling to reduce dimensionality while preserving spatial data. This repeating structure allows CNNs to learn increasingly complex features to perform tasks like image classification, object detection, semantic segmentation, and continuous control from raw pixels.
A graphic library and an application for simple curve manipolationgraphitech
The project consists in a software that uses a developed library in the Intermediate project to construct complex functionalities.
The functional asked requisites are:
1. Load a picture in background.
2. Generate different kinds of curves using points generated by mouse:
a. Hermite Spline,
b. Bezier Spline,
c. BSpline,
d. Lagrange.
In this way we can isolate the perimeter of the previous loaded picture.
3. Move single points using the mouse drag property.
4. Select multi points and move them together.
5. Curves must be connected to each other.
6. Save the composition of curves in a file.
7. Load the composition of curves saved before.
8. Load a point file ad interpolate the points using the available curves. In this way we can observe the differences generated when same points are interpolated by different kind curves.
9. Change in real time the kind of curve that interpolates a set of points.
The model explains how we can Automate System using Artificial Intelligence.
It broadly concerns about:-
1. Lane Detection.
2. Traffic Sign Classification.
3. Behavioural Cloning.
This document provides an introduction to convolutional neural networks (CNNs) in 3 paragraphs:
1. It explains the principles behind CNNs including convolution, ReLU activation, and max pooling. Convolution extracts features from images using kernels, ReLU introduces non-linearity, and max pooling reduces data size and processing time.
2. It describes how CNN stacks work with a fully connected layer at the end to calculate probabilities for each label. The feature maps from CNN layers are input to the neural network and a softmax activation assigns decimal probabilities.
3. It discusses techniques for avoiding overfitting like data augmentation, dropout regularization, and transfer learning. Data augmentation artificially increases data variety, dropout removes activations during training,
Designing a neural network architecture for image recognitionShandukaniVhulondo
The document discusses the design of a basic neural network architecture for image recognition. It begins by outlining a simple design with dense layers but notes this does not work well for images. Convolutional layers are introduced to help detect patterns regardless of location. Max pooling and dropout layers are also discussed to make the network more efficient and robust. The document provides examples of how these various layer types work and combines them into a basic convolutional block that can be stacked for more complex images.
Real Time Sign Language Recognition Using Deep LearningIRJET Journal
The document describes a study that used the YOLOv5 deep learning model to perform real-time sign language recognition. The researchers trained and tested the model on the Roboflow dataset along with additional images. They achieved 88.4% accuracy, 76.6% precision, and 81.2% recall. For comparison, they also trained a CNN model which achieved lower accuracy of 52.98%. The YOLOv5 model was able to detect signs in complex environments and perform accurate real-time detection, demonstrating its advantages over CNN for this task.
Vincent gives an introductory presentation on convolutional neural networks (CNNs) for image recognition. He covers:
1) The principles of CNNs including convolution, ReLU activation, and max pooling for extracting features from images.
2) How CNN stacks are used along with a fully connected layer to generate predictions from feature maps.
3) Techniques for avoiding overfitting like data augmentation, dropout, and transfer learning by leveraging pretrained models.
From biological to artificial neurons. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system.
To learn the basics of neural networks on this workshop we sill explain one of it implement in python. During the workshop we will explain also the script which are the layers processing unit and they function using simply matrix operation such as Hadammard or Dot product. Basic features such as learning modifier (alpha) and bias units are implemented as well..
Transcript - Data Visualisation - Tools and TechniquesARDC
Martin Schweitzer presents on data visualization tools and techniques. He demonstrates Matplotlib, Pandas, Seaborn, Bokeh, Plotly, and Basemap. With Matplotlib, he creates simple plots with just one or two lines of code, as well as more advanced plots. Pandas allows plotting data from CSV files easily. Seaborn builds on Matplotlib to provide publication-ready styling and includes sample datasets. Web-based tools like Bokeh and Plotly allow interactive visualizations. Basemap supports geographic data visualization.
Convolutional neural networks (CNNs) are a type of deep neural network commonly used for analyzing visual imagery. CNNs use various techniques like convolution, ReLU activation, and pooling to extract features from images and reduce dimensionality while retaining important information. CNNs are trained end-to-end using backpropagation to update filter weights and minimize output error. Overall CNN architecture involves an input layer, multiple convolutional and pooling layers to extract features, fully connected layers to classify features, and an output layer. CNNs can be implemented using sequential models in Keras by adding layers, compiling with an optimizer and loss function, fitting on training data over epochs with validation monitoring, and evaluating performance on test data.
Any camera in the last 10 years, probably has face detection in action:
Face detection is a great feature for cameras. When the camera can automatically pick out
faces, it can make sure that all the faces are in focus before it takes the picture.
But this concept uses it for a different purpose — finding the areas of the image that has to be passed to the next step of the procedure.
To find faces in an image, the image has to be in the black and white format as colour data
is not necessary.
Now we are to the meat of the problem — actually telling faces apart. The solution is to train a Deep Convolutional Neural Network. But instead of training the network to recognize pictures objects like we did last time, we are going to train it to generate 128 measurements for each face.
The training process works by looking at 3 face images at a time:
Load a training face image of a known person.
Load another picture of the same known person.
Load a picture of a totally different person.
Then the algorithm looks at the measurements it is currently generating for each of those three images. It then tweaks the neural network slightly so that it makes sure the measurements it generates for #1 and #2 are slightly closer while making sure the measurements for #2 and #3 are slightly further apart.
After repeating this step millions of times for millions of images of thousands of different people, the neural network learns to reliably generate 128 measurements for each person. Any ten different pictures of the same person should give roughly the same measurements.
Machine learning people call the 128 measurements of each face an embedding. The idea of reducing complicated raw data like a picture into a list of computer-generated numbers comes up a lot in machine learning.
This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
After doing the comparative analysis by processing all the known images, our software finally tells the result of test image by displaying its name.
We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
When, we isolated the faces in our image. But now we have to deal with the problem that faces turned different directions look totally different to a computer:
To account for this, we will try to warp each picture so that the eyes and lips are always in the sample place in the image. To do this, we are going to use an algorithm called face landmark estimation .
The basic idea is we will come up with 68 specific points (called landmarks) that exist on every face — the top of the chin, the outside edge of each eye, the inner edge of each eyebrow, etc. Then we will train a machine learning algorithm to be able to find these 68 specific points on any face.
It has successfully run the alg
BMVA summer school MATLAB programming tutorialpotaters
This document discusses improving the runtime performance of MATLAB code through vectorization. It provides an example of an inefficient MATLAB function that approximates cycles of a square wave using sine waves. To optimize this code, the document suggests manipulating arrays rather than individual array elements, which can be done by removing the nested for loops. Vectorizing the code to operate on entire arrays at once rather than elements sequentially would improve performance. Profiling the code using MATLAB's profiler tool can help identify bottlenecks to target for optimization.
Deep convolutional neural networks (DCNNs) are a type of neural network commonly used for analyzing visual imagery. They work by using convolutional layers that extract features from images using small filters that slide across the input. Pooling layers then reduce the spatial size of representations to reduce computation. Multiple convolutional and pooling layers are followed by fully connected layers that perform classification. Key aspects of DCNNs include activation functions, dropout layers, hyperparameters like filter size and number of layers, and training for many epochs with techniques like early stopping.
16 OpenCV Functions to Start your Computer Vision journey.docxssuser90e017
This article discusses 16 OpenCV functions for computer vision tasks with Python code examples. It begins with an introduction to computer vision and why OpenCV is useful. It then covers functions for reading/writing images, changing color spaces, resizing images, rotating images, translating images, thresholding images, adaptive thresholding, image segmentation with watershed algorithm, bitwise operations, edge detection, image filtering, contours, SIFT, SURF, feature matching, and face detection. Code examples are provided for each function to demonstrate its use.
This document provides an overview of the Scale Invariant Feature Transform (SIFT) algorithm for feature detection and matching across images. It begins by introducing SIFT and its applications in computer vision. The document then outlines the key steps of the SIFT algorithm, including constructing scale space, approximating the Laplacian of Gaussian, finding keypoints, removing low-contrast keypoints, assigning orientations to keypoints, and generating SIFT features. Details are provided for each step, with examples to illustrate the process. The goal of SIFT is to detect features that are invariant to scale, rotation, illumination and viewpoint changes.
This document provides an overview and examples of using HTML5 canvas to create graphics and mobile apps. It discusses using canvas to draw basic shapes, images, and textures. It also covers touch events, animation, and creating menus. Later examples demonstrate loading images, simple games with touch input, and playing sound. The document emphasizes best practices like only drawing after resources load and using requestAnimationFrame for smooth animation. Overall, it serves as a tutorial for beginners on building graphics and interactive content using the HTML5 canvas element.
This document provides an introduction to basic functions in Photoshop including creating a new document, using tools like the paint brush, text, and move tools, adding layers and layer effects, resizing and rotating images, combining images, cropping, filling, adding borders, and creating a watermark. It explains key Photoshop concepts like layers and recommends reading an additional resource on layers before starting. The document provides instructions and screenshots to guide users through each task.
Similar to Convolutional neural network complete guide (20)
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An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Artificial intelligence (AI) | Definitio
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.