cs247 slides

Jan 18, 2012Download as PPT, PDF1 like216 views

The document provides an overview of the Kinect SDK and how to work with Kinect data in 12 slides or less. It discusses setting up the Kinect runtime, handling event-driven data streams for depth, color, and skeleton frames. Depth and skeleton frames contain raw byte data that needs to be processed to extract useful information like joint positions. The Coding4Fun library provides helpful methods for mapping skeleton joint coordinates to pixel coordinates on the screen.

Kinect SDK Crash Course (In 12 slides or less) Elliot Babchick

Important! Kinect shines brightest when you use it in a wide, open space. Specifically, 0.8 - 4 meters is the supported range. The sweet spot is in the middle (~2.5 m). If it can��t see your entire body, it can��t track you. Make sure your entire body is in the frame!

Setting it Up We did this for you in the skeleton code, but quickly: Import it into your project via references include ��using Microsoft.Research.Kinect.Nui�� (NUI = Natural User Interface) The Nui is declared a ��Runtime�� object, pass it the sensors you want using RuntimeOptions and pipes (��|��). You must specify these up-front (no asking for them after initialized). nui = new Runtime (); try { nui.Initialize( RuntimeOptions .UseDepthAndPlayerIndex | RuntimeOptions .UseSkeletalTracking | RuntimeOptions .UseColor); } catch (InvalidOperationException) { return 42 ; }

$Event-driven data streams An example handler, taking the RGB video and putting it into a WPF element named ��video�� (really an image) nui.DepthFrameReady += new EventHandler < ImageFrameReadyEventArgs > (nui_DepthFrameReady); nui.SkeletonFrameReady += new EventHandler<SkeletonFrameReadyEventArgs> (nui_SkeletonFrameReady); nui.VideoFrameReady += new EventHandler<ImageFrameReadyEventArgs> (nui_ColorFrameReady); void nui_ColorFrameReady( object sender, ImageFrameReadyEventArgs e) { PlanarImage Image = e.ImageFrame.Image; video.Source = BitmapSource .Create( Image.Width, Image.Height, 96, 96, PixelFormats .Bgr32, null , Image.Bits, Image.Width * Image.BytesPerPixel); }$

What��s In A... ImageFrame ImageFrame We��ll cover this in more detail next week. For now, just know that you have access to the raw bytes (misnamed ��bits��) that makes up the pixels

What��s in a... DepthFrame DepthFrame Look familiar? It��s the same ImageFrame, but has a different Type field value (it��s a depth image, not a color image)

Making (quick) sense of a depth image Raw data in ImageFrame.Image.Bits Array of bytes: public byte[] Bits; 2 bytes per pixel, moves left to right then top to bottom Every 2 bytes tells how far away that particular pixel is (in millimeters). But you can��t just read the bytes straight out... You need to bit-shift differently depending on whether you��re tracking depth and skeletons or just depth... more on this next week, see the link for more detail if you need it sooner: http://channel9.msdn.com/Series/KinectSDKQuickstarts/Working-with-Depth-Data

What��s In A... SkeletonFrame SkeletonFrame A collection of skeletons, each with a collection of joints

Skeleton Data In Detail You get all the joints you see above with <x,y,z>. Z values get larger as you move away from the sensor. Moving right (your right) gives you larger X values. Moving up is left to you as an exercise (get it?). Units in meters (note that raw depth was in millimeters)

Mapping coordinates to the UI Coding4Fun Library extends the Joint object with: ScaleTo(int x, int y, float maxSkeletonX, float maxSkeleton y) x and y describe the rectangular space of pixels you��d like to scale a joint to. The second two arguments specify how far you need to move to traverse the scaled range. For example, skeleton.Joints[JointID.HandRight].ScaleTo(640, 480, .5f, .5f) means that your right hand will only need to travel one meter (-.5 to .5) to cover the full 640-pixel-wide distance on screen. Convenient function for converting ImageFrame��s byte data to actual images: ImageFrame.ToBitmapSource() Find Coding4Fun here (but it��s already in the starter code project): http://c4fkinect.codeplex.com/

This is �ݺ�ߣ #12 I had a slide to spare. Now let��s look at the skeleton code.

This document discusses deep neural networks and computational graphs. It begins by explaining key concepts like derivatives, partial derivatives, optimization, training sets, and activation functions. It then provides examples of applying the chain rule in deep learning, including forward and back propagation in a neural network. Specifically, it demonstrates forward propagation through a simple network and calculating the gradient using backpropagation and the chain rule. Finally, it works through an example applying these concepts to a neural network using sigmoid activation functions.

Image Processing using Matlab ( using a built in Matlab function(Histogram eq...Majd Khaleel

The document summarizes three digital image processing techniques demonstrated in MATLAB: 1. Histogram equalization was used to increase contrast in an image by redistributing intensity values. This made low contrast images appear lighter. 2. Gamma correction was used to make images lighter or darker by adjusting the gamma value in the imadjust function. A gamma less than 1 lightened images, while a gamma greater than 1 darkened images. 3. Spatial filtering was performed using a filter mask to lighten images by increasing or decreasing values in the filter. This inverse of gamma correction, with higher filter values lightening images more.

Class[5][9th jul] [three js-meshes_geometries_and_primitives]Saajid Akram

The document provides an overview of 3D geometric primitives and meshes in Three.js. It discusses various geometry types like BoxGeometry, SphereGeometry, CylinderGeometry, and their constructors. It also covers coordinate systems and basic 3D components like points, lines, and triangles. The document is from a lecture on Three.js meshes, geometries, and primitives, with code examples for creating different geometric shapes.

Pointer Events in Canvasdeanhudson

This document discusses game loop architecture and basic game entities in HTML5 games. It describes setting up a requestAnimationFrame loop to call update and draw on game entities each frame. A simple GameEntity class is provided to manage position and respond to update calls. A Rect class is presented as a basic drawing primitive. Handling of mouse/touch events via normalization and containment checks is outlined. Pixel manipulation with ImageData is demonstrated for effects and hit detection.

Image secret sharing using Shamir's AlgorithmNikita Kasar

This document discusses image secret sharing, which is a technique to securely distribute a secret image among multiple participants. It describes Shamir's secret sharing algorithm, which divides a secret image into shadow images using polynomial interpolation. At least k shadow images are needed to reconstruct the original secret image. The document outlines the steps to generate and distribute shadow images and then recover the secret image. It also covers advantages like robustness and flexibility, disadvantages like potential information leakage, and applications like secure storage and password generation.

Paper on Virtual inputs for ComputerIRJET Journal

This document describes a project to create virtual keyboard and touchpad inputs for a computer using image processing and MATLAB. A virtual keyboard is projected onto a surface and a camera is used to detect finger positions and key presses. When a finger cuts the infrared beam projected on the surface, its position is detected and the corresponding key press is determined. MATLAB tools are used to acquire camera frames, detect objects in the images, and map finger positions to keyboard inputs or mouse controls. The project aims to develop a more portable and user-friendly alternative to physical keyboards and mice.

ACM Mid-Southeast �ݺ�ߣskrinchan

Y TilesChristopher Yunker

Chris Yunker developed the Y-Tiles iPhone application, a sliding puzzle game where the objective is to rearrange numbered tiles in order using an empty space. It took 2-3 months to develop and has been downloaded over 2000 times since being submitted to the App Store in March 2009. The game can be played with photo tiles taken or selected by the user and features different board sizes, sound effects, and a shake gesture to restart the puzzle. Tiles move individually based on touch input and push adjacent tiles when rearranging the board order.

Image Processing using Matlab ( using a built in Highboost filtering,averagin...Majd Khaleel

1) The document describes a student's implementation of highboost filtering in MATLAB. The student used an averaging filter to blur an image, then applied a Laplacian filter with a factor of k=3 to the blurred image, making it sharper than the original. 2) Figures 2.1 and 2.2 show the original image, blurred image, and output of applying the code to the blurred image. 3) The student tested different values of the factor k, including k=5, k=1, k=9, and k=18, and found that the best value was k=1.5.

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Variable length key based visualcsandit

This document proposes a variable length key-based visual cryptography scheme for encrypting color images. It involves encrypting the original image using a variable length symmetric key, generating shares of the encrypted image using random numbers, and decrypting the shares by stacking a minimum number of shares and applying the same encryption key. The proposed scheme aims to improve security over simple visual cryptography by using an encryption key and random number-based share generation, making the shares and encrypted image different from the original image. It is described as having less complex share generation than other techniques while providing security through the use of encryption and a variable length key.

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We rely on secrets such as safe combinations, PIN codes, computer passwords, etc. But so many things happen to lose secretes. 1. Secrets can be lost. 2. Documents get destroyed. 3. Hard disks fail 4. People forget 5. People leave companies, 6. people die... One way to avoid such problems :- Divide secret data (D) in to pieces (n) * Knowledge of some pieces (k) enables to derive secret data (D) * Knowledge of any pieces (k-1) makes secret data (D) completely undetermined. Such a scheme is called a (k, n) threshold scheme. This presentation provides a in depth view of Shamir's Secret Sharing Scheme.

ReportVartika Sharma

This document summarizes an internship report on image analysis of SEM images. It discusses various image processing and analysis techniques used for SEM images, including: - Converting RGB images to grayscale and binary images - Segmentation techniques like thresholding, clustering, watershed segmentation, and quick shift segmentation - Introduction to graphs and Markov chain Monte Carlo methods like the Swendsen Wang method

Skeltrack: A Free Software library for skeleton tracking (LinuxTag 2012)Igalia

By Joaquim Rocha. With the release of the Kinect device, there was finally an affordable camera capable of giving depth information. This, together with the Kinect's open USB connection, led to a lot of innovative projects. Still, the Kinect just gives raw signals and the only way to obtain more complex information, such as skeleton tracking was to use either the Microsoft SDK or the OpenNI framework. Both of these solutions are closed, proprietary and, in the case of Microsoft's, only for non-commercial work. To solve the issue above, Igalia developed Skeltrack, a Free and Open Source library published under LGPL that performs human skeleton tracking and identifies a number of skeleton joints. It is a more atomic solution than the other commercial counterparts because it does not connect directly to the Kinect nor to any other depth camera, instead it expects to be given the buffer corresponding to the depth buffer. In this talk I will present how Skeltrack was developed and show a demo of it working.

Ground water prediction screenshotsVenkat Projects

This document describes a project to predict ground water levels using a deep convolutional neural network (DCNN) algorithm. The DCNN model will be trained on a ground water level dataset containing variables like year, month, rainfall, temperature, and recorded water levels. The trained model can then be used to predict water levels for new test data containing rainfall and temperature values. The document walks through uploading the dataset, generating training and test splits, running random forest and DCNN algorithms for comparison, and using the DCNN model to predict water levels for test data. The DCNN achieved 70% accuracy compared to 20% for random forest, demonstrating the potential of deep learning for this ground water prediction task.

[PR12] PR-036 Learning to Remember Rare EventsTaegyun Jeon

This document summarizes a paper on learning to remember rare events using a memory-augmented neural network. The paper proposes a memory module that stores examples from previous tasks to help learn new rare tasks from only a single example. The memory module is trained end-to-end with the neural network on two tasks: one-shot learning on Omniglot characters and machine translation of rare words. The implementation uses a TensorFlow memory module that stores key-value pairs to retrieve examples similar to a query. Experiments show the memory module improves one-shot learning performance and handles rare words better than baselines.

A Novel Method for Image Watermarking Using Luminance Based Block Selection a...IJERA Editor

A robust watermark scheme for copyright protection is proposed in the present paper. The present method selects the pixel locations to insert the watermark by checking luminance [1] values of blocks. The watermark is embedded in the selected pixel blocks by using local area pixel value difference method. The proposed approach overcomes the weak robustness problem of embedding the watermark in the spatial domain and also in pixel value difference method. Further the watermark extraction does not require the original image as in the case of many digital watermarking methods. The experimental results indicate the high image quality and robustness against various attacks when compared to several approaches.

A multilevel automatic thresholding method based on a genetic algorithm for a...Akshit Arora

This document summarizes a project to implement a multilevel automatic thresholding method for image segmentation based on a genetic algorithm. The method uses a genetic algorithm to initialize population of threshold values, applies genetic operators like selection, crossover and mutation to improve the threshold values at each generation, and refines the best thresholds to segment the image. The project studied this thresholding method from a research paper and implemented it in MATLAB to segment images using a genetic algorithm approach to optimization of threshold values.

IRJET- 3D Vision System using Calibrated Stereo CameraIRJET Journal

This document describes a 3D vision system that uses calibrated stereo cameras to estimate the depth of objects. It discusses using two digital cameras placed at different positions to capture images of the same object. Feature matching and disparity calculation algorithms are used to calculate depth based on the difference between images. The cameras are calibrated using camera parameters derived from images of a checkerboard pattern. Trigonometry formulas are then used to calculate depth based on the camera positions and disparity. A servo system is used to independently and synchronously move the cameras along the x and y axes to capture views of objects from different angles.

HYBRIDIZATION OF DCT BASED STEGANOGRAPHY AND RANDOM GRIDSIJNSA Journal

The document discusses a hybrid approach to steganography and visual cryptography for improved data security. It proposes combining principles of steganography, which hides data in a cover media, and visual cryptography, which encrypts images in a way that can be decrypted by human vision without algorithms. Specifically, it describes generating two random grids from a secret image that reveal the image when overlaid but hide it individually. The random grids are created by inverting or substituting pixels based on the secret image. This hybrid approach aims to provide stronger security than either technique alone by incorporating advantages of both.

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This new algorithm mixes two or more images of different types and sizes by employing a shuffling procedure combined with S-box substitution to perform lossless image encryption. This combines stream cipher with block cipher, on the byte level, in mixing the images. When this algorithm was implemented, empirical analysis using test images of different types and sizes showed that it is effective and resistant to attacks.

Build Your Own 3D Scanner: 3D Scanning with Structured LightingDouglas Lanman

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This document provides an overview of the Kinect sensor and Kinect for Windows SDK. It describes the Kinect sensor's capabilities including depth sensing, skeletal tracking, and speech recognition. It explains how the Kinect SDK allows accessing the sensor's data streams and provides APIs for tasks like skeletal tracking and speech recognition. The document also outlines the tools included in the SDK and provides code examples for initializing the sensor, accessing sensor data, and using speech recognition features.

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Telemedicine app development builds software for remote doctor consultations and patient check-ups. These apps bridge healthcare professionals with patients via video calls, secure messages, and interactive interfaces. That helps practitioners to provide care without immediate face-to-face interactions; hence, simplifying access to medical care. Telemedicine applications also manage appointment scheduling, e-prescribing, and sending reminders. Telemedicine apps do not only conduct remote consultations. They also integrate with entire healthcare platforms, such as patient forums, insurance claims processing, and providing medical information libraries. Remote patient monitoring enables providers to keep track of patients' vital signs. This helps them intervene and provide care whenever necessary. Telehealth app development eliminates geographical boundaries and facilitates easier communication. In this blog, we will explore its market growth, essential features, and benefits for both patients and providers.

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