Search Results

Search found 19365 results on 775 pages for 'machine vision'.

Page 44/775 | < Previous Page | 40 41 42 43 44 45 46 47 48 49 50 51  | Next Page >

  • Image Erosion for face detection in C#

    - by Chris Dobinson
    Hi, I'm trying to implement face detection in C#. I currently have a black + white outline of a photo with a face within it (Here). However i'm now trying to remove the noise and then dilate the image in order to improve reliability when i implement the detection. The method I have so far is here: unsafe public Image Process(Image input) { Bitmap bmp = (Bitmap)input; Bitmap bmpSrc = (Bitmap)input; BitmapData bmData = bmp.LockBits(new Rectangle(0, 0, bmp.Width, bmp.Height), ImageLockMode.ReadWrite, PixelFormat.Format24bppRgb); int stride = bmData.Stride; int stride2 = bmData.Stride * 2; IntPtr Scan0 = bmData.Scan0; byte* p = (byte*)(void*)Scan0; int nOffset = stride - bmp.Width * 3; int nWidth = bmp.Width - 2; int nHeight = bmp.Height - 2; var w = bmp.Width; var h = bmp.Height; var rp = p; var empty = CompareEmptyColor; byte c, cm; int i = 0; // Erode every pixel for (int y = 0; y < h; y++) { for (int x = 0; x < w; x++, i++) { // Middle pixel cm = p[y * w + x]; if (cm == empty) { continue; } // Row 0 // Left pixel if (x - 2 > 0 && y - 2 > 0) { c = p[(y - 2) * w + (x - 2)]; if (c == empty) { continue; } } // Middle left pixel if (x - 1 > 0 && y - 2 > 0) { c = p[(y - 2) * w + (x - 1)]; if (c == empty) { continue; } } if (y - 2 > 0) { c = p[(y - 2) * w + x]; if (c == empty) { continue; } } if (x + 1 < w && y - 2 > 0) { c = p[(y - 2) * w + (x + 1)]; if (c == empty) { continue; } } if (x + 2 < w && y - 2 > 0) { c = p[(y - 2) * w + (x + 2)]; if (c == empty) { continue; } } // Row 1 // Left pixel if (x - 2 > 0 && y - 1 > 0) { c = p[(y - 1) * w + (x - 2)]; if (c == empty) { continue; } } if (x - 1 > 0 && y - 1 > 0) { c = p[(y - 1) * w + (x - 1)]; if (c == empty) { continue; } } if (y - 1 > 0) { c = p[(y - 1) * w + x]; if (c == empty) { continue; } } if (x + 1 < w && y - 1 > 0) { c = p[(y - 1) * w + (x + 1)]; if (c == empty) { continue; } } if (x + 2 < w && y - 1 > 0) { c = p[(y - 1) * w + (x + 2)]; if (c == empty) { continue; } } // Row 2 if (x - 2 > 0) { c = p[y * w + (x - 2)]; if (c == empty) { continue; } } if (x - 1 > 0) { c = p[y * w + (x - 1)]; if (c == empty) { continue; } } if (x + 1 < w) { c = p[y * w + (x + 1)]; if (c == empty) { continue; } } if (x + 2 < w) { c = p[y * w + (x + 2)]; if (c == empty) { continue; } } // Row 3 if (x - 2 > 0 && y + 1 < h) { c = p[(y + 1) * w + (x - 2)]; if (c == empty) { continue; } } if (x - 1 > 0 && y + 1 < h) { c = p[(y + 1) * w + (x - 1)]; if (c == empty) { continue; } } if (y + 1 < h) { c = p[(y + 1) * w + x]; if (c == empty) { continue; } } if (x + 1 < w && y + 1 < h) { c = p[(y + 1) * w + (x + 1)]; if (c == empty) { continue; } } if (x + 2 < w && y + 1 < h) { c = p[(y + 1) * w + (x + 2)]; if (c == empty) { continue; } } // Row 4 if (x - 2 > 0 && y + 2 < h) { c = p[(y + 2) * w + (x - 2)]; if (c == empty) { continue; } } if (x - 1 > 0 && y + 2 < h) { c = p[(y + 2) * w + (x - 1)]; if (c == empty) { continue; } } if (y + 2 < h) { c = p[(y + 2) * w + x]; if (c == empty) { continue; } } if (x + 1 < w && y + 2 < h) { c = p[(y + 2) * w + (x + 1)]; if (c == empty) { continue; } } if (x + 2 < w && y + 2 < h) { c = p[(y + 2) * w + (x + 2)]; if (c == empty) { continue; } } // If all neighboring pixels are processed // it's clear that the current pixel is not a boundary pixel. rp[i] = cm; } } bmpSrc.UnlockBits(bmData); return bmpSrc; } As I understand it, in order to erode the image (and remove the noise), we need to check each pixel to see if it's surrounding pixels are black, and if so, then it is a border pixel and we need not keep it, which i believe my code does, so it is beyond me why it doesn't work. Any help or pointers would be greatly appreciated Thanks, Chris

    Read the article

  • Image Recognition (Shape recognition)

    - by mqpasta
    I want to recognize the shapes in the picture by template matching.Is the "ExhaustiveTemplateMatching" is the right option given in Aforge.Net for this purpose.Had anyone tried this class and find it working correctly.How accurate and right choice this class is for achieving my purpose.Suggest any other methods or Alogrithms as well for recognizing shapes by matching template.For example Identifying ComboBox in a picture.

    Read the article

  • image focus calculation

    - by Oren Mazor
    Hi folks, I'm trying to develop an image focusing algorithm for some test automation work. I've chosen to use AForge.net, since it seems like a nice mature .net friendly system. Unfortunately, I can't seem to find information on building autofocus algorithms from scratch, so I've given it my best try: take image. apply sobel edge detection filter, which generates a greyscale edge outline. generate a histogram and save the standard dev. move camera one step closer to subject and take another picture. if the standard dev is smaller than previous one, we're getting more in focus. otherwise, we've past the optimal distance to be taking pictures. is there a better way? update: HUGE flaw in this, by the way. as I get past the optimal focus point, my "image in focus" value continues growing. you'd expect a parabolic-ish function looking at distance/focus-value, but in reality you get something that's more logarithmic

    Read the article

  • How to begin with augmented reality?

    - by Terri
    I'm currently an undergrad in computer science and I'll be entering my final year next year. Augmented reality is something I find to be a really interesting topic, but I have no idea where to start learning about it. Where do you start learning about this topic and what libraries are available?

    Read the article

  • Face recognition Library

    - by Janusz
    I'm looking for a free face recognition library for a university project. I'm not looking for face detection. I'm looking for actual recognition. That means finding images that contain specified faces or libraries that calculate distances between specific faces. I'm using OpenCV for detecting the faces and a rough Eigenfaces Algorithm for the recognition now. But I thought there should be something out there with a better performance then a self written Eigenfaces Algorithm. I don't talk about speed as performance I'm looking for a library with better results as an simple Eigenfaces approach I took a look at faint but it seems the library is not very reusable for my own applications. I'm happy with a library in Python, Java, C++, C or something like that. The best thing would be if it can be run on a Windowsmachine

    Read the article

  • Marker Recognition on Android (recognising Rubik's Cubes)

    - by greenie
    Hi everybody. I'm developing an augmented reality application for Android that uses the phone's camera to recognise the arrangement of the coloured squares on each face of a Rubik's Cube. One thing that I am unsure about is how exactly I would go about detecting and recognising the coloured squares on each face of the cube. If you look at a Rubik's Cube then you can see that each square is one of six possible colours with a thin black border. This lead me to think that it should be relativly simply to detect a square, possibly using an existing marker detection API. My question is really, has anybody here had any experience with image recognition and Android? Ideally I'd like to be able to implement and existing API, but it would be an interesting project to do from scratch if somebody could point me in the right direction to get started. Many thanks in advance.

    Read the article

  • How to begin with augmented reality/compvision?

    - by Terri
    I'm currently an undergrad in computer science and I'll be entering my final year next year. Augmented reality is something I find to be a really interesting topic, but I have no idea where to start learning about it. Where do you start learning about this topic and what libraries are available?

    Read the article

  • OpenCV - DLL missing, but it's not?

    - by charles-22
    I am trying just a basic program with OpenCV with the following code: #include "cv.h" #include "highgui.h" int main() { IplImage* newImg; newImg = cvLoadImage("~/apple.bmp", 1); cvNamedWindow("Window", 1); cvShowImage("Window", newImg); cvWaitKey(0); cvDestroyWindow("Window"); cvReleaseImage(&newImg); return 0; } When I run this, I get The program can't start because libcxcore200.dll is missing from your computer. Try reinstalling the program to fix this problem. However, I can see this DLL. It exists. I have added the following to the input dependencies for my linker C:\OpenCV2.0\lib\libcv200.dll.a C:\OpenCV2.0\lib\libcvaux200.dll.a C:\OpenCV2.0\lib\libcxcore200.dll.a C:\OpenCV2.0\lib\libhighgui200.dll.a What gives? I'm using visual studio 2008.

    Read the article

  • Metric 3d reconstruction

    - by srand
    I'm trying to reconstruct 3D points from 2D image correspondences. My camera is calibrated. The test images are of a checkered cube and correspondences are hand picked. Radial distortion is removed. After triangulation the construction seems to be wrong however. The X and Y values seem to be correct, but the Z values are about the same and do not differentiate along the cube. The 3D points look like as if the points were flattened along the Z-axis. What is going wrong in the Z values? Do the points need to be normalized or changed from image coordinates at any point, say before the fundamental matrix is computed? (If this is too vague I can explain my general process or elaborate on parts)

    Read the article

  • Automatic camera calibration

    - by srand
    From Wikipedia, camera resectioning is the process of finding the true parameters of the camera that produced a given photograph or video. Camera resectioning is also known as geometric camera calibration. Currently I am using Camera Calibration Toolbox for Matlab for my camera calibration. The toolbox returns calibration parameters such as focal length, principle point, skew, and distortion. However, the issue with this method is that it requires an extra step in calibrating the camera by using a special calibration object like a checkerboard. Additionally, it only works for one focus of the camera. How can I get the calibration parameters without manually calibrating? For example, how does Microsoft's Photosynth perform camera calibration on its images?

    Read the article

  • Stereo Matching - Dynamic Programming

    - by Varun
    Hi, I am supposed to implement Dynamic programming algorithm for Stereo matching problem. I have read 2 research papers but still haven't understood as to how do I write my own c++ program for that ! Is there any book or resource that's available somewhere that I can use to get an idea as to how to start coding actually ? Internet search only gives me journal and conference papers regarding Dynamic Programming but not how to implement the algorithm step by step. Thanks Varun

    Read the article

  • Writing a VM - well formed bytecode?

    - by David Titarenco
    Hi, I'm writing a virtual machine in C just for fun. Lame, I know, but luckily I'm on SO so hopefully no one will make fun :) I wrote a really quick'n'dirty VM that reads lines of (my own) ASM and does stuff. Right now, I only have 3 instructions: add, jmp, end. All is well and it's actually pretty cool being able to feed lines (doing it something like write_line(&prog[1], "jmp", regA, regB, 0); and then running the program: while (machine.code_pointer <= BOUNDS && DONE != true) { run_line(&prog[machine.cp]); } I'm using an opcode lookup table (which may not be efficient but it's elegant) in C and everything seems to be working OK. My question is more of a "best practices" question but I do think there's a correct answer to it. I'm making the VM able to read binary files (storing bytes in unsigned char[]) and execute bytecode. My question is: is it the VM's job to make sure the bytecode is well formed or is it just the compiler's job to make sure the binary file it spits out is well formed? I only ask this because what would happen if someone would edit a binary file and screw stuff up (delete arbitrary parts of it, etc). Clearly, the program would be buggy and probably not functional. Is this even the VM's problem? I'm sure that people much smarter than me have figured out solutions to these problems, I'm just curious what they are!

    Read the article

  • Calculating rotation and translation matrices between two odometry positions for monocular linear triangulation

    - by user1298891
    Recently I've been trying to implement a system to identify and triangulate the 3D position of an object in a robotic system. The general outline of the process goes as follows: Identify the object using SURF matching, from a set of "training" images to the actual live feed from the camera Move/rotate the robot a certain amount Identify the object using SURF again in this new view Now I have: a set of corresponding 2D points (same object from the two different views), two odometry locations (position + orientation), and camera intrinsics (focal length, principal point, etc.) since it's been calibrated beforehand, so I should be able to create the 2 projection matrices and triangulate using a basic linear triangulation method as in Hartley & Zissermann's book Multiple View Geometry, pg. 312. Solve the AX = 0 equation for each of the corresponding 2D points, then take the average In practice, the triangulation only works when there's almost no change in rotation; if the robot even rotates a slight bit while moving (due to e.g. wheel slippage) then the estimate is way off. This also applies for simulation. Since I can only post two hyperlinks, here's a link to a page with images from the simulation (on the map, the red square is simulated robot position and orientation, and the yellow square is estimated position of the object using linear triangulation.) So you can see that the estimate is thrown way off even by a little rotation, as in Position 2 on that page (that was 15 degrees; if I rotate it any more then the estimate is completely off the map), even in a simulated environment where a perfect calibration matrix is known. In a real environment when I actually move around with the robot, it's worse. There aren't any problems with obtaining point correspondences, nor with actually solving the AX = 0 equation once I compute the A matrix, so I figure it probably has to do with how I'm setting up the two camera projection matrices, specifically how I'm calculating the translation and rotation matrices from the position/orientation info I have relative to the world frame. How I'm doing that right now is: Rotation matrix is composed by creating a 1x3 matrix [0, (change in orientation angle), 0] and then converting that to a 3x3 one using OpenCV's Rodrigues function Translation matrix is composed by rotating the two points (start angle) degrees and then subtracting the final position from the initial position, in order to get the robot's straight and lateral movement relative to its starting orientation Which results in the first projection matrix being K [I | 0] and the second being K [R | T], with R and T calculated as described above. Is there anything I'm doing really wrong here? Or could it possibly be some other problem? Any help would be greatly appreciated.

    Read the article

  • minimum enclosing rectangle of fixed aspect ratio

    - by Ramya Narasimha
    I have an Image with many rectangles at different positions in the image and of different sizes (both overlapping and non-overlapping). I also have a non-negative scores associated with each of these rectangles. My problem now is to find one larger rectangle *of a fixed (given) aspect ratio* that encloses as many of these rectangles as possible. I am looking for an algorithm to do this, if anyone has a solution, even a partial one it would be helpful. Please note that the positions of the rectangles in the image is fixed and cannot be moved around and there is no orientation issue as all of them are upright.

    Read the article

  • Good way to identify similar images?

    - by Nick
    I've developed a simple and fast algorithm in PHP to compare images for similarity. Its fast (~40 per second for 800x600 images) to hash and a unoptimised search algorithm can go through 3,000 images in 22 mins comparing each one against the others (3/sec). The basic overview is you get a image, rescale it to 8x8 and then convert those pixels for HSV. The Hue, Saturation and Value are then truncated to 4 bits and it becomes one big hex string. Comparing images basically walks along two strings, and then adds the differences it finds. If the total number is below 64 then its the same image. Different images are usually around 600 - 800. Below 20 and extremely similar. Are there any improvements upon this model I can use? I havent looked at how relevant the different components (hue, saturation and value) are to the comparison. Hue is probably quite important but the others? To speed up searches I could probably split the 4 bits from each part in half, and put the most significant bits first so if they fail the check then the lsb doesnt need to be checked at all. I dont know a efficient way to store bits like that yet still allow them to be searched and compared easily. I've been using a dataset of 3,000 photos (mostly unique) and there havent been any false positives. Its completely immune to resizes and fairly resistant to brightness and contrast changes.

    Read the article

  • Camera and Image recognition

    - by kjh
    I recently watched a youtube video where a guy got a camera to recognize when a rubik's cube was held up to it, and it captured the 9 square color combination before snapping a picture of the cube and displaying the 3x3 grid on the screen of his computer. What kind of programming is this and where would I start reading to get into this sort of thing? specifically, controlling a camera, and getting it to pick out certain parts of an image and translate that data.

    Read the article

  • Merging photo textures - (from calibrated cameras) - projected onto geometry

    - by freakTheMighty
    I am looking for papers/algorithms for merging projected textures onto geometry. To be more specific, given a set of fully calibrated cameras/photographs and geometry, how can we define a metric for choosing which photograph should be used to texture a given patch of the geometry. I can think of a few attributes one may seek minimize including the angle between the surface normal and the camera, the distance of the camera from the surface, as well as minimizing some parameterization of sharpness. The question is how do these things get combined and are there well established existing solutions?

    Read the article

< Previous Page | 40 41 42 43 44 45 46 47 48 49 50 51  | Next Page >