object-recognition

While working with the kinect I found out, that the bitmap and its depth information are unreliable and for some reason much more disturbed than the data from the actual byte array. I realised this when I tried get the min and max by accessing the bitmap like this for (var y = 0; y < height; y++) { var heightOffset = y * width; for (var x = 0; x < width; x++) { var index = ((width - x - 1) + heightOffset) * 4; var distance = GetDistance(depth[depthIndex], depth[depthIndex + 1]); But on the other hand I achieved much better results when I directly accessed the depth byte array (as a

I have a toy car that has an arduino and 4 Ultrasonic Range Finder sensors in front, rear and both sides, they give me the distance to a wall if any exist within the range that the sensor covers, the sensors give me this data in short intervals, the car has to move around a room and plot the outline, the rooms can have columns and be kind of irregular, I don't know the algorithms involved in this plotting, any insight would be appreciated. heh once did a robot that was almost the same :) bud it could turn around at one place (no turning radius) so it was easy to drive and map environment in

I am trying to make an automator tool and am experimenting with a type of recording which takes screen shots and records user inputs. The idea would be for user to take a snapshot and and highlight a square on the snapshot of the "submit" button. During playback, the program would take a sceenshot of the open window, and find the coordinates of the button by searching for the snapshot. So I need an algorithm to search an image for an exact (or very close) image of the button. The algorithms I've found so far compare image likeness but cannot find it in a subimage, and algorithms for object

What are moments of a contour? Could someone explain this in simplistic, non-mathematical terms? Possibly with an example? The official explanation is "integration over all the pixels in a contour". I have no idea what integration. And also what can contour moments be used for? specific examples please Thanks I saw a great explanation somewhere but do not have the link. Anyway, moment of 0th degree for a black/white image with black=0 and white=1: this is simply the sum of the pixels, i.e. the number of white pixels. Moment of 1st degree for x-axis and some particular point X on the x-axis:

I'm implementing Bag Of Words in opencv by using SIFT features in order to make a classification for a specific dataset. So far, I have been apple to cluster the descriptors and generate the vocabulary. As I know, I have to train SVM ... but i have some questions which i'm really confused about them. The major problem is the concept behind the implementations, these are my questions: 1- When I extract the features and then create the vocabulary, shall I extract the features for all the objects (let's say 5 objects)and put them in one file, so I make all of them in a one vocabulary file that

I am using FFmpeg to overlay image/emoji on video by this command - "-i "+inputfilePath+" -filter_complex "+"[0][1]overlay=enable='between(t,"+startTime+","+endTime+")'[v1]"+" -map [v0] -map 0:a "+OutputfilePath; But above command only overlay image over video and stays still. In Instagram and Snapchat there is New pin feature. I want exactly same ,eg blur on moving faces or as in below videos - Here is link . Is it possible via FFmpeg ? I think someone with OPENCV or Argumented Reality knowledge can help in this. It is quiet similar to AR as we need to move/zoom emoji exactly where we want to

I am trying to write a program using opencv to calculate the distance from a webcam to a one inch white sphere. I feel like this should be pretty easy, but for whatever reason I'm drawing a blank. Thanks for the help ahead of time. Adi Shavit You can use triangle similarity to calibrate the camera angle and find the distance. You know your ball's size: D units (e.g. cm). Place it at a known distance Z , say 1 meter = 100cm, in front of the camera and measure its apparent width in pixels. Call this width d . The focal length of the camera f (which is slightly different from camera to camera) is