object-recognition

问题 I want to evaluate my model using K-Fold Cross Validation (k=5). This means that dataset must be split in 5 parts: p1,p2,p3,p4,p5 and then: (run1) Test : p1,p2,p3,p4 Eval : p5 (run2) Test : p1,p2,p3,p4 Eval : p4 (run3) Test : p1,p2,p4,p5 Eval : p3 (run4) Test : p1,p3,p4,p5 Eval : p2 (run5) Test : p2,p3,p4,p5 Eval : p1 At the end, I calculate the average mean among all the evaluations. This is essentially K-Fold Cross validation. Right now, what I am doing is to regenerate .tf records each

问题 I want to evaluate my model using K-Fold Cross Validation (k=5). This means that dataset must be split in 5 parts: p1,p2,p3,p4,p5 and then: (run1) Test : p1,p2,p3,p4 Eval : p5 (run2) Test : p1,p2,p3,p4 Eval : p4 (run3) Test : p1,p2,p4,p5 Eval : p3 (run4) Test : p1,p3,p4,p5 Eval : p2 (run5) Test : p2,p3,p4,p5 Eval : p1 At the end, I calculate the average mean among all the evaluations. This is essentially K-Fold Cross validation. Right now, what I am doing is to regenerate .tf records each

问题 I don't know if this the right stackexchange forum where to ask this question, please let me know if this is not the case. I'm developing an application which given an input image containing a painting as input, it is able to tell you the title of the painting. An analogous case is: given an input image containing a building, the returned result is the name of the building. What kind of application is this? On first impact, I would say something like "image classification". I'm not an expert

问题 The code which is available at http://www.cs.berkeley.edu/~rbg/latent/voc-release5.tgz is widely used in object detection. There is a imgdetect function which returns ds , bs and trees . It seems ds contains detection boxes and bs contains which of the filters used for detection responded in the image, while trees contains some information about the part scores. I was not able to find out how to get the part scores using trees and bs . Basically, given a detection box, I would like to find

问题 I'm confused about the way I should make the "features extraction " method I want to use SVMs to apply "Object recognition" in images , There's a sample in Emgu's examples that holds an XML file contains the features of a cat ! and I've been trying since a week to know how they did it and what methods they used and I came across this page http://experienceopencv.blogspot.com/2011/02/learning-deformable-models-with-latent.html that displays the steps ! It's so complicated plus couldn't do it

问题 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

问题 I have implemented Bag Of Words, everything is working smoothly. But, I'm confused about some steps and how to implement it. I could create the bow descriptors as the last step in Bag of words to create the samples, as it shown here bowDE.compute(img, keypoints, bow_descriptor); .. The things is that i'm confused about the next steps. I know that in BOW that I have to train and test a class (car) with non-class (cola), what I created in bow_descriptor vector is only for the class car, so I

问题 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

问题 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. 回答1: 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

问题 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. 回答1: heh once did a robot that was almost the same :)