ECE 172A: Introduction to Intelligent Systems: Machine Vision, Fall Midterm Examination

Size: px

Start display at page:

Download "ECE 172A: Introduction to Intelligent Systems: Machine Vision, Fall Midterm Examination"

Magdalen O’Connor’
6 years ago
Views:

1 ECE 172A: Introduction to Intelligent Systems: Machine Vision, Fall 2008 October 29, 2008 Notes: Midterm Examination This is a closed book and closed notes examination. Please be precise and to the point. Please show all relevant details of your work. State any and all assumptions clearly. Good luck. Problem 1 [Total Points 25]: Part A [10 Points]: (i) What is meant by an Intelligent (or Smart) Environment? How is it similar or dissimilar to an Intelligent Robot? (ii) How is a Sensor-based intelligent robot different from a Tele-operated robot and a Pre-programmed robot? (iii) In the UCSD Intelligent Meeting Room paper (discussed in the IEEE-Trans. SMC 2005), the authors emphasize representation and analysis of information at multiple levels of abstraction. In that system, (a) What is the lowest level abstraction of information considered? (b) Define Objects, Events, and Context as described in the paper. (i) Intelligent Environments are spaces equipped with sensors and computational capabilities to: Develop and maintain awareness of events Adapt to the dynamic changes in their surroundings Interact in a natural, efficient and flexible manner with the users They are similar to autonomous, sensor-based robots in perception and task-planning. The difference lies in the motor aspect of the robot. (ii) Sensor based intelligent robot uses its own sensors to acquire and process information about its surroundings and is capable of autonomous action. Tele-operated robots on the other hand are operated from a remote location by a human controller and Pre-programmed robots are capable of repeating a pre-determined set of actions. In other words they have limited or no autonomy. (iii) (a) Signal Level (b) Object: This is a pattern defined in the spatial domain. Examples of such objects would be a person or face. Event: This is a pattern defined in the spatial-temporal domain. Examples of events can be a person entering leaving a room, or a person speaking. Context: This is considered to be a specification of the state of an intelligent environment. It is defined using prior knowledge of the environment and tasks. events detected from sensory information would cause changes in the state of the system. ECE 172A Midterm, October 29th,

2 Part B [15 points] Identify at least three specific computer vision tasks, (for each of the following three vision systems). You need to briefly describe the nature of the input, output and appropriate processing/analysis, involved in the tasks you are identifying (i) ROBOSIGHT The system starts its operation by first locating the panel. This is accomplished by placing the camera, detecting the lights on the corner of the panel, and using camera calibration and 2-D to 3-D transformation techniques to determine the panel pose with respect to the robot coordinates. Next, the system positions the camera to take the images of left and right portions of the panel. Image segmentation is based upon the region growing procedure and object recognition is based upon the graph matching algorithm. The system then enters the inspection and manipulation cycle. It is achieved by reading and decoding the LCD digital meter, and performing the command decoded accordingly, in a cyclic fashion. Edge detection and thinning routines were employed to obtain the one-pixel wide edge maps of the numeric code. The edge maps were later processed using Fourier shape descriptors to identify the numeric code accurately (ii) Automatic Spill Cleaning Robot Spill Detection: the image of the spill is acquired by the perception module. The Perception module computes the histogram of the image and performs a histogram analysis based on a minimum error method to select an optimal threshold value for image binarization. The image is then segmented (binarized) using this optimal threshold and the contour of the segmented image is then traced. Spill 3D localization: The image to world transformation procedure is performed after the Perception module has detected the spills. This procedure computes the actual spill location in the robot coordinate frame using the contour points of the spill regions in the image. The 3-D location and the area of the spill in robot and room reference frames are calculated based on the contour points. Clean-up Verification: Similar to spill detection, but no spill should be detected. (iii) Intelligent Meeting Room Person tracking using omni-video array tracker: Silhouettes of people are detected by background subtraction and tracked using Kalman filter framework. The output is the 3D location estimates for each person in the scene. Head Tracking and Face Capture: Results of the tracker are used to control the face capture module. One ODVS in the array may be picked and captured in full-frame to capture the face. Single-Frame Face Detection and Recognition: The captured video is then processed to detect the face and extract the face video, as shown in Fig. 12. From the head tracking output, skin tone segmentation is first used to find the face candidates. Possible face images are cropped from the skin tone blobs. Those images are then classified to reject nonfaces. A simple eigenface, or principle component analysis (PCA) method is used for both face classification and single-frame face recognition. Also acceptable are the following answers Face-Orientation Estimation: Streaming Face Recognition: Body Modeling and Movement Analysis: ECE 172A Midterm, October 29th,

3 Problem 2 [25 Points]: Part A [10 Points]: Discuss and describe the relationship between the fields of Image Processing and Computer Vision. Discuss and describe the relationship between the fields of Computer Graphics and Computer Vision. (Please use simple block diagrams with inputs, outputs, and typical processing tasks) ECE 172A Midterm, October 29th,

4 Part B [15 Points]: Explain and discuss the following terms, give examples where appropriate in your explanation. (i) (ii) (iii) (iv) (v) (vi) (vii) Low level and High level vision. Hierarchy of Computer Vision Tasks Model-based Vision External and Internal Sensors for Robots CCD and CMOS Camera arrays Histogram Equalization Opening and Closing Morphological Operations (i) (ii) (iii) Model based computer vision involves the encapsulation of prior knowledge about the scene and objects into tractable models. These models are then used to detect and recognize particular objects/events in the scene by choosing the model that best explains the particular scene. ECE 172A Midterm, October 29th,

5 (iv) External sensors : Provide information about the surroundings of the robot. Internal sensors : Provide information about the state of the robot, that is the information about its joint angles, positions of its limbs etc. (v) CCD (charge-coupled device) most common high sensitivity high power blooming CMOS simple to fabricate (cheap) lower sensitivity, lower power can be individually addressed In applications requiring very high resolution, low light and low noise, use CCD's. Otherwise, a CMOS imager has the flexibility to solve most applications at lower total costs including the highest speed solutions. (vi) Objective: Flatten the histogram, so each gray level occurs with equal probability Application: When we are processing a large number of images, we want to minimize the variability from one image to the next (vii) The opening of A by B, is given by the erosion by B, followed by the dilation by B. Opening is like `rounding from the inside': the opening of A by B is obtained by taking the union of all translates of B that fit inside A. Parts of A that are smaller than B are removed. Closing is the dual operation of opening. It is produced by the dilation of A by B, followed by the erosion by B. This is like `smoothing from the outside'. Holes are filled in and narrow valleys are `closed'. ECE 172A Midterm, October 29th,

6 Problem 3: [10 Points] Apply the following code to the given binary image, where a white square = 1 and a black square = 0. Draw img2. What does this code do? % img is already stored in the variable img A = [0 1 0; 1 1 1; 0 1 0]; B = [1 1 1; 1 1 1; 1 1 1]; [height width] = size(img); img2=img; for i=2:width-1 for j=2:height-1 if (sum(sum(img(j-1:j+1,i-1:i+1).*a)) == 5) img2(j-1:j+1,i-1:i+1)=b; end end end The code examines each pixel and determines if it is a white pixel with all 4- neighbors that are also white. When it encounters a white pixel with all white 4- neighbors, it makes the corresponding pixel in the new image have all white 8-neighbors. ECE 172A Midterm, October 29th,

7 Problem 4: [10 Points] For the image given below, apply the following code and sketch the output. % img is already stored in the variable A B = [1 1 1; 1 1 1; 1 1 1]; imshow(imerode(a,b),'initialmagnification','fit'); ECE 172A Midterm, October 29th,

Problem 5: [15 Points] An image and its associated histogram are given below. a) [3 Points] What is the range of pixel values in this image?

c) [3 Points] Describe in words or with the help of a sketch, the likely output of the following Matlab code, assuming that the image is stored in I?

a) [0 255] b) ~[150 200] c) The car and the darker tree will be white and the rest of the image will be black.

8 Problem 5: [15 Points] An image and its associated histogram are given below. a) [3 Points] What is the range of pixel values in this image? b) [5 Points] From the histogram, deduce the most likely range of values for the pixels representing the road? c) [3 Points] Describe in words or with the help of a sketch, the likely output of the following Matlab code, assuming that the image is stored in I? >>B = 255*double(I <100); >> imshow(b,[0 255]); d) [4 Points]Sketch the histogram of B if sum(b)/255 = 100,000 and size(b) = 500x500 =250,000 pixels? a) [0 255] b) ~[ ] c) The car and the darker tree will be white and the rest of the image will be black. It s a quick way to segment the car from the rest of the image. It will be noisy. d) Note that B is a binary images with pixels values either 255 or 0. Thus the histogram of B will have only two non zero bins and the number of 255 s in B is given to be 100,000. The rest are zeros. The histogram : ECE 172A Midterm, October 29th,

9 Problem 6: [15 Points] An image and a convolution mask are shown below. The origin is assumed to be at the center of the mask. Image A Mask M a) [5 Points] What is this specific mask/operator called? Describe in words, the purpose of using such a mask. b) [5 Points] What is the result of the 2D convolution of image A with the mask? Assume zero padding around A, wherever necessary, so that your answer will be an image of same dimensions as A. c) [5 Points] Specify a 2x2 vertical-edge detector mask in a form similar to M. a) Sobel gradient operator. It is used to estimate the gradient of the pixel values along a column of the image. b) c) 1-1 or -1 1 Note that 1 0 is also valid but is not specific to vertical edges ECE 172A Midterm, October 29th,

ENG 7854 / 9804 Industrial Machine Vision. Midterm Exam March 1, 2010.

ENG 7854 / 9804 Industrial Machine Vision Midterm Exam March 1, 2010. Instructions: a) The duration of this exam is 50 minutes (10 minutes per question). b) Answer all five questions in the space provided.