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Visual code marker detection : A DIP project for engineers

Cameras in mobile phones are no longer used just for taking snap shots. The rapid growth of digital image processing has led to its growth of application in mobile phones too. The cameras in mobile phones can now be used to detect visual code markers in an image and eventually access the information embedded to it. A scenario for such an image processing is when one takes an image of the visual code marker next to the advertisement of a movie. With this visual code marker the mobile phone can decode the image and access the backend database to know the local screening time of the movie. Thus for a camera to detect the visual code marker in an image, it needs an implementation of unique algorithms. These algorithms mostly have the four main stages to ultimately retrieve its data.  :

The main four stages are pre-processing and segmentation, region analysis, registration and thresholding. All these stages are based upon visual code markers, how they are detected and decoded. Visual code markers are two dimensional bar codes which consists of an 11*11 square comprised of black and white bits. Two guide bars and three guide points are made fixed points for its digital detection. Thus out of 121, 83 contains specific information. These are read from top to bottom, right to left.

                      
The first stage pre-processing and segmentation aims at transforming the captured image to a binary image to clearly identify the key elements of a visual code marker. The captured 640*480 RGB image is converted to a 640*480 binary image highlighting the key elements such as guide bars and fixed corners in the code marker. Next the image is enhanced to reduce its noise and smoothen it by applying morphological open-close filters which produces a grey scale image. After all the processing in the first stage the resulting will be a binary image which identifies the key element of the code marker. The next stage does a region analysis where each key elements of code marker are localized and grouped. The short and long guide bars are identified first, and then with its properties the fixed corner regions are found. These region analyses are made either based on intrinsic properties or geometric relationship between two regions. The registration is for correction for scaling and distortion in the image since it can be taken at a variety of distance and angles. If multiple code markers are present then registration is applied individually for each marker. After obtaining all the bit locations the last stage reads data bits from each grey-scale sub-image. Histogram equalization is applied to the grey-scale image obtain maximum contrast between 1- and 0- valued bits. Bit values are determined by applying threshold at the estimated bit centres. Thus the algorithm is invariant to scaling, rotation and blurring caused due to the device mobility. It is implemented in c and takes approximately 100ms to execute per image using a pentium4 3.40GHz computer.

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Data Structures, Algorithms, and Applications in Java

Data Structures, Algorithms, and Applications in Java

by

Sartaj Sahni 

 

 Download Slides:

Powerpoint presentations used in the lectures are available from this page. These presentations were prepared using Powerpoint 2000.

You may download a zip file that contains all 41 presentations by clicking here. Alternatively, you may download individual presentations from the table given below.

.

Lecture	Content	Reading	Slides
1	Course overview and insertion sort.	Chapters 1 through 3.	Powerpoint
2	Insertion sort and practical complexities.	Section 3.5.	Powerpoint
3	Run-time measurement.	Chapter 4.	Powerpoint
4	Linear lists.	Sections 5.1-5.2.	Powerpoint
5	Array representation and array resizing.	Section 5.3.	Powerpoint
6	Walk through of code for ArrayLinearList.	Section 5.3.	Powerpoint
7	Iterators. Linked representation of a linear list.	Sections 5.3 and 6.1.	Powerpoint
8	Walk through of code for Chain. Head nodes, circular lists, doubly linked lists.	Sections 6.2 and 6.3.	Powerpoint
9	Simulated pointers and available-space lists.	Sections 7.1 and 7.2.	Powerpoint
10	Row-major and column-major indexing, and special matrices.	Sections 8.1, 8.2, and 8.3.	Powerpoint
11	Sparse matrices.	Section 8.4.	Powerpoint
12	Stacks--application to parentheses matching, towers-of-hanoi, railroad car rearrangement, and switchbox routing; array stacks.	Sections 9.1, 9.2, 9.5.	Powerpoint
13	Array and linked stacks.	Section 9.3 and 9.4.	Powerpoint
14	Nonapplicability of queues for parantheses matching, towers-of-hanoi, railroad problem with LIFO tracks, and switchbox routing. Application of queues to railroad problem with FIFO tracks, wire routing, and component labeling. Array and linked queues.	Sections 10.1-10.4, 10.5.1-10.5.3.	Powerpoint
15	Exam.	-	-
16	Dictionaries, linear list representation, and hashing.	Sections 11.1, 11.2, 11.3, and 11.5.	Powerpoint
17	Hashing and hash table design.	Section 11.5.	Powerpoint
18	LZW compression.	Section 11.6.	Powerpoint
19	Trees, binary trees, and properties.	Sections 12.1-12.3.	Powerpoint
20	Binary tree representation and operations.	Sections 12.4 and 12.5.	Powerpoint
21	Binary tree traversal methods-- preorder, inorder, postorder, level order. Reconstruction from two orders	Sections 12.6-12.8.	Powerpoint
22	Online equivalence classes.	Section 12.9.2.	Powerpoint
23	Application of priority queues to heap sort and machine scheduling. Min and max heaps.	Sections 13.1-13.3, 13.6.1, and 13.6.2.	Powerpoint
24	Initialization of min and max heaps. Height- and weight-biased leftist trees.	Sections 13.4.4 and 13.5.	Powerpoint
25	Winner and loser trees and application to k-way merging, run generation, and first-fit bin packing.	Chapter 14.	Powerpoint
26	Binary search trees and indexed binary search trees.	Sections 15.1-15.5.	Powerpoint
27	Definition of AVL trees. Graph applications and properties.	Sections 16.1, 17.1-17.3.	Powerpoint
28	Graph operations and representation.	Sections 17.4-17.7.	Powerpoint
29	Breadth-first and depth-first search. Application to path finding, connected components, and spanning trees.	Sections 17.8 and 17.9.	Powerpoint
30	Greedy method and application to bin packing, loading, and knapsack problems.	Sections 18.1, 18.2, 18.3.1, and 18.3.2.	Powerpoint
31	Exam.	-	-
32	Single source all destinations shortest paths algorithm.	Section 18.3.5.	Powerpoint
33	Kruskal's and Prim's minimum-cost spanning tree algorithms.	Section 18.3.6.	Powerpoint
34	Divide and conquer, and application to defective chessboard and min-max problem. Iterative min-max implementation.	Sections 19.1 and 19.2.1.	Powerpoint
35	Merge sort, natural merge sort, and quick sort.	Sections 19.2.2 and 19.2.3.	Powerpoint
36	Selection and closest pair of points.	Sections 19.2.4 and 19.2.5.	Powerpoint
37	Dynamic programming, 0/1 knapsack problem, recursive and iterative solutions.	Sections 20.1 and 20.2.1.	Powerpoint
38	Matrix multiplication chains, dynamic programming recurrence, recursive solution.	Section 20.2.2.	Powerpoint
39	Iterative solution to matrix multiplication chains.	Section 20.2.2.	Powerpoint
40	All pairs shortest paths.	Section 20.2.3.	Powerpoint
41	Single source shortest paths with negative edge weights.	Section 20.2.4.	Powerpoint
42	Solution space trees and backtracking.	Section 21.1.	Powerpoint
43	Branch and bound.	Section 22.1.	Powerpoint

 

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CBSE Date Sheet 2013 for class X and XII announced

The Central Board of Secondary Education announced the date sheet for the Classes X and XII Examinations, 2013 on Monday. The exams for both the classes to begin on on March 1, 2013 and end by the mid of April 2013. Class X (PDF) Class XII (PDF) The schedule can be accessed at http://cbsedatesheet.org with a PDF download function to store the same on your desktop for offline viewing. About 10 lakh students across the country took the CBSE Board Exams, 2012 and a rise in this figure is expected in 2013. We wish you a good luck for almost last minute preparation and revision.

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