Machine learning and computer vision in matlab

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COURSEWORK ASSESSMENT SPECIFICATION Module Title: Machine Learning and Computer Vision Module Number: KF6052 Academic Year: 2019-2020, Year Long % Weighting (to overall module): 100% Coursework Title: First sit Average Study Time Required by Student: e.g. 50 study hours Dates and Mechanisms for Assessment Submission and Feedback Date of Handout to Students: Thursday 23rd January 2020. Mechanism for Handout to Students: Date and Time of Submission by Student: 16.00 GMT, Thursday 25th April 2020 Date by which Work, Feedback and Marks will be returned to Students: 3 weeks Mechanism for return of assignment work, feedback and marks to students: Individual feedback and marks will be sent to the students via email. Preliminaries Important Points · This is an individual coursework and must be completed independently. Evidence of academic misconduct (e.g., plagiarism, collaboration/collusion among students) will be taken seriously and University regulations will be strictly followed. You must adhere to the university regulations on academic conduct. Formal inquiry proceedings will be instigated if there is any suspicion of plagiarism or any other form of misconduct in your work. Also, If there are any suspicions about a submission, the concerned student will be invited to explain their work. Refer to the Northumbria Assessment Regulations for Taught Awards. If you are unclear as to the meaning of these terms. The latest copy is available on the University website. · Where you have used someone else’s words (quotations), they should be correctly quoted and referenced in accordance to the Harvard System. For guidance on referencing, see Pears, R. & Shields, G. (2013) Cite Them Right: the essential referencing guide, 9th edn. Basingstoke: Palgrave Macmillan. There is also Cite Them Right Online. Palgrave Macmillan (2016) Cite Them Right Online.  http://www.citethemrightonline.com/ · You are expected to produce a word-processed answer to some questions in this assignment. Please use Arial font and a font size of 12. · If needed, you are required to use the Harvard Style of referencing and citation. · Late submissions will be given zero marks unless prior permission is gained from the school office/programme leader. Learning outcomes (LOs) addressed: This assignment aims to assess the following learning outcomes: 1. Demonstrate knowledge and understanding of the theory and practice of modern digital techniques for the capture, processing and storage of visual data. 2. Critically analyse legal and ethical issues in computer vision as well as current technologies for security. 3. Apply and critically evaluate appropriate machine learning techniques to solve problems in computer vision. Assignment Tasks Part 1. LO1 & LO2 [50 marks] Section A. [15 marks] · Give a brief description of sensor pattern noise and discuss its applications in solving legal/ethical issues. [7] · Describe and discuss the different types of digital watermarking and explain how fragile watermarking can be used in image authentication. [8] Guide length: no more than 300 words each. Marking criteria. Good work in these questions will: · Give a correct description of sensor pattern noise and show good understanding of its applications. · Give a correct definition of watermarking and accurate description of different watermarking types. Good understanding of fragile watermarking and its applications. Section B. [30 marks] You are provided with a test image saved as TIF file named ‘peppers.tif’. For the purpose of image copyright protection, you are required in this section to implement a one-bit watermarking system in the wavelet domain and a similar watermarking system in the DCT domain as follows: Wavelet domain watermarking:[15 marks] Embedding: [6 marks]. The input image is wavelet transformed with two levels of decomposition. The wavelet name in Matlab is ‘haar’. The second level sub-bands HL2, HH2 and LH2 are used to hold three watermarks w1, w2, and w3 respectively. These watermarks are of size (128 x 128) each and are saved in a MAT file named ‘watermark.mat’. The embedding rule is multiplicative with a watermark strength =0.4. For instance, the ith wavelet coefficient in HL2 is updated as where Y’(i) is the watermarked coefficient in HL2. Y(i) is the original coefficient in HL2. Once the coefficients are updated, the inverse wavelet transform is performed to obtain the watermarked image. This programme should produce the watermarked image in a TIF file. Detection: [9 marks]. This programme verifies the presence of the watermarks w1, w2, and w3 in the previously saved picture. First, two wavelet decompositions are performed as before. Then, three similar watermark detectors are used for detecting w1, w2, and w3 which are stored in ‘watermark.mat’. For example, for HL2 where w1 is verified, the detector calculates where N is the number of samples. |.| represents the absolute operation. c=0.4 is called the shape parameter. 1 is the standard deviation of the HL2 sub-band (use std if the matrix is converted into a column vector and std2 otherwise). The decision for watermark w1 is made as follows if then w1 exists in HL2. Otherwise, w1 does not exist. Similarly, the second detector calculates from w2, 2 , and the coefficients in HH2 to decide on the presence of w2 in HH2. The third detector calculates from w3, 3 , and the coefficients in LH2 to decide on the presence of w3 in LH2 Hints: a- For the insertion of the watermark (Embedding), you may create a function which has two arguments (set of coefficients, watermark) and outputs the set of watermarked coefficients. Then, this function can be called in the main Embedding programme three times for the insertion of w1, w2, and w3, respectively. As for the detection, you may also need to create a function which has two arguments (set of coefficients, candidate watermark) and outputs a binary decision (1 if watermark exists, 0 otherwise). Once the function is created, it can be called in the main detection programme three times with different parameters. b- You may convert a matrix into a column vector (or into a row vector) to perform the insertion and detection of the watermark. The syntax in matlab to convert a matrix A of M rows and N columns into a column vector is column_vector=A(:); The inverse process (conversion from column vector into matrix) can be obtained by the following syntax Reconstructed_matrix=reshape(column_vector,M,N); DCT domain watermarking:[15 marks] Embedding: [6 marks]. The input image is DCT-transformed. Three square regions are selected from the transformed image to hold w1, w2 and w3 (see figure) using the same multiplicative rule as before. Then, the inverse DCT is applied to get the watermarked image. This programme should output the watermarked image in a TIF file. ( DCT - transformed image W 1 W 2 W 3 ) Detection: [9 marks]. This programme verifies the presence of the watermarks w1, w2, and w3 in the previously saved picture. First, the DCT is applied on the watermarked image. Then, three similar watermark detectors as described earlier are used for detecting w1, w2, and w3 which are stored in ‘watermark.mat’. For example, for the region where w1 is verified, the detector calculates from w1, 1 , and the coefficients in that region to decide on the presence of w1. Marking criteria. The implementation of each watermarking system is assessed as follows Codes work perfectly without errors. Excellent use of matrix manipulation in Matlab. Very good understanding and implementation of the embedding and detection equations. Good understanding of the watermarking systems. >10 Problems with the understanding and implementation of some equations. Hard coded implementation. However, it includes correct parts on embedding, detection, and image transforms. 6- 10 Poor understanding of the different parts of watermarking systems. Unable to link and analyse the main parts of the system. Incorrect implementation and the code does not compile/work properly. 0 - 6 Section C. [5 marks] In this section, you are required to apply the following manipulations on the watermarked image in both wavelet and DCT domains and check the detection of the watermarks. Complete the following table and interpret the results. Wavelet-Based DCT-Based Manipulation W1 W2 W3 W1 W2 W3 Circular shifting [1,1] use ‘circshift.m’ Average filtering 33 Guide length: no more than 50 words for the interpretation of the results. Marking criteria. Implementation and results [3 marks] Discussion of the results [2 marks]. Part 2. LO1 & LO3 [50 marks] Section A. [8 marks] Give a description of digital image filtering and its applications. An illustrative example of the process may help. Guide length: no more than 400 words. Marking criteria. Good work in this question will: · Give a correct definition of digital image filtering. · Show good understanding of image filtering and its applications. Section B. [7 marks] A- Discuss the different types of machine learning systems. A diagram showing the classification of machine learning systems may help. Guide length: no more than 300 words. Marking criteria. Good work in this question will: · Show good understanding of regression, classification (binary and multi-class), supervised learning and unsupervised learning. Section C. [35 marks] The implementation of a gender classification system using an Artificial Neural Network is given in Matlab. The Matlab function ‘getFeatureVector.m’ extracts 64 features from any input colour image by using the grey scale plane only in the Discrete Cosine Transform (DCT) domain. There are two types of training images: male and female images which are stored in two folders ‘Male’ and ‘Female’, respectively. The extracted features for both male and female images will be used to train the classifier with ‘ANN_training.m’. Once the classifier is trained, it can be evaluated on test images stored in folder ‘test_gender’. This is implemented via the Matlab code ‘ANN_testing.m’ where the default image is ‘male01.jpg’. The idea is to verify the gender each face image according to the trained classifier. If a female image is classified, the system will display a message ‘female’. Otherwise, the message ‘male’ is displayed. 1. Based on the existing Matlab codes (‘ANN_training.m’ and ‘ANN_testing.m’), create new codes that implement the linear perceptron, the Naive Bayes classifier, and a classification tree. You should submit only the part of codes which are different from the current ones. [3] 2. The false positive rate (FPR) is defined as the proportion of falsely classifying a male image as ‘female’. The false negative rate (FNR) is the proportion of misclassifying female images. For instance, if the system is applied on 20 male images and there are 2 incorrect classifications (i.e. classified as female), FPR=2/20. If the system shows 5 incorrect classifications on 20 female images, FNR=5/20. Run the Matlab codes on all test images and complete the following table [6]. Linear Perceptron ANN Decision Tree Naive Bayes FPR 0.26 FNR 0.26 Error = 0.26 3. Analyse and interpret the results. [12] Guide length: no more than 500 words. Marking criteria. Good work in this question will: · Show good analysis of the results and a reasonable justification of the performance of each classifier. The complexity cost for each algorithm should be discussed as part of the experimental analysis. 4. Explain how the performance of the gender classification system can be improved. [14] Guide length: no more than 500 words. Marking criteria. Good work in this question will: · Show good understanding of the different stages of the gender detection system. · Propose solutions in different stages of the system. Note You will be submitting the final version of your assignment on Turnitin UK. Your report should include full Matlab codes for Part 1, section B. and Part 1 section C. For Part 2, section C, You should submit only the part of codes that are different from the ones given. In order to help you to avoid any problems with plagiarism, the Turnitin UK assignment for your submission has been configured to allow you to upload your answer, receive an originality report, and then if necessary submit a revised version before the deadline. You are advised to upload your submission in time to check it and amend it if necessary. You should then look at the originality report and consider whether it indicates any material that needs to be changed. You may submit your work more than once, but note that you must wait 24 hours before receiving a second originality report, so take care with your submission. You are strongly advised to make use of this facility. Page | 8

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