# UNIVERSITI SAINS MALAYSIA. CCS513 Computer Vision and Image Analysis [Penglihatan Komputer dan Analisis Imej]

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1 UNIVERSITI SAINS MALAYSIA First Semester Examination 204/205 Academic Session December 204/January 205 CCS53 Computer Vision and Image Analysis [Penglihatan Komputer dan Analisis Ime] Duration : 2 hours [Masa : 2 am] INSTRUCTIONS TO CANDIDATE: [ARAHAN KEPADA CALON:] Please ensure that this examination paper contains FOUR questions in ELEVEN printed pages before you begin the examination. [Sila pastikan bahawa kertas peperiksaan ini mengandungi EMPAT soalan di dalam SEBELAS muka surat yang bercetak sebelum anda memulakan peperiksaan ini.] Answer ALL questions. [Jawab SEMUA soalan.] You may answer the questions either in English or in Bahasa Malaysia. [Anda dibenarkan menawab soalan sama ada dalam bahasa Inggeris atau bahasa Malaysia.] In the event of any discrepancies, the English version shall be used. [Sekiranya terdapat sebarang percanggahan pada soalan peperiksaan, versi bahasa Inggeris hendaklah diguna pakai.]...2/-

2 (a) The histograms of two images are illustrated in Figure. Sketch a transformation function that will improve the contrast of each image. Note that f is pixel intensity. Figure (9/00) The images shown in Figure 2 are quite different but their histograms are the same. Suppose that each image is blurred with a 3x3 averaging mask: Figure 2 Would the histogram of the blurred images be still equal? Explain. If the histograms are not equal, sketch the two histograms....3/-

3 - 3 - (c) The results obtained by a single application of a 2D filter can be achieved also by applying two individual D filters. For example, the same result of using a 3x3 smoothing mask with coefficients /9 can be obtained by the following process pipeline: Applying a mask the image in the horizontal direction. The result of this is then followed by applying a mask [ ] in the vertical direction. The final result is then scaled by /9. Taking a clue from the above, show that the response of Sobel mask 2 [ ] can be implemented similarly by the differentiating mask [ 0 ] 2 followed by a smoothing mask [ 2 ]. a b c Hint: You may use the sample image [ d e f]. g h k (0/00) 2. (a) Propose a morphological procedure to clear the artefacts of the image in Figure 3(a) to produce the image in. Clearly state the structuring element and number of iterations that you would use in your procedure. (a) Figure 3 Explain any one () of the following feature descriptors. Include the processing pipeline and describe each of the steps in this pipeline. Also state the uniqueness of the selected descriptor. (iii) SIFT Scale Invariant Feature Transform. HOG Histogram of Gradients. LBP Local Binary Pattern. (9/00)...4/-

4 A computer vision system can be used to classify several distinct human emotions using analysis of facial expressions. These emotions include happy, sad, angry, surprised. Figure 4 shows a set of sample expressions that could be used to train a classifier. Figure 4 : Example facial expressions (Source: Japanese Female Facial Expression (JAFFE) Database) A computer vision system was used to extract several facial feature measurements. Assume that only the following facial measurements were used for the emotion detection system: Angle between the principal axis of the two eyebrows, Curvature of the lips, The following data was obtained for a sample of 0 random face images and the system is required to classify two different facial expressions, Sad and Angry. The experimenter decides to use a Euclidean distance-based minimum distance classifier. Expression Sad Sad Sad Sad Sad Angry Angry Angry Angry Angry /-

5 - 5 - (a) Compute the prototype vectors for the two classes of facial expressions, m sad and m angry which is given by: m / N x for,2,... M x Plot the training data on a scatter plot ( vs ) and indicate the location of prototype vector for each class (Sad, Angry). Denote the prototype vector in the scatter plot with an asterisk (*). (c) For the minimum distance classifier that uses the Euclidean distance, it is given that the decision function for a given class is given by T T d ( x) x m - m m for,2,... M 2 Compute the decision function for the sad and angry classes respectively. (7/00) (d) Given an unknown face image whose feature vector, xu (67.3,0.2), use the minimum distance classifier to determine whether this is classified as an ANGRY FACE or SAD FACE? 4. (a) Explain any one () of these algorithms in detail: Watershed Segmentation. Graph-cut Segmentation. (0/00)...6/-

6 - 6 - The Grey Level Co-occurrence Matrix (GLCM) is a popular texture descriptor. GLCM basically captures the spatial relations of texels. The image shown in Figure 5 contains only 3 intensity levels: 0, and 2. P d (x, y) indicates the number of times the value x co-occurs with value y in a particular spatial relationship, d. Compute the co-occurrence matrix for the image in Figure 5, given that the spatial relationship is d(2,2) Figure 5 (5/00)...7/-

7 KERTAS SOALAN DALAM VERSI BAHASA MALAYSIA (a) Histogram bagi dua ime ditunukkan dalam Raah. Lakarkan fungsi transformasi yang akan memperbaiki kontras untuk setiap ime tersebut. Perhatikan bahawa f meruuk kepada keamatan piksel. Raah (9/00) Ime yang ditunukkan dalam Raah 2 adalah agak berbeza tetapi histogram mereka adalah sama. Andaikan setiap ime dikaburkan dengan topeng purata 3x3: Raah 2 Adakah histogram ime-ime yang dikaburkan masih sama? Terangkan. Jika histogram tidak sama, lakarkan kedua-dua histogram tersebut....8/-

8 - 8 - (c) Keputusan yang diperoleh oleh satu aplikasi penuras 2D dapat dicapai uga dengan menggunakan dua penuras D individu. Sebagai contoh, keputusan yang sama menggunakan topeng pelicinan 3x3 dengan pekali /9 boleh diperoleh dengan proses berikut: Menggunakan topeng untuk ime dalam arah melintang. Keputusan ini kemudiannya diikuti dengan menggunakan topeng [ ] dalam arah menegak. Hasil akhir kemudian diskalakan dengan nilai /9. Mengambil petunuk dari contoh di atas, tunukkan bahawa hasil topeng Sobel 2 [ ] boleh dilaksanakan sama dengan topeng pembezaan [ 0 ] 2 diikuti oleh topeng pelicinan [ 2 ]. a b c Petunuk: Anda boleh menggunakan ime sampel [ d e f ]. g h k (0/00) 2. (a) Cadangkan prosedur morfologi untuk membersihkan artifak ime dalam Raah 3(a) untuk menghasilkan ime dalam. Nyatakan dengan elas elemen penstrukturan dan bilangan lelaran yang anda akan gunakan dalam prosedur anda. (a) Raah 3 Terangkan mana-mana salah satu () deskriptor ciri berikut. Rangkumkan langkah-langkah pemprosesan dan terangkan setiap langkah-langkah tersebut. Juga nyatakan keunikan deskriptor yang dipilih. (iii) SIFT Scale Invariant Feature Transform. HOG Histogram of Gradients. LBP Local Binary Pattern. (9/00)...9/-

9 - 9 - Sistem penglihatan komputer boleh digunakan untuk mengelaskan beberapa emosi manusia berbeza menggunakan analisis ekspresi waah. Emosi termasuk gembira, sedih, marah, terkeut. Raah 4 menunukkan satu set ungkapan sampel yang boleh digunakan untuk melatih pengelas. Raah 4: Contoh ekspresi waah (Sumber: Pangkalan Data Ekspresi Waah Wanita Jepun (JAFFE) ) Sistem penglihatan komputer telah digunakan untuk mengambil beberapa pengukuran ciri waah. Andaikan bahawa hanya ukuran muka berikut digunakan untuk sistem pengesanan emosi: Sudut antara paksi prinsipal kedua bulu kening, Lengkungan bibir, Data berikut telah diperoleh bagi sampel 0 ime waah rawak dan sistem yang dikehendaki untuk mengklasifikasikan dua ekspresi muka yang berbeza, Sedih dan marah. Pengui kai memutuskan untuk menggunakan pengelas arak minimum. berdasarkan arak Euclidean. Ekspresi muka Sad Sad Sad Sad Sad Angry Angry Angry Angry Angry /-

10 - 0 - (a) Kirakan vektor prototaip untuk kedua-dua kelas dari ekspresi muka, m sad dan m angry yang diberikan oleh: m / N x for,2,... M x Plotkan data latihan dalam suatu plot scatter ( vs ) dan tandakan lokasi vektor prototaip untuk setiap kelas (SAD,ANGRY) dengan penanda asterisk (*). (c) Untuk Pengelas Jarak Minima yang menggunakan arak Euclediean, diberi bahawa fungsi keputusan bagi suatu kelas diberi oleh: T T d ( x) x m - m m for,2,... M 2 Hitungkan fungsi keputusan untuk kelas-kelas SAD dan ANGRY masingmasing. (7/00) (d) Diberi suatu ime waah yang tidak diketahui, yang mana vector cirinya diberi oleh xu (67.3,0.2), gunakan pengelas arak terdekat untuk membuat keputusan sama ada diklasifikasikan sebagai waah ANGRY ataupun waah SAD. 4. (a) Bincangkan salah satu () daripada algoritma berikut dengan teliti: Segmentasi Watershed. Segmentasi Graph-cut. (0/00).../-

11 - - Grey Level Co-occurrence Matrix (GLCM) adalah deskriptor tekstur yang popular. GLCM pada dasarnya mengungkapkan hubungan spatial antara texel. Ime yang ditunukkan dalam Raah 5 mengandungi 3 tahap intensiti: 0, dan 2. P d (x, y) mewakili berapa kali nilai x diperhatikan bersama dengan nilai y dalam hubungan ruang tertentu, d. Hitungkan matriks GLCM untuk ime dalam Raah 5, ika diberikan bahawa hubungan spatial adalah d(2,2) Raah 5 (5/00) - ooooooo -

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