Computer Graphics Question Paper

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                                    Computer Graphics

MCA302Computer Graphics
Subject CodeSubject NameTeaching Scheme (Contact Hours per week)Credits Assigned
MCA302Computer Graphics040404
Examination Scheme
TheoryTerm WorkPractOralTotal
Internal AssessmentEnd Sem. Exam.

[ Once in a semester ]

Test 1Test 2Average


Objectives Through this course students are introduced to fundamental principles and algorithms underlying computer graphics, including line drawing algorithms, circle/ellipse drawing algorithms, 2D geometrical transformation, 3D geometric transformations, viewing in 3D (orthographic projection and perspective projection), visible surface detection algorithms. They are also introduced to different image enhancement techniques.

Outcomes  After completion of this course students are expected to know how to a rasterize line, circle   etc. and implement 2D-3D transformations such as translation, rotation, scaling, shearing, and reflection. They are also expected to understand and be able use them to implement them in animation. They are expected to know how to apply different image transformation on an image.


Unit No.                                                                   Contents                                                                No of.



Unit I           Introduction: Introduction to computer graphics and Image Processing and their applications, Raster-Scan System, Random-Scan Systems.

Unit II Basic Drawing Algorithms: Line-Drawing Algorithms:  DDA  Algorithm,  Bresenham’s Line Algorithm. Circle-Generating Algorithms: Midpoint Circle Algorithm, Bresenham’s Circle Algorithm. Ellipse-Generating Algorithm: Midpoint Ellipse Algorithm. Two Dimensional Curve Generation: Bezier curves and Cubic B-Spline Curves.

Unit III Region  Filling  Algorithms  :Scan-Line  Polygon  fill  Algorithm,  Inside-Outside  Tests,   Boundary-Fill Algorithm, Flood-fill Algorithm

Unit IV Two-Dimensional Geometric  Transformations: Translation, Rotation, Scaling,  Matrix Representations and Homogeneous Coordinates, Composite Transformations, Inverse transformations, General Pivot-Point Rotation, General Fixed-Point Scaling, Concatenation Properties, General Composite Transformations, Rotation about any arbitrary line. Other Transformations: Reflection, Shear.

Unit V Two-Dimensional Viewing and  Clipping:  The  Viewing  Pipeline,  Viewing Coordinate Reference Frame, Window-to viewport Coordinate transformation. Clipping Operations: Point Clipping, Line Clipping, Cohen-Sutherland Line Clipping, Liang-Barsky Line Clipping Polygon Clipping, Midpoint subdivision line clipping algorithm, Sutherland-Hodgeman Polygon Clipping.

Unit VI Three-Dimensional Concepts and Object Representation: Three-dimensional transformations: Translation, Rotation, Scaling, and their Matrix Representations. Three-Dimensional Display Methods: Parallel Projection, Perspective Projection and their types. Three-Dimensional Object Representations: Octrees.

Unit VII Visible-Surface Detection Methods: Classification of Visible-Surface Detection Algorithms, Depth-Buffer Method, A-Buffer Method, Scan-Line Method.

Unit VIII Shading Techniques: Constant intensity shading, Gourd shading, Halftoning and Dithering. Other Applications Areas: Fractals: Fractal Geometry methods. Fractal-Generation Procedures, Classification of Fractals, Fractal Dimension, Koch Curve. Animation: Introduction to animation.

Unit IX Introduction: Fundamental Steps in Digital Image Processing:  Components of an  Image Processing System, Basic Concepts in Sampling and Quantization, Representing Digital Images,  Spatial and Gray-Level Resolution.

Unit X Image Enhancement  in  the  Spatial  Domain:  Some  Basic  Intensity Transformation Functions: Image Negatives, Log Transformations, and Power- Law Transformations. Piecewise-Linear Transformation Functions: Contrast stretching, Gray-level slicing, Bit plane slicing. Histogram Processing: Image Histogram and Histogram Equalization, Image Subtraction, and Image Averaging. Spatial Filtering: Basics of Spatial Filtering, Smoothing Spatial Filters Smoothing Linear Filters, Order-Statistics Filters. Sharpening Spatial Filters: Use of Second Derivatives for Enhancement–The Laplacian, Unsharp masking and High-Boost Filtering: Use of First Derivatives for (Nonlinear) image sharpening – The  Gradient– Robert, Prewitt and Sobel Masks. Combining Spatial Enhancement Methods.


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