IT6005-Digital Image ProcessingList the colour models involved in hardware.
Specify the basic components of image processing system.
Illustrate the term ‘Image’.
Brief about fovea.
Mention the applications of image processing.
Classify image sensing sensors and give short note.
What are the primary and secondary colours.
Summarize the membranes of human eye.
Short answer about the terms: Hue, Saturation, Grey level
How a digital image can be represented?
Construct the photonic electromagnetic spectrum.
Identify the various multispectral bands and their applications
Outline the function of an image sensor.
Relate the difference between regions and boundaries.
Point out the steps for analog to digital conversion, state its need.
Examine about adjacency and connectivity.
Compare Brightness and Contrast.
Explain the term Weber ratio and Quantization.
Discuss that how an image to be sensed by human eye?
Compose about 4 connectivity, 8 connectivity and m connectivity in pixels.
In detail explain the fundamental steps involved in digital image processing systems.
What are the components of digital image processing system?Write in detail about each block.
(i)Summarize the human visual perception system in detail with necessary diagrams.
(ii) Explain CMY and CMYK colour models.Interpret in detail about:
i)RGB model ii) HIS model i) Give in detail about image acquisition system
ii) Illustrate how the image is digitized by sampling and quantization process Describe in detail about:
i) Various sensors ii) Image acquisition systems i) Conclude that how the digital images to be represented give in detail.
ii) Analyze that how the image formation takes place in eye and state the principle of operation of brightness adaption and discrimination Distinguish the following terms and brief each:
i) Adjacency ii) Connectivity iii) Region iv)BoundaryEvaluate the various colour models. Explain each of them in detail. Compose in detail about:
i) Image sampling ii)Quantization i)Identify the applications of image processing
ii) Write about image representation system.Define briefly the following terms: i) image restoration,
ii) Compression, iii) Segmentation, iv) morphological processingi) With diagram explain the principle operation of human eye.
ii) Demonstrate about various chromic models.Classify the various parameters of image processing
i) Band number ii) Spectrum, iii) wave lengths, iv) applications.Construct suitable examples for the various relationships between pixels and describe them in detail.
Assess about image quantization and sampling and their importance and need in digital image processing.
Evaluate about the various colour models with appropriate examples.
Integrate the need for various steps in digital image processing. Explain their prominence of each block.
Discuss about image filtering?
Summarize about histogram equalization.
Explain the two categories of image enhancement.
List various gray level transformation techniques.
Recall the term histogram specification.
Specify the need for image enhancement.
What is spatial domain method?
Describe about Histogram?
Distinguish between smoothing and sharpening filters.
Explain the mechanics of spatial filtering.
Define frequency domain method.
Write expression for Gray, Log and Gamma transformations.
Identify the different type of derivative filters in DIP.
Demonstrate the effect of under sampling process.
Illustrate with examples for linear and nonlinear filters?
Examine the need for transform.
Evaluate the 2D sampling theorem.
Categorize the various frequency domain filters.
Construct the 2D Fourier transform and its inverse.
Estimate the link between spatial and frequency domain filtering.
Infer the difference between spatial correlation and convolution. Explain each with identical example.
i) Explain the histogram equalization method of image enhancement.
ii)Discuss histogram specification technique in detail with equations. i) Deduce about spatial enhancement techniques and Median Filtering (7)
ii) Compare the various image transformation techniquei) With example explain in detail about spatial averaging
ii) Describe in detail about various types of mean filters.Show the various techniques in frequency domain to enhance a image with necessary examples
Illustrate the 2D Fourier transform and its pair. State and prove their property.
i) State and explain sampling theorem in 2D
ii) Write about aliasing in ImagesTabulate the various filters available under frequency domain for image enhancement
Write detail note about i) Spatial and Frequency domain enhancement
ii) Discrete Fourier Transformi) Point out the comparison between smoothing & sharpening in frequency domain
ii) Analyze the performance of following smoothing filters Ideal Low Pass Filter Butterworth Low Pass Filter Gaussian Low Pass Filter i)Distinguish between spatial & frequency domain image enhancement
ii) Classify the performance of following sharpening filters Ideal HPF Butterworth HPF Gaussian HPF Estimate the constraints of histogram equalization and technique of histogram processing in detail.
Compose about the various grey level transformations with examples and plot the graph of the transformation functions.
Evaluate about the various filters in terms of their performance.
Integrate about the Fourier transform in frequency domain and the combining sinusoids of frequency content.
Formulate how the derivatives are obtained in edge detection.
Compose the equation for converting wiener filter into inverse filter.
Classify order statistic filter.
Can you recall the need of image restoration.
Identify the drawback of inverse filtering. How it can be overcome?
What do you understand by Mexican Hat function?
Define blind image restoration.
Give two applications of image segmentation.
Why the restoration is called as unconstrained restoration?
Classify the types of edges in the digital image.
List the various methods of thresholding in image segmentation.
Construct the image restoration model.
Give the difference between Enhancement and Restoration.
Describe thresholding and mention its types.
Summarize about region growing
How do you estimate that an image is getting over segmented?
Identify the detection of dis continuity in an image using segmentation.
Differentiate between local and global thresholding technique for image segmentation
Examine the condition to be met by the partitions in region based segmentation.
Evaluate the advantages and disadvantages of using more than one seed in a region growing technique.
(i)Discuss the concept of inverse and pseudo inverse filters for image restoration.
(ii)What are spatial transformation techniques used for image restoration? Explain them in detail.
Develop the algorithm for following filtering
(i)Adaptive filtering (ii)LMS filter (i)Classify the types of order statistic filter.
(ii) Simplify the operations of order statistic filter.Demonstrate inverse filtering for removal of blur caused by any motion and describe how it restore the image.
Derive a wiener filter for image restoration and specify its advantages over inverse filter.
How weiner filter is helpful to reduce the mean square error when image is corrupted by motion blur and additive noise.
Relate region splitting and merging technique for image segmentation with suitable examples.
(i)How edge detection is performed in digital images using (a)Laplacian operator. (b)Sobel operator. (c)Prewit operator and compare their outcomes.
(ii)How morphological processing is applicable for image processing.(i) Analyze about edge pixels through global processing.
(ii) Examine Watershed segmentation algorithm.Evaluate the following terms (i)region merging. (ii)erosion and dilation.
(ii)Discuss about edge detection and edge linking method.Describe constrained least square filtering for image restoration and derive its transfer function.
(i)What do you mean by optimal thresholding in detail and how do you obtain the threshold for image processing?
(ii) Tabulate the different types of thresholding for segmentation. (i) Summarize the process of edge linking using Hough transform.
(ii) Explain region based segmentation technique.(i)Illustrate region based segmentation and region growing examples.
(ii)Examine how to construct dams using morphological operations. Explain the use of wiener filter or least mean square filter in image restoration.
Elaborate about Inverse filtering.
Discuss about region based image segmentation techniques.
Compare Edge Detection and edge linking in detail.
Investigate the performance metrics for evaluating image compression.
What are the operations performed by error free compression?
Evaluate the two main types of data compression.
Illustrate some important applications of wavelet transform.
Formulate Compression ratio.
Compare wavelet coding with DCT based coding.
List the need of image compression and mention different compression methods.
Mention the various image and video compression standards.
Explain the function of subband coding.
Write the equation to find relative data redundancy.
Validate the types of data redundancy.
How will you apply the Lossless predictive model in image compression?
Interpret the steps of Huffman coding.
Recall the concept of runlength coding.
Classify the types of lossless and lossy compression methods.
Sketch the block diagram of block transform encoder.
Identify the role of multiresolution analysis in image processing.
Categorize video compression standards.
Summarize variable-Length coding and name the types of variable- Length coding.
What is an image pyramid?
Solve and find a Huffman code and average length of the code and its redundancy for the source emits letters from an alphabet A={a1,a2,a3,a4,a5} with probabilities P(a1)==0.2, P(a2)=0.4, P(a3)=0.2, P(a4)=0.1 and P(a5)=0.1.
Develop the tag for the sequence 1 3 2 1 for the probabilities P(1)=0.8, P(2)=0.02, P(3)=0.1813. How an image is compressed using JPEG Image compression standard?
Write short notes on: (i)Arithmetic coding.
(ii)JPEG standards.Evaluate the need for image compression. How run length encoding approach is used for compression? Is it lossy? Justify.
Design a coder which a source emits letters from an alphabet A={k1,k2,k3,k4,k5} with probabilities P(k1)=p(k3)=0.2, P(k2)=0.4, P(k4)= P(k5)=0.1, entropy = 2.122bits/symbol. Find a Huffman code for this source and the average length of the code a
Discuss in detail about (i)Bit plane coding.
(ii)LZW coding.(i)Explain the concepts of lossless predictive encoding.
(ii)Summarize the lossy predictive coding.(i)Illustrate block transform coding with suitable example.
(ii)Describe the concepts of wavelet transform coding in image compression. State the basic concepts of the following terms. (i)Haar transform
(ii)Subband coding (iii)Fast wavelet transform. Show the mathematical analysis of following Multi resolution expansion (i)Series expansion
(ii)Scaling functions (iii)Wavelet functions Analyze the concepts of wavelet transform with mathematical support (i)Continuous wavelet transform
(ii)Discrete wavelet transform(i)Distinguish between losseless and lossy compression.
(ii)Categorize image compression standard with its block diagram.(i)Examine with suitable example for huffman coding scheme results with image compression.
(ii)Demonstrate block diagram of JPEG standard.(i)With a block diagram explain shift coding approach for image compression.
(ii)Describe the stages in MPEG image compression standard.What is image compression? Explain any four variable length coding compression schemes.
Elaborate the concepts of arithmetic coding.
Explain how compression is achieved in transform coding and explain about DCT.
Design the block diagram of MPEG encoder and discuss its operation.
Short note about the term ‘entropy’. what is the relevance in image processing
Define region growing.
How texture of a region is decided?
What is training pattern and training set.
Name the approaches used to describe the texture of a region.
Formulate the Equation of circularity ratio of regional descriptors.
Estimate the equation of third moment of statistical approach for texture descriptor.
Summarize convex hull and convex deficiency concepts.
Recall the term ‘eccentricity’.
Determine the approaches used in image processing to describe the texture of a region.
Show the importance of polygonal image representation.
Categorize the Uses of chain code.
Discuss the method used to define skeleton.
Explain the features of Pattern classes.
Develop the steps for Shape number in image segmentation?
Classify the types of image representations.
Write the special features of Fourier descriptors.
Evaluate the steps involved in first difference calculation ofchain code methodology.
Identifythe various topologyapplied in image processing.
Describe Topological features?
Explain the concepts and approach of chain code, Boundary representation
(i)Explain Polygonal approximation method.
(ii)How the merging techniques applied in approximation and also develop the steps involed in approximation method. State the concepts of following methods
i) Signature ii) Boundary segments iii) Skeletons Explain the different types of boundary descriptors with suitable diagrams.
What are all the object recognition method used in image processing for decision making methods? How that methods apply in pattern classification.
Write in detail note about the following i)Textures ii)Shape numbers
iii)Fourier descriptors (iv)Pattern classes (i) Classify the Regional descriptors
(ii) Examine the regional descriptors with basic diagrammatic representations. (i) Categorize Relational descriptors with examples.
(ii)Analyze the relational descriptors with basic equations, find which method is best (i)Elaborate the decision theoretic methods for recognition
(ii)Formulate the recognition based on matching method with equations. (i)Evaluate the optimum statistical classifiers and neural networks method for recognition.
(ii)Estimate the efficiency of the above two mentioned methods Explain the structural methods of object recognition i)Matching shape numbers.
(ii)String matching.i) State the different approaches of textures?
(ii)Define the parameters of descriptors in image representationWrite the concepts of following methodologies (i)MAT(4)
(ii)Distance versus angle signature. (iii)Simple boundary descriptors. (i)Classify the different methods of image representation.
(ii)Analyze the properties of fourier descriptors. (iii)Analyse the different approaches of pattern and pattern classes Explain in detail about the object recognition techniques based on matching
Elaborate the various boundary descriptors in detail with neat diagram
Discuss on the following image representation technique i) Chain code
ii) Polygonal approximationMention the different techniques for the representation of shapes in digital image. Explain the principle behind “Fourier Descriptor” based shape representation.

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