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Brodatz Textures Vistex Textures What is texture ? Texture can be considered to be repeating patterns of local variation of pixel intensities.

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Presentation on theme: "Brodatz Textures Vistex Textures What is texture ? Texture can be considered to be repeating patterns of local variation of pixel intensities."— Presentation transcript:

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3 Brodatz Textures Vistex Textures What is texture ? Texture can be considered to be repeating patterns of local variation of pixel intensities.

4 Human Invented Algorithms Texture feature extraction algorithms can be grouped as follows* Statistical Statistical Geometrical Geometrical Model based Model based Signal Processing Signal Processing *Tuceryan and Jain, “Texture Analysis” in The Handbook of Pattern Recognition and Computer Vision, World Scientific, 2 nd edn., 1998

5 Statistical Methods Local features Local features Autoregressive Autoregressive Galloway – run length matrix Galloway – run length matrix Haralick – co-occurrence matrix Haralick – co-occurrence matrix Unser Unser Sun and Wee Sun and Wee Amadasun Amadasun Dapeng Dapeng Amalung Amalung

6 Co-occurrence matrix A co-occurrence matrix or co-occurrence distribution is a matrix or distribution that is defined over an image to be the distribution of co-occurring values at a given offset. Mathematically, a co-occurrence matrix C is defined over an n x m image I, parameterized by an offset (Δx,Δy), as:matrix or distribution that is defined over an image to be the distribution of co-occurring values at a given offset. Mathematically, a co-occurrence matrix C is defined over an n x m image I, parameterized by an offset (Δx,Δy), as:

7 Co-occurrence matrix function M=getCoMatrix(M1,M2) M = zeros(2); % 2x2 result matrix for binary image. [r,c] = size(M1); for i=1:r for j=1:c v1 = M1(i,j)+1; % Add one to binary image values to get Matlab indices. v2 = M2(i,j)+1; M(v1,v2) = M(v1,v2)+1; % Increment co-occurrence value. end

8 Digital watermarking is the process of embedding information into a digital signal in a way that is difficult to remove. The signal may be audio, pictures or video, for example. If the signal is copied, then the information is also carried in the copy. A signal may carry several different watermarks at the same time. In visible watermarking, the information is visible in the picture or video. Typically, the information is text or a logo which identifies the owner of the media. The image on the right has a visible watermark. When a television broadcaster adds its logo to the corner of transmitted video, this is also a visible watermark.

9 In invisible watermarking, information is added as digital data to audio, picture or video, but it cannot be perceived as such (although it may be possible to detect that some amount of information is hidden).detect The watermark may be intended for widespread use and is thus made easy to retrieve or it may be a form of Steganography, where a party communicates a secret message embedded in the digital signal.Steganography In either case, as in visible watermarking, the objective is to attach ownership or other descriptive information to the signal in a way that is difficult to remove. It is also possible to use hidden embedded information as a means of covert communication between individuals.

10 General watermark life-cycle phases with embedding-, attacking- and detection/retrieval functions

11 Robustness A watermark is called fragile if it fails to be detected after the slightest modification. Fragile watermarks are commonly used for tamper detection (integrity proof). A watermark is called semi-fragile if it resists benign transformations but fails detection after malignant transformations. Semi-fragile watermarks are commonly used to detect malignant transformations. A watermark is called robust if it resists a designated class of transformations. Robust watermarks may be used in copy protection applications to carry copy and access control information. Perceptibility A watermark is called imperceptible if the original cover signal and the marked signal are (close to) perceptually indistinguishable. A watermark is called perceptible if its presence in the marked signal is noticeable, but non-intrusive.

12 Embedding method A watermarking method is referred to as spread-spectrum if the marked signal is obtained by an additive modification. Spread-spectrum watermarks are known to be modestly robust, but also to have a low information capacity due to host interference. interference A watermarking method is said to be of quantization type if the marked signal is obtained by quantization. Quantization watermarks suffer from low robustness, but have a high information capacity due to rejection of host interference. A watermarking method is referred to as amplitude modulation if the marked signal is embedded by additive modification which is similar to spread spectrum method but is particularly embedded in the spatial domain.

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22 The global market for machine vision system components was $10.3 billion in 2009, slightly more than the market figure for 2008, which was nearly $9.9 billion. This is expected to grow to $11.2 billion in 2010 and further increase to nearly $18 billion in 2015, a compound annual growth rate (CAGR) of 9.9% for the period of 2010 to 2015.

23 Wood Volume Measurement Using Color Images

24 Main steps of the work: Logs detection using color images (the result is a binary image) Noise reduction Detecting objects centers Calculating the volume of the wood by analyzing the sequence of the given pictures

25 Logs detection in color images

26 Noise reduction using erosion

27 Detecting objects centers

28 Calculating the volume of the wood by analyzing the sequence of the given pictures - Compare two images, representing two moments of time, and track logs which centers cross the border:

29 Face detection

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34 A set of eigenfaces can be generated by performing a mathematical process called principal component analysis (PCA) on a large set of images depicting different human faces.principal component analysis Informally, eigenfaces can be considered a set of "standardized face ingredients", derived from statistical analysis of many pictures of faces.statistical analysis Any human face can be considered to be a combination of these standard faces. For example, one's face might be composed of the average face plus 10% from eigenface 1, 55% from eigenface 2, and even -3% from eigenface 3. Remarkably, it does not take many eigenfaces combined together to achieve a fair approximation of most faces.

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37 Face detection

38 High-speed pencil sorting using machine vision Some tracking applications Fish classification system

39 The End


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