What is texture Texture can be considered to
What is texture ? Texture can be considered to be repeating patterns of local variation of pixel intensities. Brodatz Textures Vistex Textures
Human Invented Algorithms Texture feature extraction algorithms can be grouped as follows* • Statistical • Geometrical • Model based • Signal Processing *Tuceryan and Jain, “Texture Analysis” in The Handbook of Pattern Recognition and Computer Vis World Scientific, 2 nd edn. , 1998
Statistical Methods • • • Local features Autoregressive Galloway – run length matrix Haralick – co-occurrence matrix Unser Sun and Wee Amadasun Dapeng Amalung
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:
Co-occurrence matrix function M=get. Co. Matrix(M 1, M 2) M = zeros(2); % 2 x 2 result matrix for binary image. [r, c] = size(M 1); for i=1: r for j=1: c v 1 = M 1(i, j)+1; % Add one to binary image values to get Matlab indices. v 2 = M 2(i, j)+1; M(v 1, v 2) = M(v 1, v 2)+1; % Increment co-occurrence value. end
Outline What is watermarking? Watermarking vs. Steganography vs. Cryptography Application of watermarking Focus on content authentication Properties of watermarking schemes
What is a watermark? Watermarking is an important mechanism applied to physical objects like bills, papers, garment labels, product packing… Physical objects can be watermarked using special dyes and inks or during paper manufacturing.
Characteristics of watermarks The watermark is hidden from view during normal use, only become visible by adopting a special viewing process. E. g. hold the bill up to light The watermark carries information about the object in which it is hidden. E. g. the authenticity of the bill E. g. the trademark of the paper manufacturer
IPR related information technologies Data hiding Steganography Imperceptible data embedding Non-robust data embedding Watermarking Visible data embedding Robust data embedding Imperceptible watermarking Fragile watermarking Visible watermarking Robust watermarking Semi-fragile watermarking
Information hiding Data hiding Containing a large range of problem beyond that of embedding message in content Making the information imperceptible – E. g. watermarking Keeping the existence of information secret – E. g. anonymous usage of network – E. g. hiding portions of database for non-privileged users
Steganography A term derived from the Greek words “steganos” and “graphia” (The two words mean “covered” and “writing”, respectively) The art of concealed communication. The very existence of a message is kept secret. E. g. a story from Herodotus Military Messages tatooed on the scalp of a slave
Watermarking v. s. Steganography Watermark messages contain information related the cover work In steganographic systems, the very existence of the message is kept secret. If the message tatooed on the slave is “the slave belongs to somebody”, then we can regard it as an example of watermarking
Classification of information hiding systems Cover Work Dependent Message Cover Work Independent Message Existence Hidden Steganographic Watermarking Covert Communication Existence Known Non-Steganographic Watermarking Overt Embedded Communication
Importance of digital watermarking The sudden increase in watermarking interest is most likely due to the increase in concern over copyright protection of content copyright-protected digital contents are easily recorded and distributed due to: prevalence of high-capacity digital recording devices the explosive growth in using Internet
Watermarking v. s. cryptography Cryptography is the most common method of protecting digital content and is one of the best developed science. However, encryption cannot help the seller monitor how a legitimate customer handles the content after decryption. Digital watermarking can protect content even after it is decrypted. Encryption ? Decryption Under Protection
Definitions about digital watermarking Digital watermarking: The practice of imperceptually alternating a Work to embed a message about the Work. Related terms Work: a specific copy of some electronic signal, such as a song, a video sequence, or a picture Cover Work: the original un-watermarked work Watermark: the messages being embedded, indicating some information about the work
A digital watermarking system Cover Work Watermark Embedder Watermark Message Watermarked Work Watermark Detector Recording, transmissions, or processing Detected Watermark Message
Applications of digital watermarking Owner identification Proof of ownership Broadcast monitoring Transaction tracking Copy control Device control Focus : Content authentication Forensic use of watermarking
Owner identification (I) Under the U. S. law, although the copyright notice is not required in every distributed copy to protect the rights of copyright holders, the award to the copyright holders whose work is misused will be significantly limited without a copyright notice found on the distributed materials. Traditional textual copyright notices “Copyright date owner” “© date owner” “Copr. date owner”
Owner identification (II) Disadvantages for textual copyright notices Easily removed from a document when it is copied E. g. the Lena Sjooblom picture (see the next slide) Copyright notices printed on the physical medium are not copied along with the digital content E. g. the Music CD Occupying a portion of the image and aesthetically reducing the value of artworks Since watermarks are imperceptible and inseparable from the work, they are obviously superior to textual copyright notices.
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.
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). 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. 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.
General watermark life-cycle phases with embedding-, attacking- and detection/retrieval functions
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.
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. 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.
MSB of the Image Second plane Third plane Fourth plane This Plane Contains Minimal Information Fifth plane Sixth plane Seventh plane LSB
Cover and Stego image for LSB Substituti on MSBof the. Image Secondplane Thirdplane Fourthplane Cover Image 50 50 100 150 200 250 LSBBits. Replacedby. Binary. Image Fifthplane Sixthplane Seventhplane Stego Image 50 100 150 200 250 Histogram of Cover Image LSB 800 600 400 200 0 0 100 200 150 200 250 Histogram of Stego Image 800 0 50 0 100 200
Stego Image Cover Image 50 50 100 150 200 250
• The global market for machine vision system components was $10. 3 billion in 2009, slightly more than the market The global market for machine vision system components was $10. 3 billion in figure for 2008, which was nearly $9. 9 billion. This is 2009, slightly more than the market figure for 2008, which was nearly $9. 9 billion. expected to grow to $11. 2 billion in 2010 and further 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. Market 20 18 16 14 12 10 8 6 4 2 0 Market 2008 2009 2010 … 2015
Wood Volume Measurement Using Color Images
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
Logs detection in color images
Noise reduction using erosion
Detecting objects centers
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:
Face detection
Face detection
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. Informally, eigenfaces can be considered a set of "standardized face ingredients", derived from statistical analysis of many pictures of faces. 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.
Face detection
Research on paper surface
Paper roughness measurement using machine vision
Environmental monitoring using machine vision
Ozone hole monitoring Image taken 9 / 1999 Image taken 9 / 2001
Calculation of trees in forests
Monitoring of cucumber using spectral imaging
Cameras looking right and left in the front of a car
BLIS (Blind Spot Information System) Velocity difference: > 20 km/h or < 70 km/h
Lane departure warning
Night vision in cars Head-up display (HUD) in cars
The End
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