Accelerated Computer Vision in MATLAB
Filed under: Algorithms, Code Optimization, Image processing
A recent post on the AccelerEyes’ blog discusses computer vision algorithm acceleration in MATLAB. The key problem in using MATLAB for computer vision is that:
“Matlab and the M language is great for linear algebra where blocks of matrices are the typical access pattern, but not for Computer Vision where algorithms typically operate on patches of imagery.”
In order to get around this problem, a windows function is introduced:
“The command windows signals to Jacket that we’re doing a patched access pattern that can then be optimized on the GPU.”
Anyway, this may be interesting for those involved in computer vision and interested in GPUs!
Popularity: 2% [?]
Hidden Bits In Wavelet Domain
A possible domain for watermark embedding is that of the wavelet domain. The Discrete Wavelet Transform separates an image into a lower resolution approximation image as well as horizontal, vertical and diagonal detail components. The process can then be repeated to computes multiple “scale” wavelet decomposition. One of the many advantages over the wavelet transform is that that it is believed to more accurately model aspects of the Human Visual System (HVS) as compared to the FFT or DCT. This allows us to use higher energy watermarks in regions that the HVS is known to be less sensitive to, such as the high resolution detail bands. Embedding watermarks in these regions allow us to increase the robustness of our watermark, at little to no additional impact on image quality. One of the most straightforward techniques is the embedding of a CDMA sequence in the detail bands. The wavelet domain as well proved to be highly resistant to both compression and noise, with minimal amounts of visual degradation. This is all the more impressive when one considers that the wavelet technique described here is one of the most primitive currently known. More sophisticated wavelet-domain techniques will almost certainly improve on both of these, and hopefully lower it’s computational requirements. The wavelet domain may be one of the most promising domains for digital watermarking yet found.
We have developed a new scheme for the embedding of watermark sequence with high capacity, using a multilevel approach for coefficients selection.
Index Terms: Matlab, source, code, wavelet, watermarking, capacity, human, visual, system, multilevel.
 |
Figure 1. Visible watermark |
||||||||||||||
|
A simple and effective source code for High-Capacity Wavelet Based Watermarking. |
|||||||||||||||
 |
Demo code (protected P-files) available for performance evaluation. Matlab Wavelet Toolbox is required. |
||||||||||||||
|
Release
|
Date
|
Major features
|
|||||||||||||
|
1.0
|
2009.06.11 |
|
|||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
High-Capacity Wavelet Based Watermarking. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 100 EUROS (less than 140 U.S. Dollars).
|
|||||||||||||||
|
Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of High-Capacity Wavelet Based Watermarking. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Wavelet Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 3% [?]
A Matlab toolbox implementing Level Set Methods
Baris Sumengen, Vision Research Lab at UC Santa Barbara
This set of Matlab files implements Level Set Methods and follows Osher and Fedkiw’s book. A combination of curvature-based forces, vector field-based forces and forces in the normal direction can be used.
Download: Download by clicking here. This package consist of a set of m-files compressed into a zip file. Unzip them to a folder and then add this folder to Matlab’s path.
for more click here
Popularity: 2% [?]
LSB Based Steganography
Steganography is an ancient art of conveying messages in a secret way that only the receiver knows the existence of message. So, a fundamental requirement for a stegano- graphic method is imperceptibility; this means that the embedded messages should not be discernible to the human eye. There are two other requirements, one is to maximize the embedding capacity, and the other is security. The least-significant bit (LSB) insertion method is the most common and easiest method for embedding messages in an image. However, how to decide on the maximal embedding capacity for each pixel is still an open issue. An image steganographic model is proposed that is based on variable-sized LSB insertion to maximise the embedding capacity while maintaining the image fidelity. For each pixel of a gray-scale image, at least 4 bits can be used for messages embedding. First, according to contrast and luminance characteristics, the capacity evaluation is provided to estimate the maximum embedding capacity of each pixel. Then, the minimum-error replacement method is adapted to find a gray-scale as close to the original one as possible.
Index Terms: Matlab, source, code, LSB, least, significant, bit, steganography.
 |
Figure 1. Bitstream |
||||||||||||||
|
A simple and effective source code for High Capacity Image Steganographic Model. |
|||||||||||||||
| |
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
|
||||||||||||||
|
Release
|
Date
|
Major features
|
|||||||||||||
|
1.0
|
2009.06.20
|
|
|||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
High Capacity Image Steganographic Model. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 100 EUROS (less than 140 U.S. Dollars).
|
|||||||||||||||
|
Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of High Capacity Image Steganographic Model. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 8% [?]
Fast and Accurate Face Identification Using Overlapping DCT
In the JPEG image compression algorithm, the input image is divided into 8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each block. The DCT coefficients are then quantized, coded, and transmitted. The JPEG receiver (or JPEG file reader) decodes the quantized DCT coefficients, computes the inverse two-dimensional DCT of each block, and then puts the blocks back together into a single image. For typical images, many of the DCT coefficients have values close to zero; these coefficients can be discarded without seriously affecting the quality of the reconstructed image. Such algorithm results particularly robust also for face identification. Moreover the 2D DCT operator can be applied to overlapping data.
The extracted feature vectors are used as input to a simple nearest neighbor algorithm. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors. k is a positive integer, typically small. If k = 1, then the object is simply assigned to the class of its nearest neighbor. In binary (two class) classification problems, it is helpful to choose k to be an odd number as this avoids difficulties with tied votes. The same method can be used for regression, by simply assigning the property value for the object to be the average of the values of its k nearest neighbors. It can be useful to weight the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the correct classification (or, in the case of regression, the value of the property) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required. In order to identify neighbors, the objects are represented by position vectors in a multidimensional feature space. It is usual to use the Euclidean distance, though other distance measures, such as the Manhattan distance could in principle be used instead. The k-nearest neighbor algorithm is sensitive to the local structure of the data.
The code has been tested with AT&T database achieving an excellent recognition rate of 99.20% (40 classes, 5 training images and 5 test images for each class, hence there are 200 training images and 200 test images in total randomly selected and no overlap exists between the training and test images).
Index Terms: Matlab, source, code, face recognition, face matching, face verification, dct, k-nearest neighbor algorithm, knn, discrete cosine transform.
 |
Figure 1. Example of k-NN classification |
||||||||||||||
|
A simple and effective source code for Face Recognition. |
|||||||||||||||
| |
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required. |
||||||||||||||
|
Release
|
Date
|
Major features
|
|||||||||||||
|
1.0
|
2007.10.20 |
|
|||||||||||||
|
We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
|||||||||||||||
|
Face Recognition Based On Overlapping DCT – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 250 EUROS (less than 350 U.S. Dollars).
|
|||||||||||||||
|
Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Face Recognition Based On Overlapping DCT. Alternatively, you can bestow using our banking coordinates:
|
|||||||||||||||
The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab Release 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 3% [?]
Translator
By N2H Follow Us
Video Posts
-
