ideas for paper or project
1. plan to use GVF Snake Model to segment colon surface from colon volume.
this is the 2D/3D GVF matlab implementation http://www.mathworks.com/matlabcentral/fileexchange/28149
2. use method in visual event recognition in videos by learning from web data to do colon cancer detection: http://videolectures.net/cvpr2010_duan_verv/
3. develop curvature field as new feature for recognition and tracking
4. to deal with rotation affect or detect different direction object, need to use big gradient points as candidate
points which has pretty stable direction as reference sample direction.Thus we can detect different pose objects once. Different size objects can be handled by pyramid methods
5. KD Tree can be used to speed up neural network learning. Refer to Omohundro "efficient algorithm with neural network behavior" journal of complex system, 1987.
Hence KD Tree can be used to speed up Kenji's MTANN learning process.
6. KD Tree is a powerful tool for analyzing data set structure, especially for large scale database. For example, we can
easily find each point's neighborhood. As we know, machine learning algorithm is mainly sensitive to those samples which
lie on the boundary of classifier. However, these samples can be easily found out by KD-Tree. Hence KD-Tree can be expected
to be used to speed up machine learning process by choosing the samples close to boundary with higher priority than samples
far from boundary.
6. Machine learning can be used to predict the reference orientation of fingerprint based on core points.
First,find out the core points in fingerprint image. Then,construct samples which are the windowed regions centered at the core
points.At last, learn the model to predict the orientation.
7. Neural network can be used to model fingerprint orientation model. Using neural network to learn the map from 2D coordinates
to the value of sin or cos functions over the corresponding angles. We can also try other machine learning models.
8. I am planning to test compressive sampling on fingerprint orientation field.It consist of three steps: computing orientation field;
using CS to reconstruct orientation field; check the effect. By doing this, I hope to reconstruct a more smoother orientation field
especially for noisy fingerprint images.
9. Detecting core point method: If draw a circle with the center of core point, the summation of all directions within the circular area would be zero. otherwise versa. how about delta point?
10. 3D component labeling method for colon segmentation: large intestine is a connected component which is biggest in volume in abdomen. Thus avoiding other interference from other organs like small intestine, liver, lung etc.
11. polyp detection method: on colon surface, choosing any voxel and draw a sphere around the voxel. Thus the sphere
must be divided into two parts by colon surface. Compute the volume ratio of colon lumen part over colon outside part.
The ratio is large for polyp but small for flat area.
12. Choose one voxel on colon surface as a candidate point. Compute the inner product of its normal vector and its neighboring voxel's normal vector. Its neighboring voxels must be within a sphere with prescribed radius. For true polyp, the polyp must be
closed by relatively low value of inner product. For false polyp, the point must not be closed by relatively low value of the inner product.It means there are some paths from the center point to the border along which the inner product is relatively big. So we call the polyp detection method "inner product method".
13. local diffusion based fingerprint matching method: 1. Using curvature feature descriptors to find out candidate corresponding pairs; 2. For each pair of candidate points, using them as diffusion centers. Within their respective neighborhood with radius R, searching matching point pairs by comparing their distance and orientation relative to the centers. 3. New added matching point pairs are used as new searching centers. Continue step 2 until no new added matching points. 4. Select the local diffusion map with the largest number of points as correct matching map. Done!
14.singular point detection based on Poincare Index image: 1. given the radius R, consider the circle as the integration path and computer Poincare Index for each pixel. Thus we get PI image. On PI image, singular points correspond to the center of circular area. Good point.
15. polyp detection method: choose any voxel with appropriate bright value(big enough to differentiate from lumen voxel) as an center of an sphere. Draw a sphere around the selected voxel. Compute the intensity histogram and gradient orientation histogram of the voxels inside the sphere as features. Then use the features to detect polyps.
16. Enlarging polyp method: given colon mesh surface, choose any voxel on the surface and find its neighbors. Try to displace all these voxels along the normal directions. If the distance from the center voxel to its neighbors is becoming bigger than that before displacement, the center voxel is displaced with certain distance along the normal direction. Thus, the potential polyp may become bigger and bigger.
17. polyp detection method: Choose any voxel on boundary of binary segmented colon volume. Compute its tangent plane. Consider the chosen voxel as center,draw a circle on the tangent plane. Compute the least distance from the voxels on the periphery of the circle to the voxels on surface as the feature for characterizing the chosen voxel. It can be assumed that for polyps, the least distance is the biggest, otherwise, smaller. We can use machine learning method to learn the suitable threshold to classify the two kinds of voxels. Good!
18. Fingerprint matching based on new information on minutiae: After fingerprint template alignment, check each pair of corresponding minutiae to see if their tails are overlapped. If they are not overlapped, it is a false match. Thus, for true match, it will not affected by this method. For false match, this method may find it and reduce false match. But what is the tail of one minutia? For ending point, its tail is its trunk. For bifurcation point, its tail is its two branches. We can sample a point on its tail when performing minutiae extraction process. Then each minutia has a tailing point accompanying them. It's convenient to check whether tails of matched points are overlapped. All we have to do is check the distance from one tailing point to another tailing point is less than a predefined threshold. Done!
19 AM-FM model can be used to enhance fingerprint image and detection singular points.
20. new fingerprint orientation estimation method: 1. For each pixel, compute the fourier transform of its rectangular neighborhood, i.e. 9*9 or 16*16; 2. fit the spectral data with mixing gauss model with two peaks. 3. the parameters of the found model convey the orientation of the central pixel. Thus it is hoped to get better orientation estimation.
21. fingerprint SP detection method: in high curvatured area, for each pixel, compute the fourier transformation of its rectangular neighborhood, consider the FT data as feature vector, use svm to classify them as SP or Non-SP
22. Define a new fingerprint reference point as the focus point of parabola obtained by interpolating the three points on the same ridge. Evaluate the robustness of the reference points. Using different reference points, a reference direction may be attained by connect them or fitting them linearly. Steps involved:1. choose three points in ridge; 2. interpolate them using parabola; 3. find out the parabolic focus point; 4. find out multiple of reference points from different ridges; 4. fitting different reference points linearly constituting reference line. I guess the reference line is robust to nonlinear transformation accompanying fingerprint images. Tell student to do it and submit to PR or PRL.
23. Inpainting algorithm can be used to predict the corrupted orientation field: 1. estimate good quality orientation blocks. 2. using inpainting algorithm to fill the bad quality blocks.
24. Learning Reconstruction Dictionary: min_{a,D}\sum_{l=1}^{M}||x_{l}-Da_{l}||^2 s.t. ||a_l||_0<=L. K-SVD algorithm for this optimization problem. We propose to learn the most sparsest Dictionary: min_{a,D}{max{||a_i||_{l_0}, i=1,2,...,N}} s.t. ||X-Da||_F^2<=episilon.
Steps: 1. initialize D by selecting patch images randomly from training set. 2. find the sparsest a using OMP algorithm. 3. find the most densest vector a_i and corresponding x_i, denote the bigest element value a_i^j in vector a_i, corresponding D_j; 4. update D_j=D_j+t(x_i-D_j), where parameter t in [0, 1]. Compute new a using new D. If the ||a||_l_0 decreases, update is valid, otherwise cancel the update and find less denser vector a_i, repeat step 3, 4. If no valid update, stop. otherwise repeat step 1-4 again.
These two learning methods get different results. This can be shown in 2 dimension data.\
25. Surprisingly, it seems possible to prove the sensing matrix \phy is Gaussian random distribution under orthogonal sparse representation matrix \psy. In order to minimize mutual coherence of equivalent matrix D, D must be uniformly distributed in unit hypersphere surface. Thus D must be Gaussian random distributed according to the conclusions in http: //mathworld.wolfram.com/HyperspherePointPicking.html. If we can prove that the product of Gaussian matrix D and the inverse of orthogonal matrix \psy is also Gaussian distributed. Then the sensing matrix \phy =D*inverse of \psy is Gaussian distributed. It's amazing!
26. To construct equivalent dictionary with K columns, we can construct 2K equally spaced points on unit hypersphere surface using the method in http://www.rhinocerus.net/forum/lang-idl-pvwave/117743-equally-spaced-points-hypersphere.html. Then delete K points which has the largest inner products. The left K points constitute th equivalent dictionary. It's cool.
27. How to construct M*N sensing matrix? we have a idea (force balance method) on that: 1)randomly generate N vectors in M dimenstional space. 2)Normalize to unit norm vectors. 3)These points distributed on the surface of unit ball. 4) Assume that each point exerts a force to other points, this force is proportional to the distance between any two points. The direction of force is along the line connecting these two points. 5) add all forces on each point to get the final force on the point. Move all points with the force on the ball surface. Repeat step 4) and 5) until no point moves. This is the method to construct optimal sensing matrix.
28. An easy way to construct sensing matrix with optimal coherence value using gaussian distribution. 1) for M*N sensing matrix, generate 2*N( probably more) unit random vectors in M dimensional space. 2) exclude the vector which has the biggest correlation value.
29. Super-resolution: 1) use cubic interpolattion to magnify images to the desired degree, get magnified image y; 2)model high resolution image x with normal distribution; 3)model observation process as y=h*x+n, where h is a parameter matrix and n is noise. h can be learned using some samples. 4) using beyesian inference method to estimate x for given y, thus we get high resolution image.
30. measuring matrix \phi design: 1)for given k sparse signal in R^N, contruct final sensing matrix \phipsi by choosing N points in R^k uniformly distributed on unit sphere surface as the column vectors of final sensing matrix \phipsi. 2) choose orthonormal basis \psi under which signal has sparse representation. 3)Get measuring matrix \phi by multiplying \phipsi with the inverse of \psi. Thus we complete the measuring matrix design process with given orthonormal basis. It can be extended to the case where dictionary is learned. Excellent.
31. we can use existing sensing matrix learning method to learn a binary sensing matrix: 1)learn a nonbinary sensing matrix;2)find a binary sensing matrix closest to the learned matrix by finding a binary vector which is closest to the line determined by the nonbinary colomn vector of learned sensing matrix.
32. Marting suggest that using blurred version of image to do CS reconstruction works better than using the original image without blur. Mathematically, y=\phi*S*x, where s is a smoothing filter.
33. On our PMMWI system, consider a subpixel movement when moving masks to obtain observations. Thus, we can get a different modulation method from the Hadamard matrix. We may get better results
34. SP Detection: Using convergence index filter for SP detection. refer to the paper "Convergence index filter for vector fields", 1999, IEEE TIP.
35. We can use error bar from Bayesian Compressive Sensing method to adaptively select the linear projection (i.e., the rows of the matrix \phi) to reduce uncertainty in the signal.
36. For compressive sensing model: y=\phi*x+n; we can estimate x with \phi known. we can try to estimate \phi and x simultaneously and compare the results from these two methods to see which x is better. Interesting.
37. For compressive sensing model: y=\phi*x+n; we can estimate x with \phi known. we can try to estimate h and x simultaneously for this new model y=\phi*h*x+n with \phi known and compare the results from these two methods to see which x is better and what the h is like. Interesting.
38. Sparse or smoothing assumption can be used to enhance image such as fluorescent microscopy cell images. This is Bayesian framework to enhance image. We can refer to the reference "Variational Bayesian Blind Deconvolution Using a Total Variation Prior". This is new? We have test it on cell image and get nice results.
39. We can do compressive sensing and debluring simultaneously. the formula is y=\phi*h*x+n. To estimate h and x with y known, we will formulate this problem as a compressive sensing reconstruction problem alternatively( first give initial h, compute x; then with x, compute h. and so on) reference paper "blind motion debluring from a single image using sparse approximation. We will use the Bayesian framework since we have the codes of its implement.
40. Using graph cut to do compressive sensing reconstruction. It is a graph cut with multilabel. The penalty for designating label is the error between the observation and the observation obtained from the label just assigned.
41. Using haar wavelet as basis to do passive milimeter wave compressive imaging based on Bayesian reconstruction and Hardamard Matrix. This is for APL.
42. Using the technique of locating the important wavelet coefficients to reduce the number of measurements in CS. Or explore the structure pattern in wavelet domain. refer to papers from purdue University. For APL.
43. Since we have the set of possible Hadamard sensing vectors \phi and some orthornormal basis \psi (Fourier basis, wavelet basis). We can choose the appropriate sensing vectors from the set \phi while making the incoherence between sensing vector and basis minimum. Or we can choose the sensing vector V which maximizes the product V*\psi*S. For APL
44. Graph cut for fingerprint orientation field estimation. or reference to the other paper "Gauss-Markov measure field models" for Gauss Markov Measure Field Model.
Gauss-Markov Measure Field Models for Low-Level Vision45. Reference to the PAMI paper "Markov Random Field Model for Directional Field and Singularity Extraction in Fingerprint Images". Using graph cut to do orientation field estimation and using complex rational polynomial for standard singularity template to do singular point estimation. For PAMI. 46. We can do compressive sensing and super resolution simultaneously. Probably we should give the initial image estimation using first order derivative high pass filter matrix to regularize the psuedoinverse of the sensing matrix. reference to the paper "Sparse bayesian image restoration". TIP 47. we can do blind deconvolution Using Gaussian distribution prior on the output of high pass filter called first order derivative. reference the papers " Sparse bayesian image restoration" and "Variational Bayesian Blind Deconvolution Using a Total Variation Prior". For TIP. 48. Fingerprint mathching, reference "A new algorithm for non rigid point matching" 49. Iterative Reweighted Least Square blind deconvolution used to do compressive sensing. using alternating optimization technique. refer to "Iterative reweighted least squares" 50. image segmentation model: min |g-f|^2+lamda*(|df/dx|^0+|df/dy|^0). refer to "Image smoothing via L0 Gradient Minimization" 51. using MMV model to do image compressive sensing: Y=\phi*X+V. X-input image, considered as multiple column signal using zhang zhilin MMV matlab model to recover the image. Thus avoid the super big size matrix (\phi) problem for large size image. 52. compressive sensing and deconvolution based on L0 gradient minimization. Refer to "Image Smoothing via L0 gradient Minimization" 53. compressive sensing using objective function: \sum log(|x_i|^2). It approach L0 norm. we can solve the optimization problem using Iterative Reweighted Least Square. 54. Using geometric mean function as compressive sensing objective function:[ (x1+e)*(x2+e)*(x3+e)*...*(x_n+e)]^(1/n). subject to y=\phi*x; That object function is concave. 55. Using prior p(x)=e^{-a\sum_{i,k} p(g_{i,k}*x)} to do deconvolution or compressive sensing (where p(x) may be l1 norm) based on Bayessian framework using maximizing a posteriori probability. reference to "Sparse bayesian image restoration" and " deconvolution using natural imge priors". 56. graph based cell segmentation based on ellipse prior. reference to "Efficient Graph-based image segmentation". If data match ellipse model, the value of K in the mentioned paper is set to small value, vice versal. ellipse parameter can be computed using matlab command "regionprops", and reference the webpage http://blogs.mathworks.com/steve/2010/07/30/visualizing-regionprops-ellipse-measurements/, and http://www.mathworks.com/help/toolbox/images/ref/regionprops.html project: 1. abnormal event recognition using visual event recognition in videos: http://videolectures.net/cvpr2010_duan_verv/ and modeling mutual context of object and human pose : http://videolectures.net/cvpr2010_fei_fei_mmco/ 2. fingerprint orientation field model; min \sum[arg(P(z))-arg(Q(z))-2*\theta], using gradient descent method. We develop rough gradient descent method: determine the sign of object function gradient by difference computation in two specific points,one of which is the initial known parameter and another is the perturbed parameters. The new gradient descending method is applicable in case of no object gradient available. 3. Create a new camera with binocular lens for the photogrammetry. share one CCD sensor but taking two successive pictures in short time. Then compute two perspective centers using image processing method with collinearity constraints under image reference coordinate system. This camera only needs to estimate the 3d coordinates for the two perspective centers of two lens once. Later it can be used everywhere, anytime. Furthermore, it doesn't depend on the position and orientation of camera. Very convenient. This is for new project or patent.
