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Projection features

In document Óbuda University (Pldal 25-28)

Similar low-level features could be constructed based on the sum of intensities for every column and row of the patch. These sums, the horizontal and vertical projec-tions, are one-dimensional vectors mapped from the 2D image.

Applications of row and column sum vectors are first described by Herbert J. Ryser in 1957 [37], which is referred as one of the first discrete tomography [38, 39] methods: based on the two orthogonal projections, the original binary matrix is reconstructable. Reconstruction from a small number of projections can yield mul-tiple valid results. It is notable that, in the case of a non-binary domain of values, the number of possible solutions of the reconstruction method can be lowered by more projections.

While reconstruction is important in medical applications [40], for object im-age matching the projection functions themselves could be used. Margrit Betke et al. presented a method for vehicle detection and tracking [41], where projection vectors are calculated of an edge map and are used to adjust the position during tracking.

In other applications, projections can be used for gait analysis for walking people [42], where the video sequence is transformed into a spatio-temporal 2D represen-tation. Patterns in this so-called Frieze-group [43] representation can be analyzed using horizontal and vertical projections. As an extension, a variation of this method could be developed based only on the shape information [44], resulting in a method that was not affected by body appearance.

The idea of using a mapping of the vehicle observation for further processing also appeared in [45], where the distance calculation between observations were based on edge maps.

1.2.1 4D signature calculation

Vedran Jelača et al. [46] presented a solution based on projections for vehicle match-ing in tunnels, where object signatures are calculated from projection profiles. A possible interpretation of this method is presented here in detail.

After the region of interest is selected, the area is completed to a square and cropped. As color data are irrelevant in dark, artificially lighted areas such as tunnels, the images are grayscaled, meaning that the information is simplified from a RGB structure to a single intensity value. In the case of 8-bit grayscale images, the intensity information of one pixel is stored in one single byte.

Each image can be handled as matrixI∈NN×N whereIi,j = [0,1, . . .255] denotes the element of the matrix at (i;j). The horizontal (πH) and vertical projections (πV) for a squaredN ×N matrix result in vectors with the same length:

dimπH = dimπV =N. (1.1)

These projections are the averaged sums of the rows and columns of the matrix, normalized to [0,1] by the value of maximal possible intensity:

πH(i) = 1





Figure 1.1. Horizontal (a) and vertical (b) image projections are calculated by summarizing the row and column values in the matrix.





Figure 1.2. Diagonal (a) and antidiagonal (b) image projections of a squared image matrix.

visualized in Figure 1.1.

πH,πV, therefore, defines the two-dimensional projection signature of the object:

S2 = (πH,πV). (1.3)

The diagonal and antidiagonal projections can be calculated likewise, but it is important to point out that the number of elements for each projected value differs (Figure 1.2).

The length of the diagonal projection vectors are:

dimπD = dimπA= 2N −1, (1.4)

the number of elements in each summarization is based on the distance from the main diagonal:

ElemNum(i) =

i if iN

2N −i otherwise (1.5)

wherei is the index of an element in a diagonal projection, having i≤2N −1.

The calculation of the diagonal projections πD,πA is formalized as:

These projection vectors together provide the 4D projection signature of the object:

S4 = (πH,πV,πD,πA). (1.7) After normalizing the values using the number of elements that make up the sum, multiplied with the maximum intensity value 255, the domain of the projection function is [0; 1]. The 4D signature of a vehicle observation is visualized in Fig. 1.3.

1.2.2 Projection-based object matching

As the size of the input images could be different, the length of projection func-tions is different as well. To be able to match these funcfunc-tions properly a similarity measurement method must be defined.

Cropped image

Figure 1.3. The visualized projection signature of a vehicle observation. Subfigure (a) shows the squared image of a rear-viewed vehicle. In diagrams (b), (c), (d) and (e), the horizontal, vertical, diagonal and antidiagonal projections are visualized (πH, πV, πD and πA, respectively).

To calculate the alignment of the functions, the method suggested in [46] is to align the projection functions globally, and then fine-tune with a "local alignment"

using a method similar to the Iterative Closest Point [47, 48].

First, the signature vectors are rescaled based on the camera settings and the position of the object in the image, so the effects of zoom and camera settings are corrected. After the signatures are resized, it is still necessary to align the signatures because of shifting and length differences.

The functions are compared based on the sliding window technique: the shorter function is moved over the longer function, and each correlation coefficient is calcu-lated using the Pearson correlation coefficient (PCC) formula:

ρl(x,y, s) = cov(x,ys)

σ(x)σ(ys), (1.8)

where x,y are projection vectors, where dimx ≤ dimy. ys represents the part of vectory, which is shifted by s and overlaps x.

Basically, the ρl(x,y, s) correlation coefficients are calculated for each step s, where the number of steps is dimy−dimx. cov(x,ys) means the covariance between the two vectors and σ indicates the standard deviation.

The highest value maxsρl(x,y, s) is selected asρ, defining the similarity ofxand y. For horizontal, vertical, diagonal and antidiagonal projection functions, notations ρH,ρV, ρD and ρA are used, respectively.

The local alignment suggested in [46] was based on signature smoothing followed by curve alignment: step-by-step iteratively removing the extremas caused by noise and finding the best fit for two functions.

The range of the values are mapped to [−1; 1], which could be easily handled: the higher the coefficient, the better the match. After all similarity values are calculated for the projections, the result values are filtered with a rectifier, setting all negative values to zero:

r(v) =

v if v >0

0 otherwise = max(0, v) (1.9)

Negative correlation values mean that the changes of one function affect an op-posite change on the other function, meaning that the relationship between the two is inverse. So, in this case, the penalization of the negative correlation is necessary, because projection inverses should not be relevant at all.

The suggestion in [46] is to equally handle each dimension of the data, by using the Euclidean norm (L2 norm). A single similarity value µ is calculated from the 4D signature as



2 (1.10)

where 2 is the square root of the dimension number, therefore, normalizing the norm.

In document Óbuda University (Pldal 25-28)