response. Such an architecture, sensitive to local spatial discontinuities, is particularly well-suited to detecting locations which stand out from their surround and is a general computational principle in the retina, lateral geniculate nucleus, and primary visual cortex [11]. Center-surround is implemented in the model as the difference between and coarse scales: The center is a pixel at scale c ε {2, 3, 4}, and the surround is the corresponding pixel at scale s = c + δ, with δ ε{3, 4}. The across-scale difference between two maps, denoted ϴ below, is obtained by interpolation to the finer scale and point-by-point subtraction. Using several scales not only for c but also for δ = s - c yields truly multiscale feature extraction, by including different size ratios between the center and surround regions. Extraction of visual features With r, g, and b being the red, green, and blue channels of the input image, an intensity image I is obtained as I = (r + g + b)/3. I is used to create a Gaussian pyramid I(σ), where σ ε[0..8] is the scale. The r, g, and b channels are normalized by I in order to decouple hue from intensity. However, because hue variations are not perceivable at very low luminance (and hence are not salient),normalization is only applied at the locations where I is larger than 1/10 of its maximum over the entire image (other locations yield zero r, g, and b). Four color channels are created: R = r - (g + b)/2 for red, G = g - (r + b)/2 for green, B = b - (r + g)/2 for blue, and Y = (r + g)/2 - |r - g|/2 - b for yellow (negative values are set to zero). Four Gaussian pyramids R(σ), G(σ), B(σ), and Y(σ) are created from these color channels I(c,s)=|I(c)ϴI(s)|
Ha: ρ ≠ 0 (there is some correlation; The Alternative Hypothesis) By using the coefficient of determination algorithm, the comparison was made between the training and testing images. The most important thing in object recognition system is to differentiate between the two objects. The test image will be between different objects so that the accuracy of the algorithm can be calculated. The train image of different object is saving to folder train database. Then the test image will be stored in different folder in the name of test database. Then by applying the algorithm of coefficient of determination the comparison was made between the different objects stored in two database. Figure 4.4 Train Image Folder for Different Train Image Then the testing images is saving to a folder Testing Database which consists of different images of chairs, tables and furniture's. The images are stored after the saliency was done. Then the comparison was made between the two folder by using the coefficient of determination algorithm. Figure 4.5 Test Image Folder for different test image 4.3 Graphical User Interface Design A graphical user interface (GUI) can be describe as a graphical display that contains devices, or components, that enable a user to perform interactive tasks without creating a script or type commands at the command line. These components can be push buttons menus, toggle buttons, toolbars, checkboxes, radio buttons and sliders etc.
Figure 5.3 35 degree Orientation The following figures shows the orientation in 125 degree for different chairs, tables and furniture's. ORIGINAL IMAGE INTENSITY Figure 5.4 125 degree orientation The following figures shows the orientation in 90 degree for different chairs, tables and furniture's. ORIGINAL IMAGE INTENSITY