Invariant object recognition based on extended fragments

Invariant object recognition based on extended fragments

Visual appearance of natural objects is profoundly affected by viewing conditions such as viewpoint and illumination. Human subjects can nevertheless compensate well for variations in these viewing conditions. The strategies that the visual system uses to accomplish this are largely unclear. Previous computational studies have suggested that in principle, certain types of object fragments (rath...

Object Recognition based on Local Steering Kernel and SVM

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

Invariant Object Recognition Using Radon-based Transform

The properties of the Radon transform are used to derive the transformation invariant to translation, rotation and scaling. The invariant transformation involves translation compensation, angle representation and 1-D Fourier transform. The new object recognition method is studied experimentally in two domains, mammogram labels recognition and face recognition. For mammogram labels, the recognit...

Flexible object recognition based on invariant theory and agent technology

Illumination invariant object recognition

Varying illumination is a severe problem for existing face recognition algorithms. Altering the light direction from left to right, for example, causes a change of contrast in large face regions and lets most face recognition algorithms fail. Theoretical results, based on the law of incoherent light superposition, provide the solid ground on which a new illumination invariant recognition algori...