Texture classification using invariant features of local textures

IFLT Based Real-Time Framework for Image Matching

“…Each keypoint-region is cut-out and resampled and Gaussian smoothed relative to the octave where that particular keypoint was detected. A rotation invariant descriptor, Invariant Features of Local Textures (IFLT) [12] descriptors are derived for every keypoint-region thereby eliminating the dependency on rotation normalisation. Thus, we can afford to discard duplicate keypoints.…”

“…Invariant Features of Local Textures (IFLT) [12] is a texture descriptor that is rotation and partially-illumination 1 invariant. First order finite directional differences in all directions with respect to a centre pixel are calculated, and Euclidean-normalised and Haar-wavelet filtered to obtain a texture measure.…”

“…In this work, we use 128 bin histograms. The reader is referred to [12] for details. A simple flow-diagram of IFLT is provided in Figure 2.…”

“…A simple flow-diagram of IFLT is provided in Figure 2. These descriptors are inherently intensity and rotation invariant for a small 3 × 3 neighborhood of pixels [12]. The directional differences retain the structure integrity which can be harnessed for image-matching purposes.…”

“…Can duplicate keypoints be discarded? In this paper, we propose a framework for imagematching using only unique keypoints, consisting of Hessian-Laplacian detectors and Invariant Features of Local Textures (IFLT) [12] descriptors. We present the new framework in section II and followed by experimental analysis in section III.…”