We propose a compact descriptor for the purpose of dense image matching and object recognition. The descriptor is calculated based on local gradients about each point in an image. It contains the averages of gradients at four different windows surrounding a center point. The descriptor is calculated much faster than histogram of oriented gradients (HOG). Additionally, it will be shown that it i...

Four novel color Local Binary Pattern (LBP) descriptors are presented in this chapter for scene image and image texture classification with applications to image search and retrieval. Specifically, the first color LBP descriptor, the oRGB-LBP descriptor, is derived by concatenating the LBP features of the component images in an opponent color space — the oRGB color space. The other three color ...

Local surface description is a critical stage for surface matching. This paper presents a highly distinctive local surface descriptor, namely TriSI. From a keypoint, we first construct a unique and repeatable local reference frame (LRF) using all the points lying on the local surface. We then generate three spin images from the three coordinate axes of the LRF. These spin images are concatenate...

This paper proposes a novel approach using two-dimensional principal component analysis (2D-PCA) and local direction descriptor for face recognition. The proposed method utilizes the transformed image obtained from local direction descriptor as the direct input image of 2D-PCA algorithms. The performance comparison was performed using principal component analysis (PCA) and Gabor-wavelets based ...

Probabilistic models with hidden variables such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have recently become popular for solving several image content analysis tasks. In this work we will use a pLSA model to represent images for performing scene classification. We evaluate the influence of the type of local feature descriptor in this context and co...

The topological step of a subdivision scheme can be described as a refinement on regular tiling lattices, or more generally as a local transformation descriptor. The former classifies all the regular lattice transformations which give rise to other regular lattices. These regular lattice descriptors are limited by the type of the control mesh faces, the subdivided mesh must be composed by faces...

In this chapter we examine several concepts related to local feature descriptor design— namely local patterns, shapes, spectra, distance functions, classification, matching, and object recognition. The main focus is local feature metrics, as shown in Figure 4-1. This discussion follows the general vision taxonomy that will be presented in Chapter 5, and includes key fundamentals for understandi...

For many years, various local descriptors that are insensitive to geometric changes such as viewpoint, rotation, and scale changes, have been attracting attention due to their promising performance. However, most existing local descriptors including the SIFT (Scale Invariant Feature Transform) are based on luminance information rather than color information thereby resulting in instability to p...

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (L...

This paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching. The proposed descriptor consists of two-component feature vectors. First, the local region is separated into sectors and their gradient magnitude and orientation values are extracted; a feature vector is then constructed from these...