Material recognition has several applications, such as image retrieval, object recognition and robotic manipulation. To make the material classification more suitable for real-world applications, it is fundamental to satisfy two characteristics: robustness to scale and to pose variations. In this study, the authors propose a novel discriminant descriptor for texture classification based on a ne...

We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features around a given point in an image. We also present a face alignment algorithm based on regression using these local descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize face la...

A novel Sparsely Encoded Local Descriptor (SELD) is proposed for face verification. Different from traditional hard or soft quantization methods, we exploit linear regression (LR) model with sparsity and non-negativity constraints to extract more discriminative features (i.e. sparse codes) from local image patches sampled pixel-wisely. Sum-pooling is then imposed to integrate all the sparse cod...

During the last decade a set of surface descriptors have been presented describing local surface features. Recent approaches [COO15] have shown that augmenting local descriptors with topological information improves the correspondence and segmentation quality. In this paper we build upon the work of Tevs et al. [TBW∗11] and Sun and Abidi [SA01] by presenting a surface descriptor which captures ...

Classifying color textures under varying illumination sources remains challenging. To address this issue, this paper introduces a new descriptor for color texture classification, which is robust to changes in the scene illumination. The proposed descriptor, named Color Intensity Local Mapped Pattern (CILMP), incorporates relevant information about the color and texture patterns from the image i...

The most important bottleneck for facial expression recognition system is recognizing the expression in uncontrolled environments with minimum computational time consumption. This problem has been addressed by combining the robust local texture descriptors which are invariant to illumination effects. In this work, the illumination effects are eliminated by using Weber Local Descriptor (WLD). Ne...

automatic facial recognition has many potential applications in different areas of humancomputer interaction. however, they are not yet fully realized due to the lack of an effectivefacial feature descriptor. in this paper, we present a new appearance based feature descriptor,the local directional pattern (ldp), to represent facial geometry and analyze its performance inrecognition. an ldp feat...

In this paper we propose two novel rotation invariant local texture descriptors. They are based on Local Binary Pattern (LBP), which is one of the most effective and frequently used texture descriptor. Although LBP efficiently captures the local structure, it is not rotation invariant. In the proposed methods, a dominant direction is evaluated in a circular neighbourhood and the descriptor is c...

Face recognition is becoming very popular tools for a successful human commuter interaction system. It seems to be a good compromise between reliability and social acceptance and balances security and privacy well. In this paper, we have presented a new appearance-based feature descriptor, the local directional pattern Variance (LDPv), to represent facial components and analyzed its performance...

In medical application, the usage of multiple medical images generated by computer tomography such as x-ray, Magnetic Resonance Imaging (MRI) and CT-scan images is a standard tool of medical procedure for physicians. The major problems in analyzing various modality of medical image are the inconsistent orientation and position of the body-parts of interest. In this research, local descriptor of...