This paper advocates a novel material-aware feature descriptor for volumetric image registration. We rigorously formulate a novel probability density function (PDF) based distance metric to devise a compact local feature descriptor supporting invariance of full 3D orientation and isometric deformation. The central idea is to employ anisotropic heat diffusion to characterize the detected local v...

The paper presents a novel, theoretically well-based methodology to find changes in remote sensing image series. The proposed method finds changes in images scanned with a long time-interval difference in very different lighting and surface conditions. The presented method is basically an exploitation of the Harris saliency function and its derivatives for finding featuring points among image s...

The local descriptors have been the backbone of most of the computer vision problems. Most of the existing local descriptors are generated over the raw input images. In order to increase the discriminative power of the local descriptors, some researchers converted the raw image into multiple images with the help some high and low pass frequency filters, then the local descriptors are computed o...

A very simple but efficient feature descriptor is proposed for image matching/registration applications where invariance is not important. The descriptor length is only three times the height of the local region in which the descriptor is calculated, and experiments were conducted to compare it to the SURF descriptor. In addition, it is shown, how the sampling can be modified in order to obtain...

The paper presents an efficient and reliable feature descriptor for human detection in a top-view depth image that uses two statistical values of mean and standard deviation. Human detection performance of our descriptor outperform Rauter that use mean value and Simplified Local Ternary Pattern (SLTP). To evaluate the human detection performance using our descriptor, we capture 559 positive and...

Face Recognition with Learning-based Descriptor. 1The Chinese University of Hong Kong. Xiaoou Tang1, 3.We pdf converter for linux free download examine the building blocks of descriptor algorithms and evaluate numerous. Mations and can be compared with other descriptors in a database to obtain.Several versions of the revised Descriptors were consulted on widely with stakeholders and users of th...

This paper presents several novel Gabor-based color descriptors for object and scene image classification. Firstly, a new Gabor-HOG descriptor is proposed for image feature extraction. Secondly, the Gabor-LBP descriptor derived by concatenating the Local Binary Patterns (LBP) histograms of all the component images produced by applying Gabor filters is integrated with the Gabor-HOG using an opti...

This paper presents a local feature descriptor, the Local Distinctive Star Pattern (LDSP), for facial expression recognition. The feature is obtained from a local 3x3 pixels area by computing the directional edge response value. Each pixel is represented by two 4-bit binary patterns, which is named as LDSP feature for that pixel. Each face is divided into 81 equal sized blocks and histogram of ...

Image local scale invariant features are of great importance for object recognition. Among various local scale invariant feature descriptors, Scale Invariant Feature Transform (SIFT) descriptor has been shown to be the most descriptive one and thus widely applied to image retrieval, object recognition and computer vision. By SIFT descriptor, an image may be described by hundreds of key points w...

Manifold learning based image clustering models are usually employed at local level to deal with images sampled from nonlinear manifold. Multimode patterns in image data matrices can vary from nominal to significant due to images with different expressions, pose, illumination, or occlusion variations. We show that manifold learning based image clustering models are unable to achieve well separa...