3D Face Recognition using Significant Point based SULD Descriptor

In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matching between different poses of range images of faces. For a given 3D face scan, range images are computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point we compute the SULD descriptor which consists of vector made of values from the convolved Haar wavelet responses located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experimental results show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition

Pengenalan Gambar Menggunakan Sebagian Data Gambar

Pengenalan gambar dengan menggunakan sebagian data gambar query, sebagian data gambar query ini bisa terjadi karena bentuk gambar query yang tidak utuh atau tidak sesempurna gambar asli. Gambar asli ini adalah gambar yang ada didalam database Gambar query yang tidak utuh mungkin karena objek lain yang menutupi, atau pengambilan gambar yang tidak sempurna, atau keadaan objek itu sendiri yang mengalami Perubahan. Untuk melakukan pengenalan gambar dengan kondisi seperti tersebut diatas digunakan metode ekstraksi fitur SURF

Scale Invariant Interest Points with Shearlets

Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities such as edges and corners at multiple scales. In this work we address the problem of detecting and describing blob-like features in the shearlets framework. We derive a measure which is very effective for blob detection and closely related to the Laplacian of Gaussian. We demonstrate the measure satisfies the perfect scale invariance property in the continuous case. In the discrete setting, we derive algorithms for blob detection and keypoint description. Finally, we provide qualitative justifications of our findings as well as a quantitative evaluation on benchmark data. We also report an experimental evidence that our method is very suitable to deal with compressed and noisy images, thanks to the sparsity property of shearlets

Class-Based Feature Matching Across Unrestricted Transformations

We develop a novel method for class-based feature matching across large changes in viewing conditions. The method is based on the property that when objects share a similar part, the similarity is preserved across viewing conditions. Given a feature and a training set of object images, we first identify the subset of objects that share this feature. The transformation of the feature's appearance across viewing conditions is determined mainly by properties of the feature, rather than of the object in which it is embedded. Therefore, the transformed feature will be shared by approximately the same set of objects. Based on this consistency requirement, corresponding features can be reliably identified from a set of candidate matches. Unlike previous approaches, the proposed scheme compares feature appearances only in similar viewing conditions, rather than across different viewing conditions. As a result, the scheme is not restricted to locally planar objects or affine transformations. The approach also does not require examples of correct matches. We show that by using the proposed method, a dense set of accurate correspondences can be obtained. Experimental comparisons demonstrate that matching accuracy is significantly improved over previous schemes. Finally, we show that the scheme can be successfully used for invariant object recognition

Correlation Method Based PCA Subspace using Accelerated Binary Particle Swarm Optimization for Enhanced Face Recognition

The capacity to perceive human countenances is an exhibit of unfathomable human insight. Clinicians inferred that comprehensive and highlight based methodologies are double courses to the face acknowledgment [1]. Most early methodologies in face acknowledgment extricate nearby highlights from face pictures. Be that as it may, the kind of nearby highlights which are most steady and discriminative for face acknowledgment is obscure. Because of challenges in heartily separating nearby highlights from face pictures, analysts started to utilize the entire face area as the crude info to an acknowledgment framework, and created all-encompassing coordinating strategies. There are a large number of productions in face acknowledgment utilizing all-encompassing methodologies. Furthermore, for the most part this kind of methodologies can attain to preferred execution over highlight based methodologies [2], [3]. Notwithstanding, the execution of comprehensive coordinating techniques will drop when there are varieties because of outflows or postures. Also, neighbourhood highlights extricated from nearby districts of a face picture are stronger to these varieties than the worldwide highlights. This inspires us to re-ponder the highlight based methodologies

Targeting a Practical Approach for Robot Vision with Ensembles of Visual Features

We approach the task of topological localization in mobile robotics without using a temporal continuity of the sequences of images. The provided information about the environment is contained in images taken with a perspective colour camera mounted on a robot platform. The main contributions of this work are quantifiable examinations of a wide variety of different global and local invariant features, and different distance measures. We focus on finding the optimal set of features and a deepened analysis was carried out. The characteristics of different features were analysed using widely known dissimilarity measures and graphical views of the overall performances. The quality of the acquired configurations is also tested in the localization stage by means of location recognition in the Robot Vision task, by participating at the ImageCLEF International Evaluation Campaign. The long term goal of this project is to develop integrated, stand alone capabilities for real-time topological localization in varying illumination conditions and over longer routes

Automatic Seamless of Image Stitching

The objective of this paper is to implement image stitching by adopting feature-based alignment algorithm and blending algorithm to produce a high quality image, the images for stitching to create panorama are captured in a fixed linear spatial interval. The processing method involves feature extraction, image matching based on Harris corner detectors method as the feature detection and neighboring pairs alignment using RANSAC (RANdom Sample Consensus) algorithm. Linear blending is applied to remove the transition between the aligned images. The presented image stitching algorithm is successfully able to create panorama image. Keywords: Image stitching, Harris detectors, RANSAC algorithm, Linear blending