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An Augmented Reality (AR) composite layup tool was created using lo cost, off the shelf co ponents and software to prove and demonstrate the application of AR in a manufacturing environment. The project tested different tracking technologies in order to ascertain their practicality within an industrial environment. By developing an understanding of the challenges faced in implementing such an application,...
Preprocessing and fusion techniques for finger vein recognition are investigated. An experimental study involving a set of preprocessing approaches shows the importance of selecting the appropriate single technique and the usefulness of cascading several different preprocessing methods for subsequent feature extraction of various types. Score level fusion is able to significantly improve recognition...
Face recognition has achieved immense popularity in various fields because of its robustness and accuracy. But pose variation is still a major obstacle to overcome for effective face recognition in an uncontrolled environment. A wide variety of face recognition algorithms have been proposed in the past. In this paper we exhibit a review of some of the common algorithms that expect to conquer on the...
The paper presents a pornographic image recognition using fusion of scale invariant descriptor. The pornographic image means the image contains and shows genital elements of human body having large variability due to poses, lighting, and backgrounds variations. The fusion of scale invariant descriptor that is holistic feature is employed to handle those variability problems. This holistic feature...
The performance of face recognition is easily affected by appearance variation by face rotation. The proposed method in this research recognizes who is a subject in the query image in which a face is captured from an arbitrary direction. The proposed method employs an auto-associative neural network for learning a manifold which represents principal variation of facial appearance in feature space...
This paper presents a multispectral palmprint recognition approach based on palm line orientation feature extracted with high order steerable filter. Gaussian function is used as isotropic window to design a high order steerable filter. The orientation features are selected as per dominant filter response for a particular orientation. Optimum values for parameters, i.e., standard deviation and number...
In the last decade we have witnessed a huge increase of interest in data stream learning algorithms. A stream is an ordered sequence of data records. It is characterized by properties such as the potentially infinite and rapid flow of instances. However, a property that is common to various application domains and is frequently disregarded is the very high fluctuating data rates. In domains with fluctuating...
Researchers in sign language recognition customized different sensors to capture hand signs. Gloves, digital cameras, depth cameras and Kinect were used alternatively in most systems. Due to signs closeness, input accuracy is a very essential constraint to reach a high recognition accuracy. Although previous systems accomplished high recognition accuracy, they suffer from stability in realistic environment...
A method is presented for authenticating people on the basis of lip movement. It uses the kernel mutual subspace (KMS) method using fusion of canonical angles by kernel Fisher discriminant analysis. Its authentication accuracy is better than that of previously proposed lip-movement authentication methods when the distribution of lip images has a nonlinear structure. The similarity of KMS is canonical...
This paper presents an improved vision-based algorithm for detecting and recognizing vehicle logos in images captured by road surveillance cameras. Vehicle logo recognition is quite a challenging task considering the low resolution of the logos, the wide range of variability in illumination and the interference of the air-intake grille. However, our system, assessed on a set of 1386 vehicle images...
This research is to propose a fast and highly accurate object recognition method especially for fruit recognition applications to be used in a mobile environment. Conventional techniques are based on one or more of the basic features that characterize an object: color, shape, texture and intensity, causing performance or accuracy limitations in a mobile environment. Thus, this paper presents a combined...
Optical Character Recognition (OCR) deals with automated recognition of characters that are in the format of digital image. OCR refers to the process by which scanned images are electronically processed and converted to an editable document. Handwritten and printed texts are the primary research areas of an OCR. Many OCR systems are commercially available for English and Arabic characters but there...
In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms...
CAPTCHAs exploit the gap in the ability between a human and a machine to understand the semantics of specific multimedia content, with vast applications in computer security. In this paper we compare two techniques in automated CAPTCHA solving for text-based CAPTCHA schemes, i.e., Classification based on the Vector Space Model (VSM) versus a popular Optical Character Recognition (OCR) engine. For...
In the present work, a holistic word recognition technique is proposed for the recognition of the handwritten Bangla words. Holistic word recognition technique assumes a word as a single and indivisible entity and extracts features from the entire word to recognize it. In this work, a set of elliptical features is extracted from handwritten word images to represent them in the feature space. Then,...
This paper mainly proposes a deep learning method-Stacked Denoising Auto Encoder (SDAE) to solve the problems of automatic feature extraction and dimension reduction in Braille recognition. In the construction of a network with deep architecture, a feature extractor was trained with unsupervised greedy layer-wise training algorithm to initialize the weights for extracting features from Braille images,...
Human face based gender recognition is a challenging issue in image processing and machine vision domain. In this paper we proposed an approach for gender recognition using combination of statistical features and Local Binary Pattern (LBP). The optimal block size and statistical features set are determined by sequential forward floating selection (SFFS) algorithm for gender recognition improvement...
Scene text recognition has attracted much attention in the research community. Many proposed scene text recognition methods adopt a step-by-step procedure, which includes a text extraction phase and a recognition phase. In this study, in order to eliminate the risk of text extraction error, we try to build a scene text recognition system that does not involve the text extraction phase. In our proposed...
Image compression had been extensively studied for reducing coding rate yet producing acceptable visual quality. However, there are many application scenarios where the compressed images are used for automatic recognition rather than human viewing, thus the visual quality is no longer critical for compression. SIFT features have demonstrated their utility in many recognition scenarios and SIFT-preserving...
This paper introduces a novel dynamic neural network model which can recognize dynamic visual image patterns of human actions based on learning. The proposed model is characterized by its capability of extracting the spatio-temporal feature hierarchy latent in the training visual image streams. The model achieves this property by integrating two essential ideas: (1) multiple spatial-scales processing...
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