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In this paper, an orientation and scale invariant binary descriptor is proposed, which can be used in key-points matching systems. Conventionally, a binary descriptor is generated by comparing the intensities of pixels directly, such as those in Binary Robust Independent Elementary Features (BRIEF) and Oriented FAST and Rotated BRIEF (ORB). However, comparing intensities of pixels may lose the texture...
Many research works have been done in face recognition during the last years that indicates the importance of face recognition systems in many applications including identity authentication. In this paper we propose an approach for face recognition which is suitable for unconstrained image acquisition and has a low computational cost. Since in practical applications such as in smartphones, imaging...
Speeded Up Robust Features (SURF) is one of the most robust and widely used image matching algorithms based on local features. However, the performance for rotation image is poor when one image is a rotated version of the other. To improve the matching accuracy of rotation image, we present an modified image matching algorithm combining Haar wavelet and the rotation invariant Local Binary Patterns...
Recognizing faces in presence of illuminations, pose, facial expression variations in controlled as well as uncontrolled environments remains one of the most challenging aspect. In this paper, we propose a novel recognition methodology which deals with challenges of face recognition to obtain robust and efficient recognition. The framework is based on extracting discriminant statistical features from...
This manuscript present a gender classification technique uses the (2D)2PCA, Gabor filter, and SVM for classifying the gender from occluded and non occluded face images. Present work explores two approaches, i.e., Fusion at feature level, and fusion at classifier level. Experimental result shows that, both the proposed approaches give an acceptable result on non-occluded face image database. For occlude...
Appearance-based person re-identification namely matching the same pedestrian across disjoint camera views, is an increasingly research spot. Researchers have proposed many complicated appearance descriptions. The role of basic visual features is seldom investigated, e.g. color, texture features, which are the basis of descriptions. By analyzing the characteristics of data environments, we can get...
This paper presents a method for extracting a new feature descriptor, named Partial Dominant Orientation Descriptor (PDOD), which can be used to perform reliable matching between objects with similar as well as different textures. The object is represented by a set of key locations of stable points using Difference of Gaussian, so that the matching can proceed successfully despite changes in viewpoint,...
Local ternary pattern (LTP) is a noise-robust version of local binary pattern (LBP). They are both encoding for the differences between the intensity of the center pixel and its neighborhoods. In this paper, based on Webers law we propose two new local descriptors, named Weber binary pattern (WBP) and Weber ternary pattern (WTP), which utilize binary and ternary encoding separately for the evaluation...
Since its emergence, Optical communication systems have always been considered as a significantly attractive alternative to wired data transfer techniques, especially in environment where high data rate is essential in a multi-component system. Unfortunately, optical communication is still not as robust as traditional mechanisms and suffers from various types of distortion, such as jitter in data...
Among many texture descriptors, the LBP-based representation emerged as an attractive approach thanks to its low complexity and effectiveness. Many variants have been proposed to deal with several limitations of the basic approach like the small spatial support or the noise sensitivity. This paper presents a new method to construct an effective texture descriptor addressing those limitations by combining...
In this paper, we study some feature descriptors and detectors based on invariants type and use the most stable and robust keypoints for an efficient object detection. From many points of view local descriptors are relatively different, the way of their extraction and the type of the invariance, the goal of this paper is to examine existing feature detectors and apply the most appropriate and efficient...
in this paper, we address the problem of tracking drift and failure due to background clustering, illumination and scale changes. To resolve these problems, we propose an efficient model that project original RGB color space to a more robust color space—Color Names feature space. Furthermore, we represent objects by background weighted histograms, and thus suppress the similar background around. Moreover,...
Questionnaires are widely used for investigation and statistical analysis. However, paper-based questionnaires require great human resources to count the statistical results or enter data into database, which are time consuming. A system capable of recognizing results of questionnaires will be very useful in many aspects. In this paper, we develop a fast and robust digital recognition system for questionnaire...
Recently, nuclear norm based matrix regression (NMR) for classification has been proposed to characterize the whole structure of the error image. However, NMR ignores both the label information and the group structure of training samples. This paper presents a novel yet effective coding scheme called locality-constrained group sparse coding regularized NMR (LGNMR) which not only overcomes these limitations...
In the census transform, hamming weight calculation is an important process because the hamming weight is the criterion for finding the similarity between stereo images. If the intensity value of a center pixel is changed by different illumination of two cameras, erroneous hamming weight calculation can happen. To compensate for such vulnerability to different illumination, this paper suggests a modified...
Detecting an illegally parked vehicle in urban scenes of traffic monitoring system becomes more complex task due to occlusions, lighting changes, and other factors. In this paper, a new framework to detect illegally parked vehicle using dual background model subtraction is presented. In our system, the adaptive background model is generated based on statistical information of pixel intensity that...
Obstacle detection is a key technology of intelligent transportation and autonomous robot navigation. Aiming at the shortages of traditional obstacle detection technologies, the paper applies the Kinect depth camera as the sensor of obstacle detection system, and an obstacle detection method based on Kinect depth image is proposed on the theoretical basis of Kinect real-time 3D reconstruction and...
This paper presents a new method for 3D face pose tracking in arbitrary illumination change conditions using color image and depth data acquired by RGB-D cameras (e.g., Microsoft Kinect, Asus Xtion Pro Live, etc.). The method is based on an optimization process of an objective function combining photometric and geometric energy. The geometric energy is computed from depth data while the photometric...
This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results...
The proposed system comes in the context of intelligent parking lots management and presents an approach for vacant parking spots detection and localization. Our system provides a camera-based solution, which can deal with outdoor parking lots. It returns the real time states of the parking lots providing the number of available vacant places and its specific positions in order to guide the drivers...
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