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In this paper, we have implemented and tested a system of detection and recognition of road signs. The approach taken in this work consists of two main modules: a sensor module, which is based on color segmentation and shape detection where we converted the images to the HSV color space, then labeled the detected regions and tested for their shape. A recognition module, Template Matching, whose role...
Compared to the traditional Gabor transform, the circularly symmetrical Gabor transform (CSGT) not only retains the characteristics of local and multi-resolution analysis, but also has the remarkable advantages of less redundancy and rotational invariance. Simultaneously, the collaborative representation-based classification with regularized least square (CRC-RLS) overcomes the shortcoming of the...
Face recognition (FR) is an interesting topic in recent pattern recognition investigation. Especially, the accuracy of FR is the foremost concern for practical applications. Linear regression classification (LRC) is one of the most famous and effective methods in the FR area. However, it could perform inaccuracy under variant situations such as few training samples, lighting changes, and partial occlusions...
The paper proposes a novel face recognition technique based on discrete Contourlet Transform (CT). CT represents smooth contour information in different directions and so relevant to recognize individuals more accurately. Each face image is decomposed up to fourth level in transformed domain using Contourlet Transform and coefficients are analyzed to obtain statistical features. For classification...
Significant progress towards visual search has been made in the past two decades through the development of local invariant features. Among existing local feature detectors, the Scale Invariant Feature Transform (SIFT) is widely used since it is designed to be invariant to minimal illumination changes and certain geometric transformations. However, in practice, the recognition performance is still...
Over the past decade, a considerable amount of literature has been published on face recognition. Since recognition of frontal face images under controlled settings has become easy to achieve, a number of recent studies have emphasized the importance of robustness to variations in pose and illumination. So in this paper, we undertake the task of recognizing face images taken under drastic lighting...
In this paper we introduce a novel face representation method called Cross Local Binary Patterns(XLBP) to improve the robustness of face recognition for partially occluded and non-uniformly illuminated face images. In our method we use Radon transform to capture the coarse level shape information and XLBP to capture the texture information. Individual histograms computed on each sub-block of the face...
Sparse representation based classification (SRC) as an efficient method has high recognition rate in many pattern recognition applications. Unfortunately, the original SRC method generally requires rigid alignment in classification. In this paper, the feature-based SRC method is addressed by using the PCA-SIFT and SPP-SIFT descriptors, respectively. The presented methods are not only efficient for...
In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence...
Gabor features have been demonstrated to be very effective for face representation. Recently, non-sub sampled contour let transform (NSCT), which is a newly developed multi-resolution analysis tool based on contour let transform, is also used in facial image processing. In fact, the two image decomposition methods are performed from two different angles. To exploit complementarity of these features,...
This paper presents a new method to automatically locate pupils in images (even with low-resolution) containing human faces. In particular pupils are localized by a two steps procedure: at first self-similarity information is extracted by considering the appearance variability of local regions and then they are combined with an estimator of circular shapes based on a modified version of the Circular...
A new shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed in this paper for invariant face recognition while accommodating expression and illumination variations. The enhanced local binary pattern operator is utilized in the preprocessing stage for facial feature extraction. A phase-shifted and phase-encoded fringe-adjusted joint transform correlator (FJTC) is implemented...
A novel algorithm based on the hybrid of contourlet and manifold learning is proposed for face recognition. In this study, the features of the low frequency and directional subbands in contoulet domain are first extracted, with the low frequency components sensitive to illumination variations ignored to effectively alleviate the effect of illuminations. Then the dimensionality of features is reduced...
Face pose estimation plays a vital role in human-computer interaction, automatic human behavior analysis, pose-independent face recognition, gaze estimation, virtual reality applications etc. A novel face pose estimation method using distance transform and normalized cross-correlation, is presented in this paper. The use of distance transform has two main advantages: first, unlike intensity image,...
This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS)....
We present a method for matching image local features, specifically SIFT features, to a database of learned object features for the purpose of object recognition and localisation. Our approach differs from existing methods by taking into account the geometric consistency of matched features concurrently with their description vector similarity. As a result we do not need to over-constrain the description...
Vehicle type (make and model) recognition provides high level of security to the systems that are solely based on automatic license plate detection and recognition. Most of the work in this direction has been done in controlled conditions. In this paper we evaluate in an extensive experimental setting, the strength and weakness of various global and local feature based methods on vehicle images captured...
In this paper, we propose a novel illumination normalization method for face recognition under varying lighting conditions. In the proposed method, the logarithm transform is firstly applied to facial image under various lighting conditions, which transfers illumination model from multiplicative model to additive one. Then the adaptive normal shrink filter based on the nonsubsampled contourlet transform...
In this paper, an efficient local appearance feature extraction method based steerable pyramid (S-P) is proposed for face recognition. Local information is extracted from S-P sub-bands using block-based statistics. The underlying statistics allow us to reduce the required amount of data to be stored. The obtained local features are combined at the feature and decision level to enhance face recognition...
We present an automatic coin classifier mainly depending on visual features. Our multistage system starts out by segmentation using circular Hough transform, features extraction by two complementary cues and finally classification by simple nearest neighbor measure. Our features extraction process relies on rotation invariant edge orientation followed by Gabor wavelet convolution. Testing on the publicly...
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