Blur Invariants for Image Recognition

Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any deblurring. In this paper, we present an original unified theory of blur invariants. Unlike all previous attempts, the new theory does not require any prior knowledge of the blur type. The invariants are constructed in the Fourier domain by means of orthogonal projection operators and moment expansion is used for efficient and stable computation. It is shown that all blur invariants published earlier are just particular cases of this approach. Experimental comparison to concurrent approaches shows the advantages of the proposed theory.Comment: 15 page

Color Image Analysis by Quaternion-Type Moments

International audienceIn this paper, by using the quaternion algebra, the conventional complex-type moments (CTMs) for gray-scale images are generalized to color images as quaternion-type moments (QTMs) in a holistic manner. We first provide a general formula of QTMs from which we derive a set of quaternion-valued QTM invariants (QTMIs) to image rotation, scale and translation transformations by eliminating the influence of transformation parameters. An efficient computation algorithm is also proposed so as to reduce computational complexity. The performance of the proposed QTMs and QTMIs are evaluated considering several application frameworks ranging from color image reconstruction, face recognition to image registration. We show they achieve better performance than CTMs and CTM invariants (CTMIs). We also discuss the choice of the unit pure quaternion influence with the help of experiments. appears to be an optimal choice

Design and Development of Robotic Part Assembly System under Vision Guidance

Robots are widely used for part assembly across manufacturing industries to attain high productivity through automation. The automated mechanical part assembly system contributes a major share in production process. An appropriate vision guided robotic assembly system further minimizes the lead time and improve quality of the end product by suitable object detection methods and robot control strategies. An approach is made for the development of robotic part assembly system with the aid of industrial vision system. This approach is accomplished mainly in three phases. The first phase of research is mainly focused on feature extraction and object detection techniques. A hybrid edge detection method is developed by combining both fuzzy inference rule and wavelet transformation. The performance of this edge detector is quantitatively analysed and compared with widely used edge detectors like Canny, Sobel, Prewitt, mathematical morphology based, Robert, Laplacian of Gaussian and wavelet transformation based. A comparative study is performed for choosing a suitable corner detection method. The corner detection technique used in the study are curvature scale space, Wang-Brady and Harris method. The successful implementation of vision guided robotic system is dependent on the system configuration like eye-in-hand or eye-to-hand. In this configuration, there may be a case that the captured images of the parts is corrupted by geometric transformation such as scaling, rotation, translation and blurring due to camera or robot motion. Considering such issue, an image reconstruction method is proposed by using orthogonal Zernike moment invariants. The suggested method uses a selection process of moment order to reconstruct the affected image. This enables the object detection method efficient. In the second phase, the proposed system is developed by integrating the vision system and robot system. The proposed feature extraction and object detection methods are tested and found efficient for the purpose. In the third stage, robot navigation based on visual feedback are proposed. In the control scheme, general moment invariants, Legendre moment and Zernike moment invariants are used. The selection of best combination of visual features are performed by measuring the hamming distance between all possible combinations of visual features. This results in finding the best combination that makes the image based visual servoing control efficient. An indirect method is employed in determining the moment invariants for Legendre moment and Zernike moment. These moments are used as they are robust to noise. The control laws, based on these three global feature of image, perform efficiently to navigate the robot in the desire environment

Feature Extraction Methods for Character Recognition

Not Include

Distortion Robust Biometric Recognition

abstract: Information forensics and security have come a long way in just a few years thanks to the recent advances in biometric recognition. The main challenge remains a proper design of a biometric modality that can be resilient to unconstrained conditions, such as quality distortions. This work presents a solution to face and ear recognition under unconstrained visual variations, with a main focus on recognition in the presence of blur, occlusion and additive noise distortions. First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features. In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks. The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Face Recognition with Degraded Images

After more than two decades of research on the topic, automatic face recognition is finding its applications in our daily life; banks, governments, airports and many other institutions and organizations are showing interest in employing such systems for security purposes. However, there are so many unanswered questions remaining and challenges not yet been tackled. Despite its common occurrence in images, blur is one of the topics that has not been studied until recently. There are generally two types of approached for dealing with blur in images: (1) identifying the blur system in order to restore the image, (2) extracting features that are blur invariant. The first category requires extra computation that makes it expensive for large scale pattern recognition applications. The second category, however, does not suffer from this drawback. This class of features were proposed for the first time in 1995, and has attracted more attention in the last few years. The proposed invariants are mostly developed in the spatial domain and the Fourier domain. The spatial domain blur invariants are developed based on moments, while those in the Fourier domain are defined based on the phase\u27 properties. In this dissertation, wavelet domain blur invariants are proposed for the first time, and their performance is evaluated in different experiments. It is also shown that the spatial domain blur invariants are a special case of the proposed invariants. The second contribution of this dissertation is blur invariant descriptors that are developed based on an alternative definition for ordinary moments that is proposed in this dissertation for the first time. These descriptors are used forface recognition with blurred images, where excellent results are achieved. Also, in a comparison with the state-of-art, the superiority of the proposed technique is demonstrated

Face Recognition: A Comparative Approach from Traditional to Recent Trends

Face recognition, an important biometric method used extensively by researchers, has become more popular recently due to development of mobile applications and frequent usages of facial images in social media. A major development is attained in facial recognition methods due to the emergence of deep learning methods. As a result, the performance of face recognition systems reached a matured state. The objectives of this research are to improve the accuracy rate of both traditional and modern methods of face recognition system under illumination variation by applying various preprocessing techniques. In the proposed face recognition approach, various preprocessing methods like SQI, HE, LTISN, GIC and DoG are applied to the Local Binary Pattern (LBP) feature extraction method and by using the Weighted Entropy based method to fuse the output of classifiers on FERET database, we have shown improvement in recognition accuracy of as high as 88.2 % can be obtained after applying DoG . In a recently used approach, deep CNN model is suggested. The Experiments are conducted in Extended Yale B and FERET Database. The suggested model provides good accuracy rates. To improve the accuracy rates further, preprocessing methods like SQI, HE, LTISN, GIC and DoG are applied to both the models. As a result, higher accuracy rates are achieved in deep CNN model both in Extended Yale B Database and FERET Database. Extended Yale B Database provides the highest accuracy rate of 99.8% after the application of SQI and an accuracy rate of 99.7% is achieved by applying HE

2D partially occluded object recognition using curve moment invariants

Master'sMASTER OF ENGINEERIN