This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed b...
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One of the major goals of computervision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse rep...
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作者：
Jinn-Li TanS. A. R Abu-BakarComputer Vision
Video and Image Processing Department of Microelectronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia
This paper presents a license plate character segmentation method in the context of Malaysian cars. First of all, pre-processing steps will enhance the image before Laplacian pyramid takes place. For a proper binariza...
This paper presents a license plate character segmentation method in the context of Malaysian cars. First of all, pre-processing steps will enhance the image before Laplacian pyramid takes place. For a proper binarization, Laplacian pyramid which up-sampled the image from an image lower in the pyramid when the image is captured under low resolution. By using Sobel edge detector and then median filtering, circumscribe rectangle of minimum area is formed and the angle is calculated. At the same time, the area of characters is focussed. The characters are then selected based on connected component analysis after applying Niblack's threshold. Our goal is to segment the characters properly from the steps mentioned. Therefore, our algorithm tries to find the best point to segment the characters using little prior knowledge. Experimental shows promising results on the flexibility of the proposed design method.
Cellular Simultaneous Recurrent Network (CSRN) is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. CSRN has been proven to be a powerful tool for learning and predicting t...
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ISBN:
(纸本)9781467314886
Cellular Simultaneous Recurrent Network (CSRN) is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. CSRN has been proven to be a powerful tool for learning and predicting temporal information in face image sequences. In this work, we propose a novel implementation of feature-based CSRN for large-scale pose invariant face recognition. We also report systematic evaluation and performance comparison of our feature-based CSRN method with other well-known standard algorithms (PCA, LDA, Bayesian Classifier and EBGM) using face recognition technology standards for large-scale pose invariant face recognition.
作者：
J. K. ChawM. M. MokjiComputer Vision
Video and Image Processing (CvviP) Laboratory Department of Microelectronic and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia
Produce recognition system is a system that can categorize types of vegetables and fruits based on features extracted from the images. However, there are numerous features that can be extracted from fruits and vegetab...
Produce recognition system is a system that can categorize types of vegetables and fruits based on features extracted from the images. However, there are numerous features that can be extracted from fruits and vegetables such as colour, texture and shape. As a result, it is effort consuming to identify suitable features ad hoc. Thus, data mining is required to discover the most discriminative features for recognition. This paper aims to extend the usage of data mining algorithm to image domain. Data mining algorithm is preferred to other feature selection algorithms because it discovers nuggets of knowledge that can be understood by human whereas classic feature selection techniques provide outputs that can only be managed by learning algorithms.
Requirement for a person to face a camera for face identification process may no longer be necessary if the face recognition system is robust against variation of facial pose. In this paper, we proposed a face recogni...
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Requirement for a person to face a camera for face identification process may no longer be necessary if the face recognition system is robust against variation of facial pose. In this paper, we proposed a face recognition method which remains reliable even in very large head pose variations. In this method, feature from local regions of face are extracted after employing both discrete cosine transform and discrete wavelet transform. Learning strategy is then applied to infer the relationship between face in a given pose and its frontal view. Results we obtained are very promising considering that our proposed method solely relies on a single gallery image. We also demonstrated the high performance of our method in a condition whereby the face images are of low-resolution quality.
This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed b...
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This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed before in the literature. We show that this is possible, both theoretically and practically, taking advantage of some local shading measures that vary nonlinearly as a function of luminance and geometric nonlinearities (e.g., gamma correction and lens distortion). This can work as a basis for developing a simple method to estimate these nonlinearities from a single image. Several experiments are reported to validate the proposed method.
Polarization imaging can give information about surface shape, and roughness. Polarization has been used for shape recovery, but with convex/concave reconstruction ambiguity. In this paper, we present a direct method ...
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Polarization imaging can give information about surface shape, and roughness. Polarization has been used for shape recovery, but with convex/concave reconstruction ambiguity. In this paper, we present a direct method to shape recovery using both polarization and shading that resolves this ambiguity, without the need for nonlinear optimization routines. Several experiments on synthetic and real datasets are reported to evaluate the proposed method. The method consistently outperforms some well-known methods based on polarization information alone.
One of the major goals of computervision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse rep...
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One of the major goals of computervision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Laplace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem.
Bone cancer is a pathologic condition which may occur for both humans and canines. This tumor develops quickly from within the bone tissue and become painful as it grows outward. A metastasized bone tumor may be cured...
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Bone cancer is a pathologic condition which may occur for both humans and canines. This tumor develops quickly from within the bone tissue and become painful as it grows outward. A metastasized bone tumor may be cured by amputation, otherwise it will be fatal. The current diagnostic imaging methods for bone cancer are X-rays, Computed Tomography scan (CT scan), and Magnetic Resonance Imaging (MRI). The disadvantages of these methods include not enough detail in X-ray images, high dose of radiation from CT scans, and high expense of the time-consuming MRI method. In most of the bone cancer cases, when this tumor is detected by these imaging methods, it has already metastasized. The study is to investigate whether it is possible to detect canine bone cancer by thermography imaging. This alternative imaging method will decrease diagnostic time, expenses and prevent radiation exposure. The best classification success rate obtained in this study is 80.77%.
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