International Journal of Pattern Recognition and Artificial Intelligence

Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications.

Each Chinese character is comprised of radicals, where a single character (compound character) contains one (or more than one) radicals. For human cognitive perspective, a Chinese character can be recognized by identifying its radicals and their spatial relationship. This human cognitive law may be followed in computer recognition. However, extracting Chinese character radicals automatically by computer is still an unsolved problem. In this paper, we propose using an improved sparse matrix factorization which integrates affine transformation, namely affine sparse matrix factorization (ASMF), for automatically extracting radicals from Chinese characters. Here the affine transformation is vitally important because it can address the poor-alignment problem of characters that may be caused by internal diversity of radicals and image segmentation. Consequently we develop a radical-based Chinese character recognition model. Because the number of radicals is much less than the number of Chinese characters, the radical-based recognition performs a far smaller category classification than the whole character-based recognition, resulting in a more robust recognition system. The experiments on standard Chinese character datasets show that the proposed method gets higher recognition rates than related Chinese character recognition methods.

Image composition is a very important technique in computer generated imagery. Besides some factors such as contrast, texture and noise that affect the quality of the composition, color harmony between fore- and background is also an important factor that would affect the quality of the composition. However, in the previous image composition techniques, color harmony between fore- and background is seldom considered. In this paper, an optimization method is proposed to deal with the color harmonization problem that used in image composition. A cost function is derived from the local smoothness of the hue values, and the image is harmonized by minimizing the cost function. A new matching cost function is proposed to select the best matching harmonic schemes. Our approach overcomes several shortcomings of the existing color harmonization methods. We validate the performance of our method and demonstrate its effectiveness with a variety of experiments.

There has been a significant improvement in stereo matching with the introduction of adaptive support weights. Existing local methods mainly focus on the computation of support weight which is critical in cost aggregation and usually get excellent results. However, the negative effects of occluded regions are often ignored, which results in the problem of foreground fattening and blurred depth borders. This paper proposes a novel support aggregation strategy by utilizing the occlusion information obtained from left-right consistency check. The weights of invalid points are noticeably reduced at each disparity estimation stage. Experimental results on the Middlebury images show that our method is highly effective in improving the disparities of points around occluded areas and depth discontinuities. According to the Middlebury benchmark, the proposed method achieves the best performance among all the local methods. Moreover, our approach can be easily integrated into nearly all the existing support weights strategies.

Full rank principal component analysis (FR-PCA) is a special form of principal component analysis (PCA) which retains all nonzero components of PCA. Generally speaking, it is hard to estimate how the accuracy of a classifier will change after data are compressed by PCA. However, this paper reveals an interesting fact that the transformation by FR-PCA does not change the accuracy of many well-known classification algorithms. It predicates that people can safely use FR-PCA as a preprocessing tool to compress high-dimensional data without deteriorating the accuracies of these classifiers. The main contribution of the paper is that it theoretically proves that the transformation by FR-PCA does not change accuracies of the k nearest neighbor, the minimum distance, support vector machine, large margin linear projection, and maximum scatter difference classifiers. In addition, through extensive experimental studies conducted on several benchmark face image databases, this paper demonstrates that FR-PCA can greatly promote the efficiencies of above-mentioned five classification algorithms in appearance-based face recognition.

Face recognition, a kind of biometric identification, researched in several fields such as computer vision, image processing, and pattern recognition is a natural and direct biometric method. Face Recognition Technology has diverse potential over applications in the fields of information security, law enforcement and surveillance, smart cards, access control and more. Face recognition is one of the diverse techniques used for identifying an individual. Generally the image variations because of the change in face identity are less than the variations among the images of the same face under different illumination and viewing angle. Illumination and pose are the two major challenges, among the several factors that influence face recognition. After pose and illumination, the main factors that affect the face recognition performance are occlusion and expression. So in order to overcome these issues, we proposed an efficient face recognition system based on partial occlusion and expression. The similar blocks in the face image are identified and occlusion can be recovered using the block matching technique. This is combined with expression normalized by calculating the Empherical Mode Decomposition feature. Finally, the face can be recognized by using the PCA. From the implementation result, it is evident that our proposed method based on the PCA technique recognizes the face images effectively.

This paper proposes the utility of texture and color for iris recognition systems. It contributes for improvement of system accuracy with reduced feature vector size of just 1 × 3 and reduction of false acceptance rate (FAR) and false rejection rate (FRR). It avoids the iris normalization process used traditionally in iris recognition systems. Proposed method is compared with the existing methods. Experimental results indicate that the proposed method using only color achieves 99.9993 accuracy, 0.0160 FAR, and 0.0813 FRR. Computational time efficiency achieved is of 947.7 ms.

A novel approach for feature extraction of fingerprint matching is proposed by using two-dimensional (2D) rotated wavelet filters (RWF). 2D RWF are used to capture the characterization of diagonally oriented information present in fingerprint image. Proposed method extracts the significant information from small area of fingerprint image. Experimental results conducted on standard database of Bologna University and FVC2002 indicate that the proposed method improves the genuine acceptance rate (GAR) from 92.14% to 96.12% and reduces false acceptance rate (FAR) from 25.2% to 21.2% on Bologna University database and it reduces FAR from 36.71% to 22.79% on FVC2002 database compared with discrete wavelet transform-based approach.

The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classifier and Support Vector Machine (SVM) classifier in the classification process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study.

Crew scheduling of the passenger dedicated line is the daily work plan of train attendants, and crew scheduling problem is one of the key problems of the train crew management. Considering the actual condition of China's railway and crew rules, a crew scheduling model is established in this paper to minimize rest time between two consecutive shifts in all roundtrips. In order to solve the scheduling model, in this paper a serial-parallel ant colony optimization algorithm (SP-ACO) is provided. The SP-ACO proposed two improvements: (1) a serial-parallel ant grouping strategy, it means that we let every group of ants find the solution one group by one group for reflecting the positive feedback in a generation; (2) the transition method based on the roulette wheel selection, it means that we randomly select between the certain search and the roulette wheel to make the diversity of solutions for avoiding all ants toward the same part of the search space. Finally, two application cases of the scheduling model are represented. The two cases results show that the methods can not only automatically set roundtrips, but also decrease the number of roundtrips, and the results also show that the feasibility of model and the validity of improved ant colony algorithm for crew scheduling.