Image enhancement is an essential step in developing a robust computer vision system. This study presents an image contrast enhancement method for an online fish behaviour monitoring system. The method executes in three steps. First, the critical information on the context of the image is derived from different responses of two edge detectors: one with high threshold and the other with low threshold, which differentiate the image structure into the good contrast part and the poor contrast part. Then, statistics of the gradient field in the former part are used to modify the gradient field in the latter part. Finally, in the modified region, the image contents are reconstructed through solving a Poisson equation with the Dirichlet boundary condition. Comparison with state‐of‐the‐art methods has been made both qualitatively and quantitatively using realistic images and advantages of the proposed method have been demonstrated.

When working with the line-based wavelet transform, JPEG2000 needs to buffer many sub-band lines for future block coding. This coding buffer occupies the majority of the memory usage in a JPEG2000 system. In this paper, we first use the multilevel block-based wavelet transform, and then utilize the multilevel stripe-based wavelet transform to realize JPEG2000 coding. The system schemes of the multilevel block-based wavelet transform and the multilevel stripe-based wavelet transform for JPEG2000 are presented. The proposed schemes can effectively control the wavelet coefficient output pattern, and thus reduce the memory usage of JPEG2000 coding buffer. Compared with the line-based wavelet transform, the proposed schemes reduce more than 50% memory usage of the JPEG2000 system and slightly decrease the memory bandwidth. By holding the blocks/stripes in on-chip memory, which do not depend on the image size, the memory bandwidth of the JPEG2000 system can be further significantly reduced. The advantage of the stripe-based wavelet transform over the block-based wavelet transform lies in that it occupies much less internal memory.

This paper presents a new keypoint-based approach for Near-Duplicate (ND) keyframes identification using global and local features. In the proposed method, the matched keypoints are filtered for affine transform estimation based on an affine invariant feature which is using the lesser matching pairs and voting bins. To further confirm the matching, the Local Binary Pattern (LBP) and color features of areas formed by matched keypoints in two images are compared. This method combines the advantage of global and local matching, it is able to identify two images related with affine transform. The proposed algorithm has been tested in Columbia dataset and conducted the quantitative comparison with RANSAC algorithm and SR-PE algorithm. The experiment results show that the proposed method can achieve good results.

This paper presents a new keypoint-based approach to near-duplicate images detection. It consists of three steps. Firstly, the keypoints of images are extracted and then matched. Secondly, the matched keypoints are voted for estimation of affine transform based on an affine invariant ratio of normalized lengths. Finally, to further confirm the matching, the color histograms of areas formed by matched keypoints in two images are compared. This method has the advantage for handling the case when there are only a few matched keypoints. The proposed algorithm has been tested on Columbia dataset and conducted the quantitative comparison with RANdom SAmple Consensus (RANSAC) algorithm and Scale-Rotation Invariant Pattern Entropy (SR-PE) algorithm. The experiment result turns out that the proposed method compares favorably against the state-of-the-arts.

Abstract Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract A critical issue in the development of an automated fingerprint identification system (AFIS) is to detect the reference point accurately and reliably. Most existing techniques are based on the singular points of the fingerprint which is sensitive to fingerprint artifacts and is not well defined for arch type fingerprints. In this study, we present a topological interpretation of fingerprint reference point, where the reference points are posed as topological features of fingerprint structure and can be detected seamlessly from either arch type or non‐arch type fingerprints. Extensive experiments demonstrate the advantages of the proposed method over the existing ones. Copyright © 2010 John Wiley & Sons, Ltd.

A fundamental problem in computer vision is to enhance images under various lighting conditions. This paper proposes an approach to lighting correction through reconstructing the image based on relative gradients. Compared with the absolute gradients as exemplified in the originally acquired image, the relative gradients are more stable under the variation of lighting. Extensive experiments have been carried out over images under a wide range of lighting variations. Comparisons with popular lighting correction methods are presented.

Fingerprint orientation plays important roles in fingerprint recognition. This paper proposes a framework for modeling the fingerprint orientation field based on the variational principle. The proposed method does not require any prior information about the structure of acquired fingerprints. Comparison has been made with respect to state-of-the-arts in fingerprint orientation modeling.

Image visibility is a fundamental issue in the field of computer vision. This paper investigates the connection between histogram and image visibility, where the concept of entropy is employed to depict the information content of the histogram. It turns out that image visibility is more dependent on the observed intensity levels with higher frequencies and the distribution of their locations in the range of intensity levels. With this in mind, the concept of visible entropy is proposed. The usefulness of the proposed visibility measure has been evaluated using a number of realistic images.

This paper presents a novel method for fingerprint orientation modeling, which executes in two phases. Firstly, the orientation field is reconstructed through fitting to a lower order Legendre polynomial basis to capture the global orientation pattern. Then the preliminary model around the singular region is dynamically refined by fitting to a higher order Legendre polynomial basis. The singular region is automatically detected through the analysis on the orientation residual field between the original orientation field and the orientation model. The method has been evaluated using the FVC 2004 data sets and compared with state-of-the-arts. Experiments turn out that the propose method attains higher accuracy in fingerprint matching and singularity preservation.

Singular point detection is an important issue in fingerprint image analysis. General methods like Poincare index method can detect singular points in non-arch type fingerprints but fail on arch-type fingerprints. Some more sophisticated methods like complex filter method also face the same problem. In this paper, we propose a systematic method for detecting singular points in fingerprint images which utilizes the most fundamental topological feature of acquired fingerprints as the basis for singular point identification. The method differentiates the input fingerprint between arch type and non-arch type. For non-arch type fingerprints singular points are detected as intersection points in a c(>2) level segmentation map. As for arch type fingerprints, singular points are identified from the symmetric line of the fingerprint structure. The method is evaluated using the NIST DB4 database and compared with the complex filter method. The proposed method attains near 90% success rate in detecting singular points and the displacement from ground truth is comparable to that of the complex filter method. Moreover our method is more than once faster in CPU time.

This paper proposes to characterize the fingerprint orientation using a novel topological representation, which transforms the orientation field into a map composed of a set of geometric objects and analyzes the properties of each geometric object based on two assumptions: regional coherence assumption and convexity assumption. Different from prior works on fingerprint orientation analysis, this approach has a capability to identify where the errors happen, not only the ldquorandom errorsrdquo (which is assumed by local-filtering and global modeling methods), but also the ldquostructure irregularitiesrdquo, which often occur in real prints and impose difficulty on existing techniques.

A new deformable model called active b-snake model (ABM) is presented for object boundary extraction. First, an affine-invariant landmark point assignment strategy is proposed to avoid manually assign landmarks. Second, an adaptive control point insertion algorithm is used to enhance the flexibility of B-Snake to describe complex shape. Third, for modeling the shape distribution and appearance characteristics of landmark points in the training samples, a statistical framework is embedded into ABM. Finally, the minimum mean square error (MMSE) approach with control point insertion provides fine deformation to the desired object boundaries. Experimental results show that ABM can be used to achieve more accurate object extraction.

This paper presents a method to improve the semi-automatic method for intensity inhomogeneity correction by Dawant et al. through introducing a fully automatic approach to reference points generation, which is based on order statistics and integrates information from the fine to coarse scale representations of the input image. The method has been validated and compared with two popular methods, N3 and BFC. Advantages of the proposed method are demonstrated.

A novel thresholding approach to confine the intensity frequency range of the object based on supervision is introduced. It consists of three steps. First, the region of interest (ROI) is determined in the image. Then, from the histogram of the ROI, the frequency range in which the proportion of the background to the ROI varies is estimated through supervision. Finally, the threshold is determined by minimizing the classification error within the constrained variable background range. The performance of the approach has been validated against 54 brain MR images, including images with severe intensity inhomogeneity and/or noise, CT chest images, and the Cameraman image. Compared with nonsupervised thresholding methods, the proposed approach is substantially more robust and more reliable. It is also computationally efficient and can be applied to a wide class of computer vision problems, such as character recognition, fingerprint identification, and segmentation of a wide variety of medical images.

In this paper, we propose an automatic method to segment the Sylvian fissure (SF) from T1-weighted magnetic resonance (MR) images. The method consists of four steps. First, morphological methods are applied to remove non-brain tissues. Then, 2D region growing is used to segment the cerebral spinal fluid (CSF) and gray matter (GM) in the SF, in which spatial relationships are used to control the region growing. A modified fuzzy c-means (FCM) clustering algorithm is introduced as well, which is more accurate than the conventional FCM method. After that, we get the 3D volume of the SF through segmenting CSF and GM slice by slice. Finally, CSF in the SF is extracted incorporating the anatomical information on a constant cortex thickness. We have demonstrated our method through both phantom and patient data.