Non-linear Intensity Changes Research Articles

Multispectral Image Matching Method Based on Histogram of Maximum Gradient and Edge Orientation

Motivated by the problem of nonlinear intensity changes in multispectral image matching, this letter introduces a robust and efficient image-matching method. The proposed method consists of three steps. First, control-point candidates are identified that are widely distributed in the areas of effective main structure. Then, a novel feature descriptor called the histogram of maximum gradient and edge orientation (HGEO) is proposed for the purpose of multispectral image matching. Finally, a bilateral matching process is carried out to perform the matching process and remove mismatches. The proposed method is successfully applied for matching various multispectral remote sensing images, and experiments are performed with typical datasets that are widely applied in tests of multispectral image matching. According to some popular feature descriptors, the test results demonstrate that the proposed HGEO achieves better matching performance than do many currently used methods.

An iterative phase-correction method for low-quality phase-shift images and its application

To solve the issue of phase recovery from low-quality fringe images (where the low quality is caused by noise, non-linear intensity and surface reflectivity changes), an iterative phase-correction method is proposed. The high-phase-precision solution is achieved by regenerating the phase-shift-fringe images from the wrapped phase and performing iterative filtering. In general, compared with conventional methods, the proposed iterative phase-correction method has three main advantages: (a) it suppresses the influence of noise without interference from surface reflectivity; (b) it effectively improves the wrapped phase accuracy, thereby improving 3D measurement accuracy, and (c) it effectively recovers the absolute phase (determined by a multi-frequency heterodyne method) information of surfaces with very low or very high reflectivities, without the additional projection of phase-shift-fringe images. The effectiveness of the proposed iterative phase-correction method is verified by simulation and experiment. The proposed method, applied to binocularly structured light, can improve measurement accuracy and greatly improve the effectiveness of measurement of objects with drastic changes in reflectivity. Based on the calibration accuracy (without gamma calibration of the projector) described in this paper, the root-mean-square deviation of the measurement results for binocularly structured light can reach 0.0094 mm.

A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency.

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.

Histogram of maximal point-edge orientation for multi-source image matching

ABSTRACT Features determined from keypoints or edges are widely used in multi-source image matching. However, for cases that exhibit non-linear intensity changes and significant noise, identifying sufficient identical features for multi-source image pairs can be complicated. Motivated by problems encountered in existing multi-source image matching algorithms, a robust and effective algorithm for multi-source image matching was proposed in this paper. First, keypoints uniformly and sufficiently distributed on the significant edge were extracted by a location-based boosting detector. Second, in order to effectively describe the corresponding region and reduce the influence of noise and non-linear intensity changes, a novel descriptor, denoted as the histogram of point-edge orientation (HPEO) was proposed for multi-source image matching. A bilateral matching process was then used to remove the incorrect matches. Experiments were performed with standard infrared-visible datasets and the results demonstrate that the proposed algorithm achieves a more accurate matching performance.

A Local Feature Descriptor Based on Combination of Structure and Texture Information for Multispectral Image Matching

Due to the significant nonlinear intensity changes of multispectral images, automatic image feature point matching is a challenging task. This letter addresses the problem and proposes a novel descriptor combining the structure and texture information to solve the nonlinear intensity variations of multispectral images. We first propose directional maps, i.e., the directional response maps (DMs) and the directional response binary maps (DBMs), which can capture the common structure and texture properties of multispectral images, respectively. We then use the spatial pooling pattern of the histogram of oriented gradients to separately describe the local region of each point of interest based on the DMs and DBMs. In order to speed up the calculation, we apply Gaussian filters to the DMs and average filters to the DBMs to construct the per-pixel histogram bins. Finally, we conjoin the normalized feature vectors corresponding to the structure description and texture description of each point of interest to obtain the histograms of directional maps (HoDMs). The proposed HoDM descriptor was evaluated using three data sets composed of images obtained in both visible light and infrared spectra. The experimental results confirm that the proposed HoDM descriptor is robust to the nonlinear intensity changes of multispectral images and has a superior matching performance as well as a much higher computational efficiency.

Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes.

Step‐by‐step pipeline processing approach for line segment detection

This study proposes a line segment detection that can efficiently and effectively handle non-linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre-processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state-of-the-art line detectors. Moreover, the performance of the proposed method produces a combined advantage of approximate to 17% in the coverage of line segments over the line segment detector with noisy images.

A novel coarse-to-fine method for registration of multispectral images

Due to non-linear intensity changes between multispectral images, the existed descriptors often yield low matching performance. In order to build reliable keypoint mappings on multispectral images, a novel coarse-to-fine method is designed using projective transformation and the information of edge overlap. The method consists of a coarse process and a fine-tuning process. In the coarse process, initial keypoint mappings are built with the descriptors associated with keypoints and the relative distance constraints are employed on them to remove outliers. In the fine-tuning process, the edge overlap information is utilized as similarity metric and an iterative framework is applied to search correct keypoint mappings. The performance of the proposed is investigated with keypoints extracted by speeded-up robust features. The experiment results show that the proposed method can build more reliable keypoint mappings on multispectral images than existed methods.

High-resolution multispectral satellite image matching using scale invariant feature transform and speeded up robust features

Satellite images captured in different spectral bands might exhibit nonlinear intensity changes at the corresponding spatial locations due to the different reflectance responses for these bands. This affects the image registration performance negatively as the corresponding features might have different properties in different bands. We propose a modification to the widely used scale invariant feature transform (SIFT) method to increase the correct feature matching ratio and to decrease the computation time of this algorithm for the multispectral satellite images. We also apply scale restriction to SIFT and speeded up robust features (SURF) algorithms to increase the correct match ratio. We present test results for variations of SIFT and SURF algorithms. The results show the effectiveness of the proposed improvements compared to the other SIFT- and SURF-based methods.

Two-Dimensional Correlation Spectroscopy: Effect of Normalization of the Dynamic Spectra

Simulation studies have demonstrated that linear nonselective intensity variations coupled with nonlinear selective intensity changes may develop new features in two-dimensional (2D) correlation spectra. Some types of linear nonselective intensity changes are hardly seen in the normal and synchronous 2D correlation spectrum. In contrast, they may develop quite strong features in the companion asynchronous spectrum, especially if the selective intensity variations for different bands have similar response functions. The simplest way of removing this effect is normalization of the dynamic spectra prior to 2D correlation analysis. This operation can be easily performed if we know the relationship between the perturbation and the nonspecific intensity variations. Otherwise, one has to employ an “internal reference” for normalization of the experimental spectra. The “internal reference” means the band that does not selectively change in its intensity under given perturbation. Fourier transform near-infrared (FT-NIR) measurements of octan-1-ol in CCl4 revealed a strong correlation between the concentration and the integrated intensity of the second overtone of the v(C–H) band. Also a strong correlation was found between the integrated intensity of the same band and the temperature-induced density changes of pure octan-1-ol. Thus, in the NIR region the second overtone of the v(C–H) band can be successfully applied for normalization of both the concentration and temperature-perturbed spectra of numerous organic samples. The most complicated situation appears for a rheo-optical experiment involving pronounced deformation, where any simple normalization of the experimental spectra cannot be applied. In this instance, knowledge of the exact relationship between the strain and the sample thickness is required.