Research on Disparity Map Generation Method of Underwater Target Based on the Improved SIFT Algorithm

Aiming at the defects of PET beverage bottles in the process of capping and filling, an improved algorithm based on visual inspection is proposed. First, an improved SIFT algorithm is proposed to improve the running speed, and OPENCV, C++, and Virtual Studio are used to build the interface and implement the algorithm. The preprocessed images are analyzed by the improved SIFT algorithm, and experiments and data analysis are carried out. The research results show that compared with the traditional SIFT algorithm, the improved SIFT algorithm greatly saves the algorithm time when detecting PET bottle capping defects and retains the anti-interference ability of SIFT feature points. The detection of insufficient liquid levels provides a new method and makes the algorithm more practical and valuable.

Video mosaic is an extend technology which based on image mosaic. It has a wide range of applications such as video surveillance, television, military inspection and other fields. SIFT algorithm is widely used in computer vision fields, this algorithm can be used in multivideo mosaic. but this algorithm is of high complexity. So we present an improved SIFT algorithm based on SIFT feature extraction, and we use this improved algorithm in the fast video sticking area. This algorithm is achieved with multicore system. Experimental results show that our improved SIFT algorithm can complete muliti-video mosaics, and can work better than original SIFT algorithm in combining a greater view. at the same time, this improved algorithm has a good performance than other similar algorithm.

A new SAR image registration method was Proposed based on improved SIFT algorithm. Which adopted multi-core system platform was used to overcoming the problem of high complexity algorithm of SIFT algorithm; According to the characteristics of SAR image, first of all, the source SAR image was enhanced in airspace, and finish the parallel extraction of feature points with the improved SIFT algorithm, then used Euclidean distance and the RANSAC algorithm to complete the matching of feature points and eliminate unmatching, finally realizes the SAR image registration. The experimental results show that the method can guarantee in the registration precision and reduce the complexity of the registration. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4414

In this paper, we propose a stereo matching method based on adaptive plane-fitting. In the proposed method, we use the disparity map adjustment with adaptive weight filter to preserve the boundary region. To estimate the initial disparity map, we use two cost aggregation functions, and in addition to that, we combined color and gradient for cost computation. In addition, the error value of the initial disparity map is calibrated by applying plane-fitting using mean-shift segmentation. The disparity map is adjusted by using adaptive weights to improve the accuracy of the disparity map. Experimental results show that the proposed method generates a high-quality disparity map which has the distinct boundary between objects compared to other methods.

As-planar-as-possible depth map estimation

By the aid of Tangent-Cross-Point feature, a novel improved SIFT algorithm is proposed in this paper. This method is more efficient and reliable in some cases, since it adopts Tangent-Cross-Point feature to improve the efficiency and robustness of SIFT algorithm. With a pair of matching feature points and a pair of curves nearby, it’s very easy to estimate the values of local transformation parameters and validate the correctness of this matching by the aid of Tangent-Cross-Point feature.

Stereo vision finds a wide range of applications in automotive, object detection, robot navigation, agriculture mapping, and others. Stereo matching is a stereo algorithm targeted to identify the corresponding pixels from two or more images. This study shows the implementation of stereo matching using the Sum of Absolute Difference (SAD) algorithm to extract the object's depth or disparity from stereo images. Our key objective revolves around the implementation of the stereo matching algorithm for a small field-programmable gate array (FPGA)—requiring relatively few resources while maintaining the processing speed as well as disparity map. For meeting this requirement, we used small window buffers to compute the stereo matching. The occluded pixels were reduced by introducing secondary consistency checking implementation. From the results of experiments performed using the Zynq UltraScale+ ZCU102 FPGA board with SDSoC compiler, the processing speed for computing the stereo matching algorithm with a 4×4 window buffer was 0.038 s for an image size of 486×720 pixels and 0.051 s for 375×1242 pixels resolution. The proposed design needed 1% each of BRAM and FF and 7% of LUT. An 18% reduction in the pixel errors has been observed when employing the secondary consistency matching on the post-processing.

The stereo matching <span>process is one of the key areas that impact the stereo vision technologies which are commonly used in the application of three-dimensional reconstructions. The accuracy of the depth information used in three-dimensional reconstruction is directly proportional to the accuracy of the disparity obtained from stereo matching. The challenging issue in the stereo matching process is to determine the accurate corresponding point between the left image and right image, especially for image pairs that have different exposure such as different illumination and image pair with less texture region. In order to increase the accuracy of disparity value, a new stereo matching algorithm is proposed based on the combination of Sum of absolute different and census transform at matching cost computation. guided filter was used in the matching cost aggregation in order to remove noise and preserve the edge of the image. In the optimization step, the winner take all strategy is used to select the minimum matching cost. Finally, a median filter is applied to the initial disparity map for refinement purposes. The experimental results show that the algorithm is effective in reducing the error and improving the accuracy of the disparity map in different illumination regions, less textured regions and different environmental exposure.</span>

In this paper, we propose the development of a system for generating disparity maps using rectified images, one technique that is booming in depth perception systems in computer vision. For this reason, in this project, is made a brief explanation process associated with mating techniques for stereo vision. For this project, we explore correlation algorithms, such as sum of absolute differences and Sum of Hamming Distances, combining the latter with the use of Census Transform. Additionally, we present the behavior of disparity maps for images that have been affected by impulsive noise. Initially rectified images were considered, which were taken from the image bank, presented by Middlebury College, who have worked extensively in the stereo vision problem. In this case, we used the known stereo pair Tsukuba.

In stereo vision, disparity map after stereo matching using projection model can get the depth information and 3D information of the original image. In order to improve the accuracy and speed of stereo matching, improving the quality of disparity map becomes the core problem of stereo matching. Firstly, the paper extracted edge information of binocular images using Canny, Scharr and Sobel edge detection algorithm. On this basis, the paper use SGBM algorithm(Semi-global block Matching) and DP algorithm(Dynamic Programming)and CSCA algorithm(Cross-scale Cost Aggregation) respectively calculated the disparity map and its error rate. After analysis, the combination of Canny detection algorithm and CSCA algorithm can eliminate a large number of number of irrelevant information and reduce the amount of data processing. The real scene image is selected to validate the method .Based on the above research results, the method improved the quality of disparity map.

To realize the accuracy and stability of the underwater binocular image matching, this paper design a stereo matching algorithm based on Harris corner detection. Firstly, apply median and homomorphic filtering for underwater stereo image preprocessing to minimize noise and enhance image contrast. Secondly, apply the corner feature extraction and matching method based on Harris for obtaining the characteristic information of underwater target. And then apply the constraint condition to eliminate the error matched points, so as to improve the underwater image matching stability. Finally, the experiments verify the algorithm proposed in this paper, and the experimental results have shown the effectiveness of this algorithm.

Traditional two-dimensional vision only has basic pixel information and no depth information, while three-dimensional vision has depth information, which has high application value in the era of intelligence. The mainstream methods for obtaining depth information of objects include binocular stereo matching and TOF (Time of flight) cameras. The depth obtained by binocular stereo matching is with high precision in high-texture objects but with low precision in low-texture and repeated-texture objects, while the precision of depth obtained by TOF is the exact opposite. By combining binocular stereo matching and TOF, high-precision depth maps can be obtained in various scenarios. This paper proposes a new method of combining binocular stereo matching and TOF technology. The credible depth information is obtained through TOF to guide the stereo matching of the binocular camera, and then the information obtained after the guided matching is fused with the TOF information. The final fusion result is a high-precision depth map. The experimental results show that the fused depth map has higher accuracy and the execution speed within a reasonable range.

Warping-based spectral translation network for unsupervised cross-spectral stereo matching

It is an important problem for computer vision to match the stereo image pair. Only the problems of stereo matching are solved, the accurate location or measurement of object can be realized. In this paper, an integrated stereo matching approach is presented. Unlike most stereo matching approach, it integrates area-based and feature-based primitives. This allows it to take advantages of the unique attributes of each of these techniques. The feature-based process is used to match the image feature. It can provide a more precise sparse disparity map and accurate location of discontinuities. The area-based process is used to match the continuous surfaces.It can provide a dense disparity map. The techniques of stereo matching with adaptive window are adopted in the area-based process. It can make the results of area-based process get high precise. An integrated process is also used in this approach. It can integrate the results of feature-based process and area-based process, so that the approach can provide not only a dense disparity map but also an accurate location of discontinuities. The approach has been tested by some synthetic and nature images. From the results of matched wedding cake and matched aircraft model, we can see that the surfaces and configuration are well reconstructed. The integrated stereo matching approach can be used in 3D part recognition in intelligent assembly system and computer vision.

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.