CBRISK: Colored Binary Robust Invariant Scalable Keypoints

Jing, Huiyun; He, Xin; Han, Qi; Niu, Xiamu

doi:10.1587/transinf.e96.d.392

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…BRISK loses information about the image colors, which can provide more key points for matching points. Owing to this reason, a CBRISK algorithm is proposed to maintain the information of the RGB color channels [72]. To decrease computation time, the SBRISK development shifts the binary vector rather than rotating the image pattern or constellation, as many other descriptors do [73].…”

Section: Briskmentioning

confidence: 99%

Image Mosaicing Applied on UAVs Survey

et al. 2022

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The use of UAV (unmanned aerial vehicle) technology has allowed for advances in the area of robotics in control processes and application development. Such is the case of image processing, in which, by the use of aerial photographs taken by these aircrafts, it is possible to perform surveillance and monitoring tasks. As an example, we can mention the use of aerial photographs for the generation of panoramic images through the process of stitching images without losing image resolution. Some applications are photogrammetry and mapping, where the main problems to be solved are image alignment and ghosting images, for which different stitching techniques can be applied. These methodologies can be categorized into direct methods or feature-based methods. This paper aims to show an overview of the most frequently applied mosaicing techniques in UAVs by providing an introduction to those interested in developing in this area. For this purpose, a summary of the most applied techniques and their applications is given, showing the trend of the research field and the contribution of different countries over time.

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Section: Briskmentioning

confidence: 99%

Image Mosaicing Applied on UAVs Survey

et al. 2022

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“…Thereafter, color synthesis for the feature descriptors is addressed by these four group points from four color images. Compared with color-invariant methods [ 57 , 58 ], the proposed method generates SC keypoints that can preserve the true color information in the image without deteriorating the spectral information. Therefore, the SC descriptors of the most representative keypoints are expected to be more distinctive than those obtained via the grayscale image alone.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, most image matching algorithms convert an optical image into a grayscale image and utilize pixel intensities to match different images; color information, namely the red, green, and blue frequency bands, is not involved. A few methods, such as the colored SIFT (CSIFT) [ 57 ] and colored BRISK (CBRISK) [ 58 ] techniques, use a spectrum model to normalize color spaces to generate color-invariant images and thus avoid the influences of different illumination conditions caused by radiometric changes. However, both techniques may alter the true electromagnetic information stored in the original imagery data, thus leading to some mismatches with unknown causes.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points

Cheng

Matsuoka

2021

Sensors

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Matching local feature points is an important but crucial step for various optical image processing applications, such as image registration, image mosaicking, and structure-from-motion (SfM). Three significant issues associated with this subject have been the focus for years, including the robustness of the image features detected, the number of matches obtained, and the efficiency of the data processing. This paper proposes a systematic algorithm that incorporates the synthetic-colored enhanced accelerated binary robust invariant scalar keypoints (SC-EABRISK) method and the affine transformation with bounding box (ATBB) procedure to address these three issues. The SC-EABRISK approach selects the most representative feature points from an image and rearranges their descriptors by adding color information for more precise image matching. The ATBB procedure, meanwhile, is an outreach that implements geometric mapping to retrieve more matches from the feature points ignored during SC-EABRISK processing. The experimental results obtained using benchmark imagery datasets, close-range photos (CRPs), and aerial and satellite images indicate that the developed algorithm can perform up to 20 times faster than the previous EABRISK method, achieve thousands of matches, and improve the matching precision by more than 90%. Consequently, SC-EABRISK with the ATBB algorithm can address image matching efficiently and precisely.

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“…Performance depends on the percentage of synthetic Mikolajczyk and Schmid [12] suggested the Harris-Affine detector retrieved local interest region descriptors and compared them to shift and distributed descriptors. Jing et al [13] told colored binary robust invariant scalable key points (CBRISK). They're coloured binary key points in this publication.…”

Section: Figure 1 Inlier Extraction Of the Real-world Imagesmentioning

confidence: 99%

Image quality evaluation: evaluation of the image quality of actual images by using machine learning models

Reddy,

Maheswara Rao,

Sravani

et al. 2024

Bulletin EEI

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Evaluating image features is a significant step in image processing in applications like number plate detection, vehicle tracking and many image processing-based applications. Image processing-based applications need accurate parts to get the best outcomes. Feature detection is done based on various feature detection techniques. The proposed system aims to get the best feature detector based on the input images by evaluating the image features. For assessing the image features, the proposed system worked on various descriptors like oriented FAST and rotated brief (ORB), learned arrangements of three patch codes (LATCH), binary robust independent elementary features (BRIEF), and binary robust invariant scalable keypoints (BRISK) to extract and evaluate the features using K-nearest neighbor (KNN)-matching and retrieve the inliers of the matching. Each descriptor produces different matching features and inliers; with the matchings and inliers, the inlier ratio calculates to show the analysis. To increase performance, we also examine adding depth information to descriptors.

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CBRISK: Colored Binary Robust Invariant Scalable Keypoints

Cited by 7 publications

References 14 publications

Image Mosaicing Applied on UAVs Survey

Image Mosaicing Applied on UAVs Survey

An Efficient and Precise Remote Sensing Optical Image Matching Technique Using Binary-Based Feature Points

Image quality evaluation: evaluation of the image quality of actual images by using machine learning models

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