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Finding corresponding image features between two images is often the first step for many computer vision algorithms. This paper introduces an improved synthetic basis feature descriptor algorithm that describes and compares image features in an efficient and discrete manner with rotation and scale invariance. It works by performing a number of similarity tests between the feature region surrounding the feature point and a predetermined number of synthetic basis images to generate a feature descriptor that uniquely describes the feature region. Features in two images are matched by comparing their descriptors. By only storing the similarity of the feature region to each synthetic basis image, the overall storage size is greatly reduced. In short, this new binary feature descriptor is designed to provide high feature matching accuracy with computational simplicity, relatively low resource usage, and a hardware friendly design for real-time vision applications. Experimental results show that our algorithm produces higher precision rates and larger number of correct matches than the original version and other mainstream algorithms and is a good alternative for common computer vision applications. Two applications that often have to cope with scaling and rotation variations are included in this work to demonstrate its performance. they perform well on intensity images and are robust against image variations, these algorithms require complex computations and large memory storage. These requirements are not desirable for resource-limited embedded applications and applications that require real-time performance.Several binary feature description algorithms have been developed to use simpler computation to provide descriptor with smaller size. Binary Robust Independent Elementary Features (BRIEF) [5] and the Binary Robust Invariant Scalable Key-points (BRISK) [6] are two representative and popular algorithms. A newer and improved version of BRIEF called ORB or rBRIEF has been developed [7] to cope with rotation invariance. These binary descriptors algorithms are sensitive to image variations and can have fewer accurate matches and a reduced match count compared to SIFT and SURF. A more detailed comparison of these algorithms is included in the next section.In our previous work, a new binary descriptor algorithm called SYnthetic BAsis (SYBA) descriptor was developed to obtain a higher percentage of correct matches [8]. SYBA performs binary comparisons between the binarized feature region and synthetic basis images to obtain a compact feature descriptor. SYBA's performance and size are comparable to other binary feature descriptors. However, like other binary feature descriptors, SYBA's feature point matching accuracy suffers under large amounts of image variations.In this paper, we introduce an improved version of SYBA called robust-SYBA (rSYBA). rSYBA maintains the same reduced descriptor size and computational complexity as the original SYBA while increasing the number of correct matches and matching precision, especiall...