Feature Evaluation for Mobile Applications: A Design Science Approach Based on Evolutionary Software Prototypes

Igler, Bodo

doi:10.1007/978-3-642-39253-5_75

Cited by 1 publication

(2 citation statements)

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“…Gathering such knowledge in real life is complicated. Igler [3] introduced a lightweight process model to help identify virtual user interface and application logic properties. The iterative, incremental process approach is based on engineering principles for software product lines and combined with design science research.…”

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

confidence: 99%

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

confidence: 99%

“…Incorporating key features specific to their intended use is critical to the success of mobile applications [3]. In image processing,different methods, and techniques extract the features.…”

Section: Introductionmentioning

confidence: 99%