Feature detection, description and matching are essential components of various computer vision applications, thus they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection and description algorithms.

Image Features Detection, Description and Matching

Texture information plays an important role in image analysis. Although several descriptors have been proposed to extract and analyze texture, the development of automatic systems for image interpretation and object recognition is a difficult task due to the complex aspects of texture. Scale is an important information in texture analysis, since a same texture can be perceived as different texture patterns at distinct scales. Gray level co-occurrence matrices (GLCM) have been proved to be an effective texture descriptor. This paper presents a novel strategy for extending the GLCM to multiple scales through two different approaches, a Gaussian scale-space representation, which is constructed by smoothing the image with larger and larger low-pass filters producing a set of smoothed versions of the original image, and an image pyramid, which is defined by sampling the image both in space and scale. The performance of the proposed approach is evaluated by applying the multi-scale descriptor on five benchmark texture data sets and the results are compared to other well-known texture operators, including the original GLCM, that even though faster than the proposed method, is significantly outperformed in accuracy.

Multi-scale gray level co-occurrence matrices for texture description

MODS: Fast and robust method for two-view matching☆

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information wit...

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2020.1859636

Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

/pdf/a-novel-image-retrieval-based-on-visual-words-integration-of-4nkbztiyoi.pdf

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

Fully affine invariant SURF for image matching

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Fully affine invariant SURF for image matching