Most automatic chord recognition systems follow a standard approach combining chroma feature extraction, filtering and pattern matching. However, despite much research, there is little understanding about the interaction between these different components, and the optimal parameterization of their variables. In this paper we perform a systematic evaluation including the most common variations i...

In this paper we present a robust feature matching scheme in which features can be matched in 2.3μs. For a typical task involving 150 features per image, this results in a processing time of 500μs for feature extraction and matching. In order to achieve very fast matching we use simple features based on histograms of pixel intensities and an indexing scheme based on their joint distribution. Th...

This paper proposes a new feature point detector which uses a wedge model to characterize corners by their orientation and angular width. This detector is compared to two popular feature point detectors: the Harris and SUSAN detectors, on the basis of some defined quality attributes. It is also shown how feature points between widely separated views can be matched by using the information provi...

This paper presents a statistical method to match feature points from stereo pairs of images. The proposed method is evaluated in terms of effectiveness, robustness and computational speed. The evaluation was performed on several pairs of real stereo images of natural scenes taken onboard an unmanned aerial vehicle. The results show that the proposed method reduces the number of incorrect match...

Detection, Labeling, and segmentation of the spinal column from CT images is a pre-processing step for a range of image-guided interventions. State-of-the art techniques have focused either on image feature extraction or template matching for labeling of the vertebrae followed by segmentation of each vertebra. Recently, statistical multi-object models have been introduced to extract common stat...

Most cross-view image matching algorithms focus on designing network structures with excellent performance, ignoring the content information of image. At same time, there are non-fixed targets such as cars, ships, and pedestrians in ground perspective images aerial images. Differences perspective, direction, scale cause serious interference process. This paper proposes a method feature enhancem...

Numerous deep learning-based object detection methods have achieved excellent performance. However, the performance on small-size and positive negative sample imbalance problems is not satisfactory. We propose a multi-scale feature selective matching network (MFSMNet) to improve of alleviate problems. First, we construct semantic enhancement module (MSEM) compensate for information loss small-s...

Image feature matching is to seek, localize and identify the similarities across the images. The matched local features between different images can indicate the similarities of their content. Resilience of image feature matching to large view point changes is challenging for a lot of applications such as 3D object reconstruction, object recognition and navigation, etc, which need accurate and ...