Keypoint detection by wave propagation

We propose to rely on the wave equation for the detection of repeatable keypoints invariant up to image scale and rotation and robust to viewpoint variations, blur, and lighting changes. The algorithm exploits the properties of local spatial–temporal extrema of the evolution of image intensities under the wave propagation to highlight salient symmetries at different scales. Although the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to poorly textured objects, which are hardly dealt with by current pipelines based on local invariant features. The impact on the overall algorithm of different numerical wave simulation schemes and their parameters is discussed, and a pyramidal approximation to speed-up the simulation is proposed and validated. Experiments on publicly available datasets show that the proposed algorithm offers state-of-the-art repeatability on a broad set of different images while detecting regions that can be distinctively described and robustly matched

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

First published: April 2016This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Cell theory has officially reached 350 years of age as the first use of the word ‘cell’ in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication “Micrographia: or some physiological definitions of minute bodies”. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the sub-cellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more inter-disciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft-neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking

A generalised framework for saliency-based point feature detection

Here we present a novel, histogram-based salient point feature detector that may naturally be applied to both images and 3D data. Existing point feature detectors are often modality specific, with 2D and 3D feature detectors typically constructed in separate ways. As such, their applicability in a 2D-3D context is very limited, particularly where the 3D data is obtained by a LiDAR scanner. By contrast, our histogram-based approach is highly generalisable and as such, may be meaningfully applied between 2D and 3D data. Using the generalised approach, we propose salient point detectors for images, and both untextured and textured 3D data. The approach naturally allows for the detection of salient 3D points based jointly on both the geometry and texture of the scene, allowing for broader applicability. The repeatability of the feature detectors is evaluated using a range of datasets including image and LiDAR input from indoor and outdoor scenes. Experimental results demonstrate a significant improvement in terms of 2D-2D and 2D-3D repeatability compared to existing multi-modal feature detectors

A generalised framework for saliency-based point feature detection

Here we present a novel, histogram-based salient point feature detector that may naturally be applied to both images and 3D data. Existing point feature detectors are often modality specific, with 2D and 3D feature detectors typically constructed in separate ways. As such, their applicability in a 2D-3D context is very limited, particularly where the 3D data is obtained by a LiDAR scanner. By contrast, our histogram-based approach is highly generalisable and as such, may be meaningfully applied between 2D and 3D data. Using the generalised approach, we propose salient point detectors for images, and both untextured and textured 3D data. The approach naturally allows for the detection of salient 3D points based jointly on both the geometry and texture of the scene, allowing for broader applicability. The repeatability of the feature detectors is evaluated using a range of datasets including image and LiDAR input from indoor and outdoor scenes. Experimental results demonstrate a significant improvement in terms of 2D-2D and 2D-3D repeatability compared to existing multi-modal feature detectors

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

Efficient region proposals for polip detection

In this thesis we deal with the issue of polyp detection in images of oysters. Modern methods of object detection are often composed of two parts. Firstly we use a fast region proposal method to generate hypothetical regions in places where objects are located with a higher probability. We then use a strong classifier to verify the proposed hypothetical regions. We address the issue of the first part of the object detection pipeline and we propose a new region proposal method for the purpose of detecting polyps in images of oysters. We compared the results of our method with other region proposal methods such as ACF and Selective Search. We also propose a metric for region proposal method performance based on the AR metric. Polyp detection is a hard problem to solve due to the visual properties of individual polyps as they often vary in size, orientation and opacity which causes them to blend with their surroundings and can often make reliable detection difficult

Self-Similarity Based Corresponding-Point Extraction from Weakly Textured Stereo Pairs

For the areas of low textured in image pairs, there is nearly no point that can be detected by traditional methods. The information in these areas will not be extracted by classical interest-point detectors. In this paper, a novel weakly textured point detection method is presented. The points with weakly textured characteristic are detected by the symmetry concept. The proposed approach considers the gray variability of the weakly textured local regions. The detection mechanism can be separated into three steps: region-similarity computation, candidate point searching, and refinement of weakly textured point set. The mechanism of radius scale selection and texture strength conception are used in the second step and the third step, respectively. The matching algorithm based on sparse representation (SRM) is used for matching the detected points in different images. The results obtained on image sets with different objects show high robustness of the method to background and intraclass variations as well as to different photometric and geometric transformations; the points detected by this method are also the complement of points detected by classical detectors from the literature. And we also verify the efficacy of SRM by comparing with classical algorithms under the occlusion and corruption situations for matching the weakly textured points. Experiments demonstrate the effectiveness of the proposed weakly textured point detection algorithm

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available