Using Top-Points as Interest Points for Image Matching

We consider the use of so-called top-points for object retrieval. These points are based on scale-space and catastrophe theory, and are invariant under gray value scaling and offset as well as scale-Euclidean transformations. The differential properties and noise characteristics of these points are mathematically well understood. It is possible to retrieve the exact location of a top-point from any coarse estimation through a closed-form vector equation which only depends on local derivatives in the estimated point. All these properties make top-points highly suitable as anchor points for invariant matching schemes. In a set of examples we show the excellent performance of top-points in an object retrievaltask

Automotive top-view image generation using orthogonally diverging fisheye cameras

Advanced Driver Assistance Systems in vehicles can be a great assistance to drivers by providing them a quick and easy way to visualize their entire 360-degree surroundings. We introduce a new camera set-up for a surround-view imaging system that may be part of an ADAS. This set-up involves four wide-angle fisheye cameras with orthogonally diverging camera axes, which allows for capturing the entire 360 degrees around a vehicle in four images, captured from the lateral, front, and rear views. Simple perspective transforms can be used to convert these images into a synthesized top-view image, which displays the scene as viewed from above the vehicle. These transforms, however, are typically derived using a basic calibration procedure that is only capable of correctly mapping ground-plane points in captured images to their corresponding locations in the top-view image, and subsequently, all off-the-ground points look distorted. We present a new method for calibrating a top-view image, in which objects and off-the-ground points are accurately represented. We also present a method for using specifically designed disparity search bands to segment the scene in the overlapping field-of-view (FOV) regions between adjacent cameras, each pair of which is effectively a stereo imaging system. Such wide-baseline stereo systems with orthogonally diverging camera axes make stereo matching difficult, and traditional correspondence algorithms cannot reliably generate the dense disparity maps that might be computed in a parallel stereo set-up involving cameras that follow a rectilinear model. We segment the scene into the ground plane, objects of interest, and the background, and show that our new virtual camera calibration parameters can be applied to represent objects in the scene in a more realistic manner.Electrical and Computer Engineerin

SuperPoint: Self-Supervised Interest Point Detection and Description

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM Workshop (DL4VSLAM2018

SenseCam image localisation using hierarchical SURF trees

The SenseCam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day. Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer's location during capture time. We propose a method for automatically determining the wearer's location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered