Touch-based object localization is an important component of autonomous robotic systems that are to perform dexterous tasks in real-world environments. When the objects to locate are placed within clutters, this touch-based procedure tends to generate outlier measurements which, in turn, can lead to a significant loss in localization precision. Our first contribution is to address this problem ...

Co-registered intensity and range imaging is one example of complementary multi-modality that has been extensively explored for 3D reconstruction, yet not fully researched for robust estimation. The classic RANdom SAmple Consensus and its variants such as PROSAC, Preemptive RANSAC and SCRAMSAC, to name a few, have been the most commonly applied approaches to visionbased homography estimation, w...

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing...

The random sample consensus (RANSAC) algorithm is frequently used in computer vision to estimate the parameters of a signal in the presence of noisy and even spurious observations called gross errors. Instead of just one signal, we desire to estimate the parameters of multiple signals, where at each time step a set of observations of generated from the underlying signals and gross errors are re...

We present a method for automatic surgical tool localization in 3D ultrasound images based on line filtering, voxel classification and model fitting. This could possibly provide assistance for biopsy needle or micro-electrode insertion, or a robotic system performing this insertion. The line-filtering method is first used to enhance the contrast of the 3D ultrasound image, then a classifier is ...

This paper summarizes the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). This paper uses KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) to the three methods in order to analyze the results of the methods’ application in recognition. KNN is used to find the matches, an...

To improve the accuracy of image matching shoeprint image feature matching method based on PCA-SIFT is proposed. Firstly, feature detection and pre-matching of images are done by using PCA-SIFT (principal component analysisscale invariant feature transform) algorithm. And then, the correlation coefficient is used as similarity measurement, which can filter image interest points. By this method,...

For object recognition under varying illumination conditions, we propose a method based on photometric alignment. The photometric alignment is known as a technique that models both diffuse reflection components and attached shadows under a distant point light source by using three basis images. However, in order to reliably reproduce these components in a test image, we have to take into accoun...