Efficiently Tracking Homogeneous Regions in Multichannel Images

We present a method for tracking Maximally Stable Homogeneous Regions (MSHR) in images with an arbitrary number of channels. MSHR are conceptionally very similar to Maximally Stable Extremal Regions (MSER) and Maximally Stable Color Regions (MSCR), but can also be applied to hyperspectral and color images while remaining extremely efficient. The presented approach makes use of the edge-based component-tree which can be calculated in linear time. In the tracking step, the MSHR are localized by matching them to the nodes in the component-tree. We use rotationally invariant region and gray-value features that can be calculated through first and second order moments at low computational complexity. Furthermore, we use a weighted feature vector to improve the data association in the tracking step. The algorithm is evaluated on a collection of different tracking scenes from the literature. Furthermore, we present two different applications: 2D object tracking and the 3D segmentation of organs.Comment: to be published in ICPRS 2017 proceeding

A comparative evaluation of interest point detectors and local descriptors for visual SLAM

Abstract In this paper we compare the behavior of different interest points detectors and descriptors under the conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM). We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors, under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes. We believe that this information will be useful when selecting an appropriat

A Hybrid Connecting Character Based Text Recognition and Extraction Algorithm

Traffic sign recognition is a technology by which a vehicle is able to recognize the traffic signs put on the road e.g. "speed limit" or "children" or "turn ahead". In this paper a novel Connecting Character based text recognition and extraction algorithm is designed which uses Maximally Stable Extremely Regions (MSER) for test candidate recognition and extraction from traffic signs. Despite their auspicious properties, MSER has been conveyed to be delicate towards blurred Image. To allow for detecting small letters in images of limited resolution or blurred Image, the complimentary properties of Lucy-Richardson Algorithm and canny edge Algorithm is used

Development of a text reading system on video images

Since the early days of computer science researchers sought to devise a machine which could automatically read text to help people with visual impairments. The problem of extracting and recognising text on document images has been largely resolved, but reading text from images of natural scenes remains a challenge. Scene text can present uneven lighting, complex backgrounds or perspective and lens distortion; it usually appears as short sentences or isolated words and shows a very diverse set of typefaces. However, video sequences of natural scenes provide a temporal redundancy that can be exploited to compensate for some of these deficiencies. Here we present a complete end-to-end, real-time scene text reading system on video images based on perspective aware text tracking. The main contribution of this work is a system that automatically detects, recognises and tracks text in videos of natural scenes in real-time. The focus of our method is on large text found in outdoor environments, such as shop signs, street names and billboards. We introduce novel efficient techniques for text detection, text aggregation and text perspective estimation. Furthermore, we propose using a set of Unscented Kalman Filters (UKF) to maintain each text region¿s identity and to continuously track the homography transformation of the text into a fronto-parallel view, thereby being resilient to erratic camera motion and wide baseline changes in orientation. The orientation of each text line is estimated using a method that relies on the geometry of the characters themselves to estimate a rectifying homography. This is done irrespective of the view of the text over a large range of orientations. We also demonstrate a wearable head-mounted device for text reading that encases a camera for image acquisition and a pair of headphones for synthesized speech output. Our system is designed for continuous and unsupervised operation over long periods of time. It is completely automatic and features quick failure recovery and interactive text reading. It is also highly parallelised in order to maximize the usage of available processing power and to achieve real-time operation. We show comparative results that improve the current state-of-the-art when correcting perspective deformation of scene text. The end-to-end system performance is demonstrated on sequences recorded in outdoor scenarios. Finally, we also release a dataset of text tracking videos along with the annotated ground-truth of text regions