Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Monocular SLAM Supported Object Recognition

In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.Comment: Accepted to appear at Robotics: Science and Systems 2015, Rome, Ital