Recovering hard-to-find object instances by sampling context-based object proposals
نویسندگان
چکیده
منابع مشابه
Recovering hard-to-find object instances by sampling context-based object proposals
In this paper we focus on improving object detection performance in terms of recall. We propose a post-detection stage during which we explore the image with the objective of recovering missed detections. This exploration is performed by sampling object proposals in the image. We analyse four different strategies to perform this sampling, giving special attention to strategies that exploit spat...
متن کاملVideo Object Segmentation using Tracked Object Proposals
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 [8] challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can ...
متن کاملGeodesic Object Proposals
We present an approach for identifying a set of candidate objects in a given image. This set of candidates can be used for object recognition, segmentation, and other object-based image parsing tasks. To generate the proposals, we identify critical level sets in geodesic distance transforms computed for seeds placed in the image. The seeds are placed by specially trained classifiers that are op...
متن کاملDiversity in Object Proposals
Current top performing object recognition systems build on object proposals as a preprocessing step. Object proposal algorithms are designed to generate candidate regions for generic objects, yet current approaches are limited in capturing the vast variety of object characteristics. In this paper we analyze the error modes of the state-of-the-art Selective Search object proposal algorithm and s...
متن کاملCategory Independent Object Proposals
We propose a category-independent method to produce a bag of regions and rank them, such that top-ranked regions are likely to be good segmentations of different objects. Our key objectives are completeness and diversity: every object should have at least one good proposed region, and a diverse set should be top-ranked. Our approach is to generate a set of segmentations by performing graph cuts...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Vision and Image Understanding
سال: 2016
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2016.08.007