6,401 research outputs found
Salient Object Detection via Augmented Hypotheses
In this paper, we propose using \textit{augmented hypotheses} which consider
objectness, foreground and compactness for salient object detection. Our
algorithm consists of four basic steps. First, our method generates the
objectness map via objectness hypotheses. Based on the objectness map, we
estimate the foreground margin and compute the corresponding foreground map
which prefers the foreground objects. From the objectness map and the
foreground map, the compactness map is formed to favor the compact objects. We
then derive a saliency measure that produces a pixel-accurate saliency map
which uniformly covers the objects of interest and consistently separates fore-
and background. We finally evaluate the proposed framework on two challenging
datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that
our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing methods represent the multi-scale features in a layer-wise manner. In
this paper, we propose a novel building block for CNNs, namely Res2Net, by
constructing hierarchical residual-like connections within one single residual
block. The Res2Net represents multi-scale features at a granular level and
increases the range of receptive fields for each network layer. The proposed
Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these
models and demonstrate consistent performance gains over baseline models on
widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies
and experimental results on representative computer vision tasks, i.e., object
detection, class activation mapping, and salient object detection, further
verify the superiority of the Res2Net over the state-of-the-art baseline
methods. The source code and trained models are available on
https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure
Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects
Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Segmentation Based Features for Wide-Baseline Multi-view Reconstruction
A common problem in wide-baseline stereo is the sparse and non-uniform distribution of correspondences when using conventional detectors such as SIFT, SURF, FAST and MSER. In this paper we introduce a novel segmentation based feature detector SFD that produces an increased number of ‘good’ features for accurate wide-baseline reconstruction. Each image is segmented into regions by over-segmentation and feature points are detected at the intersection of the boundaries for three or more regions. Segmentation-based feature detection locates features at local maxima giving a relatively large number of feature points which are consistently detected across wide-baseline views and accurately localised. A comprehensive comparative performance evaluation with previous feature detection approaches demonstrates that: SFD produces a large number of features with increased scene coverage; detected features are consistent across wide-baseline views for images of a variety of indoor and outdoor scenes; and the number of wide-baseline matches is increased by an order of magnitude compared to alternative detector-descriptor combinations. Sparse scene reconstruction from multiple wide-baseline stereo views using the SFD feature detector demonstrates at least a factor six increase in the number of reconstructed points with reduced error distribution compared to SIFT when evaluated against ground-truth and similar computational cost to SURF/FAST
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