| 1. |
- Fu, Keren, 1988, et al.
(author)
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Adaptive Multi-Level Region Merging for Salient Object Detection
- 2014
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In: British Machine Vision Conference (BMVC) 2014. ; , s. 11 -
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Conference paper (peer-reviewed)abstract
- Most existing salient object detection algorithms face the problem of either under or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph based merging scheme is developed to reassemble regions based on their shared contourstrength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance.
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| 2. |
- Fu, Keren, 1988, et al.
(author)
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Automatic traffic sign recognition based on saliency-enhanced features and SVMs from incrementally built dataset
- 2014
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In: Proceedings of the 3rd International Conference on Connected Vehicles and Expo, ICCVE 2014; Vienna; Austria; 3-7 November 2014. - 9781479967292 ; , s. 947-952
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Conference paper (peer-reviewed)abstract
- This paper proposes an automatic traffic sign recognition method based on saliency-enhanced feature and SVMs. As when human observe a traffic sign, a two-stage procedure is performed by first locating the region of sign according to its unique shape and color, and second paying attention to content inside the sign. The proposed saliency feature extraction attempts to resemble these two processing stages. We model the first stage via extracting salient regions of signs from detected bounding boxes contributed by sign detector. Salient region extraction is formed as an energy propagation process on local structured graph. The second stage is modeled by exploiting a non-linear color mapping under the guidance of the output of the first stage. As results, salient signature inside a sign is popped up and can be directly used by subsequent SVMs for classification. The proposed method is validated on Chinese traffic sign dataset that is incrementally built.
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| 3. |
- Fu, Keren, 1988, et al.
(author)
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Detection and Recognition of Traffic Signs from Videos using Saliency-Enhanced Features
- 2015
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In: Nationell konferens i transportforskning, Oct. 21-22, 2015, Karstans universitet, Sweden.. ; , s. 2-
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Conference paper (peer-reviewed)abstract
- Traffic sign recognition, including sign detection and classification, is an essential part in advanced driver assistance systems and autonomous vehicles. Traffic sign recognition (TSR), that exploits image analysis and computer vision techniques, has drawn increasing interest lately due to recently renewed efforts in vehicle safety and autonomous driving. Applications, among many others, include advanced driver assistance systems, sign inventory, intelligent autonomous driving.We propose efficient methods for detection and classification of traffic signs from automatically cropped street view images. The main novelties in the paper include:• An approach for automatic cropping of street view images from public available websites; The method detects and crops candidate traffic sign regions (bounding boxes) along the roads, from a specified route (i.e., the beginning and end points of the road), instead of conventionally using existing datasets;• An approach for generating saliency-enhanced features for the classifier. A novel method for obtaining the saliency-enhanced regions is proposed. It is based on a propagation process on enhancing sign part that attracts visual attention. Consequently, this leads to salient feature extraction. This approach overcomes the short coming in the conventional methods where features are extracted from the entire region of a detected bounding box which usually contains other clutter (or background).• A coarse-to-fine classification method that first classifies among different sign categories (e.g. categoryof forbidden, warning signs), followed by fine-classification of traffic signs within each category.The proposed methods have been tested on 2 categories of Chinese traffic signs, each containing many different signs.
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| 4. |
- Fu, Keren, 1988, et al.
(author)
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Effective Small Dim Target Detection by Local Connectedness Constraint
- 2014
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479928927 ; , s. 8110-8114
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Conference paper (peer-reviewed)abstract
- The main drawback of conventional filtering based methods for small dim target (SDT) detection is they could not guarantee sufficient suppression ability towards trivial high frequency component which belongs to background, such as strong corners and edges. To overcome this bottleneck, this paper proposes an effective SDT detection algorithm by using local connectedness constraint. Our method provides direct control for target size, ensure high accuracy and could be easily embedded into the classical sliding-window based framework. The effectiveness of the proposed method is validated using images with cluttered background.
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| 5. |
- Fu, Keren, 1988
(author)
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Enhancement of Salient Image Regions for Visual Object Detection
- 2014
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Licentiate thesis (other academic/artistic)abstract
- Salient object/region detection aims at finding interesting regions in images and videos, since such regions contain important information and easily attract human attention. The detected regions can be further used for more complicated computer vision applications such as object detection and recognition, image compression, content-based image editing, and image retrieval. One of the fundamental challenge in salient object detection is to uniformly emphasize desired objects and meanwhile suppress irrelevant background. Existing heuristic color contrast-based methods tend to obtain false detection in complex scenarios and attenuate the inner part of large salient objects. In order to achieve uniform object enhancement and background suppression, several new techniques including color feature integration, graph-based geodesic saliency propagation, hierarchical segmentation based on graph spectrum decomposition are developed in this thesis to assist saliency computation. Paper 1 proposes a superpixel-based salient object detection method which takes advantages of color contrast and distribution. It develops complementary abilities among hypotheses and generates high quality saliency maps. Paper 2 proposes a novel geodesic propagation method for salient region enhancement. It leverages an initial coarse saliency map that highlight potential salient regions, and then conducts geodesic propagation. Local connectivity of objects is retained after the proposed propagation. Papers 3 and 4 use graph-based spectral decomposition for hierarchical segmentation, which enhances saliency detection. As most previous work on salient region detection is done for still images, paper 5 extends graph-based saliency detection methods to video processing. It combines static appearance and motion cues to construct graph. A spatial-temporal smoothing operation is proposed on a structured graph derived from consecutive frames to maintain visual coherence in both inter- and intra- frames. All these proposed methods are validated on benchmark datasets and achieve comparable/better performance to the state-of-the-art methods.
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| 6. |
- Fu, Keren, 1988, et al.
(author)
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Geodesic Distance Transform-based Salient Region Segmentation for Automatic Traffic Sign Recognition
- 2016
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In: Proceedings - 2016 IEEE Intelligent Vehicles Symposium, IV 2016, Gotenburg, Sweden, 19-22 June 2016. - 9781509018215 ; 2016-August, s. 948-953
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Conference paper (peer-reviewed)abstract
- Visual-based traffic sign recognition (TSR) requiresfirst detecting and then classifying signs from capturedimages. In such a cascade system, classification accuracy is often affected by the detection results. This paper proposes a method for extracting a salient region of traffic sign within a detection window for more accurate sign representation and feature extraction, hence enhancing the performance of classification. In the proposed method, a superpixel-based distance map is firstly generated by applying a signed geodesic distance transform from a set of selected foreground and background seeds. An effective method for obtaining a final segmentation from the distancemap is then proposed by incorporating the shape constraints of signs. Using these two steps, our method is able to automatically extract salient sign regions of different shapes. The proposed method is tested and validated in a complete TSR system. Test results show that the proposed method has led to a high classification accuracy (97.11%) on a large dataset containing street images. Comparing to the same TSR system without using saliency-segmented regions, the proposed method has yielded a marked performance improvement (about 12.84%). Future work will be on extending to more traffic sign categories and comparing with other benchmark methods.
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| 7. |
- Fu, Keren, 1988, et al.
(author)
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Geodesic Saliency Propagation for Image Salient Region Detection
- 2013
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In: IEEE Int'l conf. on Image Processing (ICIP 2013), Sept.15-18, Melbourne, Australia. - 9781479923410 ; , s. 3278-3282
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Conference paper (peer-reviewed)abstract
- This paper proposes a novel geodesic saliency propagation method where detected salient objects may be isolated from both the background and other clutters by adding global considerations in the detection process. The method transmits saliency energy from a coarse saliency map to all image parts rather than from image boundaries in conventional cases. The coarse saliency map is computed using the combination of global contrast and Harris convex hull. Superpixels from pre-segmented image are used as pre-processing to further enhance the efficiency. The proposed propagation is geodesic distance assisted and retains the local connectivity of objects. It is capable of rendering a uniform saliency map while suppressing the background, leading to salient objects being popped out. Experiments were conducted on a benchmark dataset, visual comparisons and performance evaluations with eight existing methods have shown that the proposed method is robust and achieves the state-of-the-art performance.
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| 8. |
- Fu, Keren, 1988, et al.
(author)
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Graph Construction for Salient Object Detection in Videos
- 2014
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In: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9781479952083 ; , s. 2371-2376
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Conference paper (peer-reviewed)abstract
- Recently many graph-based salient region/object detection methods have been developed. They are rather effective for still images. However, little attention has been paid to salient region detection in videos. This paper addresses salient region detection in videos. A unified approach towards graph construction for salient object detection in videos is proposed. The proposed method combines static appearance and motion cues to construct graph, enabling a direct extension of original graph based salient region detection to video processing. To maintain coherence in both intra- and inter-frames, a spatial-temporal smoothing operation is proposed on a structured graph derived from consecutive frames. The effectiveness of the proposed method is tested and validated using seven videos from two video datasets.
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| 9. |
- Fu, Keren, 1988, et al.
(author)
-
Learning full-range affinity for diffusion-based saliency detection
- 2016
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In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479999880 ; 2016-May, s. 1926-1930
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Conference paper (peer-reviewed)abstract
- In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperforms 7 recent state-of-the-art saliency models on 3 benchmark datasets.
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| 10. |
- Fu, Keren, 1988, et al.
(author)
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Normalized Cut-based Saliency Detection by Adaptive Multi-Level Region Merging
- 2015
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In: IEEE Transactions on Image Processing. - 1941-0042 .- 1057-7149. ; 24:12, s. 5671-5683
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Journal article (peer-reviewed)abstract
- Existing salient object detection models favor over-segmented regions upon which saliency is computed. Such local regions are less effective on representing object holistically and degrade emphasis of entire salient objects. As a result, existing methods often fail to highlight an entire object in complex background. Towards better grouping of objects and background, in this paper we consider graph cut, more specifically the Normalized graph cut (Ncut) for saliency detection. Since the Ncut partitions a graph in a normalized energy minimization fashion, resulting eigenvectors of the Ncut contain good cluster information that may group visual contents. Motivated by this, we directly induce saliency maps via eigenvectors of the Ncut, contributing to accurate saliency estimation of visual clusters. We implement the Ncut on a graph derived from a moderate number of superpixels. This graph captures both intrinsic color and edge information of image data. Starting from the superpixels, an adaptive multi-level region merging scheme is employed to seek such cluster information from Ncut eigenvectors. With developed saliency measures for each merged region, encouraging performance is obtained after across-level integration. Experiments by comparing with 13 existing methods on four benchmark datasets including MSRA-1000, SOD, SED and CSSD show the proposed method, Ncut saliency (NCS), results in uniform object enhancement and achieves comparable/better performance to the state-of-the-art methods.
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