Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-u...Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.展开更多
Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applicatio...Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.展开更多
The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salie...The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salient (which usually means important and interesting) objects in video segments.Unlike existing approaches,we propose a method that combines the saliency measurement with spatial and temporal coherence.The integration of spatial and temporal coherence is inspired by the focused attention in human vision.In the proposed method,the spatial coherence of low-level visual grouping cues (e.g.appearance and motion) helps per-frame object-background separation,while the temporal coherence of the object properties (e.g.shape and appearance) ensures consistent object localization over time,and thus the method is robust to unexpected environment changes and camera vibrations.Having developed an efficient optimization strategy based on coarse-to-fine multi-scale dynamic programming,we evaluate our method using a challenging dataset that is freely available together with this paper.We show the effectiveness and complementariness of the two types of coherence,and demonstrate that they can significantly improve the performance of salient object detection in videos.展开更多
基金supported by National Basic Research Program of China (973 Program) (No. 2006CB300407)National Natural Science Foundation of China (No. 50775017)
文摘Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.
文摘Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.
基金supported by the National Natural Science Foundation of China(60635050 and 90820017)the National Basic Research Program of China(2007CB311005)
文摘The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salient (which usually means important and interesting) objects in video segments.Unlike existing approaches,we propose a method that combines the saliency measurement with spatial and temporal coherence.The integration of spatial and temporal coherence is inspired by the focused attention in human vision.In the proposed method,the spatial coherence of low-level visual grouping cues (e.g.appearance and motion) helps per-frame object-background separation,while the temporal coherence of the object properties (e.g.shape and appearance) ensures consistent object localization over time,and thus the method is robust to unexpected environment changes and camera vibrations.Having developed an efficient optimization strategy based on coarse-to-fine multi-scale dynamic programming,we evaluate our method using a challenging dataset that is freely available together with this paper.We show the effectiveness and complementariness of the two types of coherence,and demonstrate that they can significantly improve the performance of salient object detection in videos.
