In this paper, we propose a novel metric for image quality assessment based on the ratio of Non-shift Edge (rNSE), whose elegance lies in succinctness and effectiveness. In this metric, an image is filtered by the LOG...
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In this paper, we propose a novel metric for image quality assessment based on the ratio of Non-shift Edge (rNSE), whose elegance lies in succinctness and effectiveness. In this metric, an image is filtered by the LOG operator, who acts like the classical receptive field, and the edge points are detected as the zero-crossings of the filtered image. Then the binary Non-shift Edge (NSE) map is derived to represent the strong edge structure remained in the distorted image. The perceptual quality is calculated by the ratio of NSE. The performance of rNSE in the scale-threshold plane shows similar frequency and threshold selectivity. Comparing with the existing well-designed metrics, the proposed rNSE performs equivalently in accuracy and consistency.
The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape,...
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In this paper, we propose a method for robot self-position identification by active sound localization. This method can be used for autonomous security robots working in room environments. A system using an AIBO robot...
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This paper presents a novel method for natural image understanding. We improved the effect of saliency detection for the purpose of image segmentation at first. Then Graph cuts are used to find global optimal segmenta...
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ISBN:
(纸本)9781457701221
This paper presents a novel method for natural image understanding. We improved the effect of saliency detection for the purpose of image segmentation at first. Then Graph cuts are used to find global optimal segmentation of N-dimensional image. After that, we adopt the scheme of supervised learning to classify the scene type of the image. The main advantages of our method are that: Firstly we revised the existed sparse saliency model to better suit for image segmentation, Secondly we propose a new color modeling method during the process of GrabCut segmentation. Finally we extract object-level top down information and low-level image cues together to distinguish the type of images. Experiments show that our proposed scheme can obtain comparable performance to other approaches.
In this paper, we propose a scatter-glare correction method based on thickness estimation and digital filtration. The method is an improvement of the scatter-glare correction method proposed by Ersahin, et al The meth...
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In this paper, we propose a scatter-glare correction method based on thickness estimation and digital filtration. The method is an improvement of the scatter-glare correction method proposed by Ersahin, et al The method estimates scatter glare fraction in two steps. The first step is to assign an equivalent Lucite thickness to every pixel in the image, and the second step is to estimate the scatter-glare fraction from the thickness information. Experiments show that this method can well estimate the scatter-glare fraction in a wide range of thicknesses, and a higher accuracy for scatter-glare estimation can be obtained by this method compared with Ersahin's method.
SIFT (Scale Invariant Feature Transform) is one of most popular approach for feature detection and matching. Many parallelized algorithms have been proposed to accelerate SIFT to apply into real-time systems. This pap...
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This paper presents a novel method for image segmentation. We improved the effect of saliency detection for the purpose of image segmentation. Graph cuts are used to find global optimal segmentation of N-dimensional i...
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ISBN:
(纸本)9781612847719
This paper presents a novel method for image segmentation. We improved the effect of saliency detection for the purpose of image segmentation. Graph cuts are used to find global optimal segmentation of N-dimensional image. With the guidance of saliency, users do not have to select foreground object and background seeds. The main advantages of our method are that: Firstly we revised the existed sparse saliency model to better suit for image segmentation, Secondly we propose a new color modeling method during the process of GrabCut segmentation. Finally we combine these two processes together to segment images without interference. We demonstrate our proposed scheme for image segmentation on several databases and get satisfactory results.
In this paper we describe modifications of irregular image segmentation pyramids based on user-interaction. We first build a hierarchy of segmentations by the minimum spanning tree based method, then regions from diff...
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In this paper, the subjective image quality for different image content is investigated by psychophysical experiments. The experimental images are the parts from natural scenes distorted by integer transform and quant...
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In this paper, the subjective image quality for different image content is investigated by psychophysical experiments. The experimental images are the parts from natural scenes distorted by integer transform and quantization in H.264 frame. These images are divided into two types based on the scene content, type I and type II. The perceived thresholds and subjective graded scores for different quantization are obtained using forced choice staircase experiments and graded response experiments, respectively. The subjective assessment results showed that the image quality of type I degrades much more than the type II when quantization steps increase, and the preliminary experiment showed that the existed IQA metric, i.e. SSIM, could not predict it well. We also present a content-based image classifier to predict the two image types. The results show good accordance between the classifier and the subjective assessment.
A key problem of image Based Visual Servo (IBVS) System is to track objects in image sequences. Thus, the tracking algorithm plays an important role in improving the efficiency of IBVS systems. In this paper, a novel ...
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ISBN:
(纸本)9781457701221
A key problem of image Based Visual Servo (IBVS) System is to track objects in image sequences. Thus, the tracking algorithm plays an important role in improving the efficiency of IBVS systems. In this paper, a novel tracking algorithm called Modified CamShift Guided Particle Filter (MCAMSGPF) is proposed, which interpolated Speeded-Up Robust Features (SURF) into the framework of conventional CamShift Guided Particle Filter (CAMSGPF) tracking method. This new algorithm outperforms conventional CAMSGPF and other baseline trackers with respect to tracking robustness in the clutter background of similar colors and occlusions. We also proposed a new system model to implement and test the new algorithm in a real time moving IBVS system, which is applied in a mobile robot with an on-board camera.
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