Frequency-tuned salient region detection

Achanta, Radhakrishna; Hemami, S.S.; Estrada, Francisco José Pozuelos; Süsstrunk, Sabine

doi:10.1109/cvpr.2009.5206596

Cited by 3,190 publications

(1,143 citation statements)

References 23 publications

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“…Figure 3 compares the result obtained from the F-PISA algorithm against FT (frequency-tuned), AC (achanta), HC (histogram-based contrast), and LC (luminance Contrast) algorithms. [19]; (c) AC algorithm [20]; (d) HC algorithm [21]; (e) LC algorithm [22]; and (f) F-PISA algorithm.…”

Section: Generation Of Final Saliency Imagementioning

confidence: 99%

Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images

et al. 2018

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Accurate and timely detection of insulator flashover on power transmission lines is of paramount importance to power utilities. Most available solutions mainly focus on the exploitation of the flashover mechanism or the discharge area detection, rather than the identification of a damaged area due to flashovers using captured aerial images. To this end, this paper proposes a multi-saliency aggregation-based porcelain insulator flashover fault detection approach. The target area of the insulator is determined using the Faster-Pixelwise Image Saliency by Aggregating (F-PISA) algorithm based on the color and structural features. The color model can be established based on the color feature of the damaged areas on the insulator surface, and hence the damaged area can be identified. Based on the information obtained above, the contour information can be extracted. With the preceding process, the fault location can be confirmed with a good accuracy. The performance of the proposed detection approach is assessed through a comparative study with other available solutions. The numerical result demonstrates that the suggested solution can detect the insulator flashover with improved performance in terms of the average detection rate and average efficient detection rate. Additional analysis is carried out to evaluate its robustness and real-time performance, which confirms its deployment feasibility in practice.

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Section: Generation Of Final Saliency Imagementioning

confidence: 99%

Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images

et al. 2018

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“…We compare our algorithm with the state-of-the-art saliency algorithms, e.g., IT [33], HC [36], RC [36], FT [37], CA [38], LC [39], SR [40], DSR [41] and BL [42] in the five types of data set. IT is the most cited algorithm.…”

Section: Saliency Experimentsmentioning

confidence: 99%

“…Cheng et al [37], calculate the histogram contrast of the whole image to obtain the saliency image. It means the wider the distribution of a color in the image, the lower the probability of the saliency region that contains this color.…”

Section: Related Workmentioning

confidence: 99%

Aircraft detection in remote sensing images based on saliency and convolution neural network

Yang

Han

et al. 2018

J Wireless Com Network

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New algorithms and architectures for the current industrial wireless sensor networks shall be explored to ensure the efficiency, robustness, and consistence in variable application environments which concern different issues, such as the smart grid, water supply, and gas monitoring. Object detection automatic in remote sensing images has always been a hot topic. Using the conventional deep convolution network based on region proposal for detection, there are many negative samples in the generated region proposal, which will affect the model detection precision and efficiency. Saliency uses the human visual attention mechanism to achieve the bottom-up object detection. Since replacing the selective search with saliency can greatly reduce the number of proposal areas, we will get some region of interests (RoIs) and their position information by using the saliency algorithm based on the background priori for the remote sensing image. And then, the position information is mapped to the feature vector of the whole image obtained by deep convolution neural network. Finally, the each RoI will be classified and fine-tuned bounding box. In this paper, our model is compared with Fast-RCNN that is the current state-of-the-art detection model. The mAP of our model reaches 99%, which is 12.4% higher than that of Fast-RCNN. In addition, we also study the effect of different iterations on model and find the model of 10,000 iterations already has a higher accuracy. Finally, we compare the results of different number of negative samples and find the detection accuracy is highest when the number of negative samples reaches 400.

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“…Achanta et. al [28] proposed a frequency tuned SD method which overcome the limitations of these existing saliency methods. This SD method is able to generate uniformly highlighted full resolution saliency maps with well-defined boundaries.…”

Section: Saliency Map Extractionmentioning

confidence: 99%

“…At first, Achanta et al proposed a frequency-tuned saliency detection algorithm [28] to utilize almost all low frequency content and most of the high frequency content to obtain perceptually good saliency maps with full resolution. This Saliency map is obtained by taking the Euclidean distance between the average of an image I  and each pixel of the Gaussian blurred version   …”

Section: Saliency Map Extractionmentioning

confidence: 99%

Multi-focus image fusion using maximum symmetric surround saliency detection

Bavirisetti

Dhuli

2016

ELCVIA

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In digital photography, two or more objects of a scene cannot be focused at the same time. If we focus one object, we may lose information about other objects and vice versa. Multi-focus image fusion is a process of generating an all-in-focus image from several out-of-focus images. In this paper, we propose a new multi-focus image fusion method based on two-scale image decomposition and saliency detection using maximum symmetric surround. This method is very beneficial because the saliency map used in this method can highlight the saliency information present in the source images with well defined boundaries. A weight map construction method based on saliency information is developed in this paper. This weight map can identify the focus and defocus regions present in the image very well. So we implemented a new fusion algorithm based on weight map which integrate only focused region information into the fused image. Unlike multi-scale image fusion methods, in this method two-scale image decomposition is sufficient. So, it is computationally efficient. Proposed method is tested on several multi-focus image datasets and it is compared with traditional and recently proposed fusion methods using various fusion metrics. Results justify that our proposed method gives stable and promising performance when compared to that of the existing methods.

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Frequency-tuned salient region detection

Cited by 3,190 publications

References 23 publications

Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images

Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images

Aircraft detection in remote sensing images based on saliency and convolution neural network

Multi-focus image fusion using maximum symmetric surround saliency detection

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