Regions of interest for accurate object detection

Kapsalas, Petros; Rapantzikos, Konstantinos; Sofou, Anastasia; Avrithis, Yannis

doi:10.1109/cbmi.2008.4564940

Cited by 19 publications

(16 citation statements)

References 22 publications

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“…Although the use of keypoints in the detection of ROI has been investigated by Kapsalas et al [3], who used Harris corner detector in identifying objects present in an image, the approach proposed in this paper differs, in that it attempts to achieve effective labelling through the identification of the important object within the image. It also differs from the ROI detection approach proposed by Huang et al [4] since it does not compared the image being processed with other images in the set during the ROI detection, thus reducing the computational requirement.…”

Section: Related Workmentioning

confidence: 99%

“…The identification of interest points present within the space of an image is important in the determination of the image's ROI [3], therefore the method being proposed in this paper maximises the number of the number of interest points detected within a sample image through the use of the combination of FAST corner detector and SURF detector as shown in Figure 2A. The use of several complementary feature detectors in such manner ensures good coverage of the image surface [2].…”

Section: The Proposed Unsupervised Frameworkmentioning

confidence: 99%

“…Common methods of achieving this involves exhaustively sampling different subparts of the image at each location and scale. Such approach has the tendency to become computational expensive and inefficient [2,3]. An efficient alternative is to determine the most important region of the image, commonly regarded as Region of Interest (ROI) [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Region of Intrest Detection Using Fast and Surf

Olaode

Naghdy

Todd

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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The determination of Region-of-Interest has been recognised as an important means by which unimportant image content can be identified and excluded during image compression or image modelling, however existing Region-of-Interest detection methods are computationally expensive thus are mostly unsuitable for managing large number of images and the compression of images especially for real-time video applications. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detection.

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Section: Related Workmentioning

confidence: 99%

Section: The Proposed Unsupervised Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Region of Intrest Detection Using Fast and Surf

Olaode

Naghdy

Todd

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…One idea is to estimate how different a region is than the rest of the image. Studies have been undertaken in this domain [9,10] and we will try to include one that offers a favorable balance between complexity and speed.…”

Section: Future Workmentioning

confidence: 99%

Adaptive Video Transmission Using Residue Octree Cubes

Enache

Boiangiu

2014

Computers

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This paper proposes a method of transmitting video streaming data based on downsampling-upsampling pyramidal decomposition. By implementing an octal tree decomposition of the frame cubes, prior to transforming them into hypercubes, the algorithm manages to increase the granularity of the transmitted data. In this sense, the communication relies on a series of smaller hypercubes, as opposed to a single hypercube containing the entire, undivided frames form a sequence. This translates into increased adaptability to the variations of the transmitting channel's bandwidth.

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“…Corners were considered as a measure of saliency firstly by Schmid and Mohr in 1997 as part of their effort to identify interest points locally (Schmid & Mohr, 1997) (Kapsalas, et al, 2008). In their paper published in 2000, Loupias et al (Loupias, et al, 2000) have criticized the use of corner-based techniques and showed the limitations of using this method.…”

Section: Previous Workmentioning

confidence: 99%

Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

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IntroductionSalient points, objects, or regions extraction is still a challenging topic of research and its definition is still very general. Different literature have defined the saliency in different ways. In general and based on human vision system HVS, the salient object is the object that may capture the attention or attract the HVS. HVS uses two stages to identify the objects, preattentive and attentive stages (Kadir & Brady, 2001).In pre-attentive stage, the local regions of image that present spatial discontinuity are detected. In the attentive stage, relationships between these regions are found, and grouped together. The rest of the paper will be organized as follows: previous work is presented in section 2. In Section 3, we will explain the proposed saliency extraction technique based on the irregularity of the regions in an image. In section 44, a new automatic thresholding technique will be proposed. Points clustering and region merging will be discussed in section 5. Section 6 will review the saliency evaluation technique and introduce a new method that is suitable to the application at hand. In section 7 we will discuss the experimental results and the optimum window size selection. Finally, conclusions will be derived in section 8. 2 Previous WorkWavelet was used by many authors such as in (Loupias, et al., 2000), (Tian, et al., 2001), (Song, et al., 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) In this paper, we shall introduce a robust and general saliency extraction method that is fully automated and does not need any intervention from the user. In addition, it does need any pre-knowledge about the image. Irregularity as a Measure of SaliencyIn this Section, we shall discuss the proposed technique. First, we shall define the saliency as any irregular region in the image with nature differs from the nature of the image as a whole; for example, a region with texture in a uniform region, or vice versa, thus we shall define irregularity as the main measure of saliency.Let ℐ( , ) be the image intensity at position ( , ), where = 1, 2, … . , , = 1,2, … , , with and are the image width and height respectively. The image is represented as a set of pixels and is defined as:Where p xy is the pixel's value at location ( , ) and ℕ is the set of natural numbers. Furthermore, we shall define the set ℙ = { | ⊆ } as the power set of .According to the application at hands, the main constrain is that; all the elements of a given subset should belong to a connected region which means they should be in a given neighbouring area.The sub-image, which we shall refer to as a region, is a subset of the image with elements that belong to the same region. The size of this sub-image is × ℎ.Considering that the regions are disjoint, they can be defined as:Assuming that there is no overlapping between the adjacent regions, the total number of regions is equal to × ℎ . In most cases there should be overlapping between the moving windows, if we denote the overlapping value by , then the total numbers of wind...

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Regions of interest for accurate object detection

Cited by 19 publications

References 22 publications

Unsupervised Region of Intrest Detection Using Fast and Surf

Unsupervised Region of Intrest Detection Using Fast and Surf

Adaptive Video Transmission Using Residue Octree Cubes

Irregularity-based image regions saliency identification and evaluation

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