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Proceedings ArticleDOI

Segmentation of camera-trap tiger images based on texture and color features

TL;DR: A combination of texture and color features are used to discriminate the tiger from the background and a level set based active contour segmentation is performed to obtain the region of interest in a camera-trap tiger image.
Abstract: In this paper we propose a method to obtain the region of interest in a camera-trap tiger image so that it can be used for identification of the tiger in later stage. We use a combination of texture and color features to discriminate the tiger from the background. Based on these features, a level set based active contour segmentation is performed. A series of morphological operation are then performed on the segmented result to obtain the desired region of interest. The experimental results show the effectiveness of the proposed algorithm.
Citations
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Journal ArticleDOI
TL;DR: This paper is the first work proposing multi-layer robust principal component analysis (multi-layer RPCA) and using it for camera-trap images segmentation and compared it with some state-of-the-art algorithms of background subtraction, where it outperformed these other methods.
Abstract: The segmentation of animals from camera-trap images is a difficult task. To illustrate, there are various challenges due to environmental conditions and hardware limitation in these images. We proposed a multi-layer robust principal component analysis (multi-layer RPCA) approach for background subtraction. Our method computes sparse and low-rank images from a weighted sum of descriptors, using color and texture features as case of study for camera-trap images segmentation. The segmentation algorithm is composed of histogram equalization or Gaussian filtering as pre-processing, and morphological filters with active contour as post-processing. The parameters of our multi-layer RPCA were optimized with an exhaustive search. The database consists of camera-trap images from the Colombian forest taken by the Instituto de Investigacion de Recursos Biologicos Alexander von Humboldt. We analyzed the performance of our method in inherent and therefore challenging situations of camera-trap images. Furthermore, we compared our method with some state-of-the-art algorithms of background subtraction, where our multi-layer RPCA outperformed these other methods. Our multi-layer RPCA reached 76.17 and 69.97% of average fine-grained F-measure for color and infrared sequences, respectively. To our best knowledge, this paper is the first work proposing multi-layer RPCA and using it for camera-trap images segmentation.

31 citations


Cites methods from "Segmentation of camera-trap tiger i..."

  • ...Reddy and Aravind proposed a method to segment tigers on camera-trap images, using texture and color features with active contours [6]....

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Proceedings ArticleDOI
19 Aug 2016
TL;DR: This research introduces techniques that find motion in camera trap images that are robust to changes in illumination due to time of day or the presence of camera flash.
Abstract: Camera trapping is used by conservation biologists to study snow leopards. In this research, we introduce techniques that find motion in camera trap images. Images are grouped into sets and a common background image is computed for each set. The background and superpixel-based features are then used to segment each image into objects that correspond to motion. The proposed methods are robust to changes in illumination due to time of day or the presence of camera flash.

23 citations


Cites background from "Segmentation of camera-trap tiger i..."

  • ...Their range of more than 2 million km2 spans 12 countries in Central Asia....

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Journal ArticleDOI
TL;DR: In this article, a multi-layer robust principal component analysis (multi-layer RPCA) approach was proposed for background subtraction of camera-trap images from the Colombian forest taken by the Instituto de Investigación de Recursos Biologicos Alexander von Humboldt.
Abstract: The segmentation of animals from camera-trap images is a difficult task. To illustrate, there are various challenges due to environmental conditions and hardware limitation in these images. We proposed a multi-layer robust principal component analysis (multi-layer RPCA) approach for background subtraction. Our method computes sparse and low-rank images from a weighted sum of descriptors, using color and texture features as case of study for camera-trap images segmentation. The segmentation algorithm is composed of histogram equalization or Gaussian filtering as pre-processing, and morphological filters with active contour as post-processing. The parameters of our multi-layer RPCA were optimized with an exhaustive search. The database consists of camera-trap images from the Colombian forest taken by the Instituto de Investigaci\'on de Recursos Biol\'ogicos Alexander von Humboldt. We analyzed the performance of our method in inherent and therefore challenging situations of camera-trap images. Furthermore, we compared our method with some state-of-the-art algorithms of background subtraction, where our multi-layer RPCA outperformed these other methods. Our multi-layer RPCA reached 76.17 and 69.97% of average fine-grained F-measure for color and infrared sequences, respectively. To our best knowledge, this paper is the first work proposing multi-layer RPCA and using it for camera-trap images segmentation.

10 citations

References
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Journal ArticleDOI
TL;DR: The PSC algorithm as mentioned in this paper approximates the Hamilton-Jacobi equations with parabolic right-hand-sides by using techniques from the hyperbolic conservation laws, which can be used also for more general surface motion problems.

13,020 citations

Journal ArticleDOI
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >

12,560 citations


"Segmentation of camera-trap tiger i..." refers background in this paper

  • ...To overcome this problem nonlinear diffusion is suggested in [9]....

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Journal ArticleDOI
TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.
Abstract: We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.

10,404 citations


"Segmentation of camera-trap tiger i..." refers background or methods in this paper

  • ...The technique is able to differentiate between the tiger and the ground better due to the texture features, whereas the segmentation like the one proposed in [15] which is based on only color gives poor result since the color of the tiger and the ground are very similar....

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  • ...Using the same finite difference equations defined earlier, the discrete version [15] of the equation (8) can be written as,...

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  • ...Top row: original image, second row: segmentation by method proposed in [15], third row: segmented result using texture and color features and last row: segmentation after applying morphological operations...

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Journal ArticleDOI
TL;DR: The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations.
Abstract: For the purposes of object or defect identification required in industrial vision applications, the operations of mathematical morphology are more useful than the convolution operations employed in signal processing because the morphological operators relate directly to shape. The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations. Examples are given for each morphological concept and explanations are given for many of their interrelationships.

2,676 citations


"Segmentation of camera-trap tiger i..." refers background in this paper

  • ...Readers are referred to [16] for more details....

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Journal ArticleDOI
TL;DR: It is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied.
Abstract: In practice the relevant details of images exist only over a restricted range of scale. Hence it is important to study the dependence of image structure on the level of resolution. It seems clear enough that visual perception treats images on several levels of resolution simultaneously and that this fact must be important for the study of perception. However, no applicable mathematically formulated theory to deal with such problems appears to exist. In this paper it is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied. The structure of this family is governed by the well known diffusion equation (a parabolic, linear, partial differential equation of the second order). As such the structure fits into existing theories that treat the front end of the visual system as a continuous stack of homogeneous layers, characterized by iterated local processing schemes. When resolution is decreased the images becomes less articulated because the extrem ("light and dark blobs") disappear one after the other. This erosion of structure is a simple process that is similar in every case. As a result any image can be described as a juxtaposed and nested set of light and dark blobs, wherein each blob has a limited range of resolution in which it manifests itself. The structure of the family of derived images permits a derivation of the sampling density required to sample the image at multiple scales of resolution.(ABSTRACT TRUNCATED AT 250 WORDS)

2,641 citations


"Segmentation of camera-trap tiger i..." refers background in this paper

  • ...The basic idea of diffusion in image processing comes from the convolution of an image with Gaussian filter [8]....

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