8 results
Search Results
2. Nonlinear subspace clustering using non-convex Schatten-p norm regularization.
- Author
-
Bai, Yunqing, Pei, Jihong, and Li, Min
- Subjects
- *
HILBERT space , *IMAGE segmentation , *SPARSE approximations , *DATA mapping , *LOW-rank matrices - Abstract
Subspace clustering aims to seek a multi-subspace representation that is best suitable for data points taken from a high-dimensional space. Sparse representation and low-rank approximation-based methods have become one of the main melodies for subspace clustering. In the existing methods, nuclear norm is used to approximate rank minimization. However, the common deficiency still exists for nuclear norm, which always over-penalizes large singular values and results in a biased solution. In this paper, we propose a nonlinear subspace clustering model that exploits sparsity and low-rank of data in high dimensional feature space by using Schatten-p norm surrogate (p ∈ (0 , 1)) with learned low-rank kernel. By this manner, the model guarantees that the data mapped in the high-dimensional feature spaces is lower rank and self-expressive. And we show the alternating direction method of multipliers (abbreviated as ADMM) for the corresponding problem in a reproducing kernel Hilbert space. Various experiments on motion segmentation and image clustering display that the proposed model has potentiality in outperforming most of state-of-the-art models in current literature. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Synaptic clef segmentation method based on fractal dimension for ATUM-SEM image of mouse cortex.
- Author
-
Ma, Chao, Shen, Lijun, Deng, Hao, and Li, Jialin
- Subjects
- *
FRACTAL dimensions , *NERVOUS system , *ELECTRON microscopes , *MICE , *SYNAPSES , *NEURAL circuitry - Abstract
It is well known that neurons communicate through synapses in the nervous system, and the size, morphology, and connectivity of synapses determine the functional properties of the neural network. Therefore, synapses have always been one of the key objects of neuroscience. Due to the technical advance in electron microscope (EM), the physical structure of synapses can be observed at high resolution. Nevertheless, to date, the automatic analysis of the synapse in EM images is still a challenging task. In this paper, we proposed a fractal dimension-based segmentation method for synaptic clef of mouse cortex on EM image stack. Our method does not require a lot of groundtruth to train the model, and shows better adaptive anti-noise performance. That should be ascribed to the stability of segmentation-related key parameters in the data from same tissue. In this way, we only need to give initial values, and then gradually adjust these key parameters. Experiments reveal that our method achieves the desired results, and reduces the time in artificial annotating, so that researchers can focus more on the analysis of segmentation results. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. A novel fast detection method of infrared LSS-Target in complex urban background.
- Author
-
Wu, Yanfeng, Sun, Haijiang, and Liu, Peixun
- Subjects
- *
DATA fusion (Statistics) , *MAXIMUM likelihood statistics , *IMAGE processing , *INFRARED imaging , *SIGNAL-to-noise ratio , *IMAGE segmentation - Abstract
LSS-Target (the Low altitude, Slow speed and Small Target) is likely to be a threat to the observation platform, thus infrared LSS-Target detection is an urgent task. LSS-Target is a challenging issue due to the low Signal-to-Noise Ratio (SNR) and sophisticated background. Motivated by the analysis of infrared imaging characteristics, this paper proposes a novel fusion method for IR LSS-Target detection with complex urban background, which is suitable for precise guidance and self defense. First, an adaptive threshold segmentation based on accumulative histogram and maximum likelihood estimation are utilized to eliminate the clutter and improve SNR of the initial image. Second, a template is set up to identify the seed points in the image. Third, a constrained four criteria region growth algorithm is performed to separate the entire regions. Finally, the confidence measure is constructed, which can eliminate false targets and the background edges. Experimental results show that the method in this paper can screen out the real LSS-Target in real time with high accuracy under sophisticated background. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
5. On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation.
- Author
-
Roy, Apurba and Maity, Santi P.
- Subjects
- *
IMAGE segmentation , *CURVELET transforms , *MAGNETIC resonance imaging , *COMPRESSED sensing , *GENETIC algorithms - Abstract
In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
6. Automatic segmentation and classification of olive fruits batches based on discrete wavelet transform and visual perceptual texture features.
- Author
-
Nashat, Ahmed A. and Hassan, N. M. Hussain
- Subjects
- *
IMAGE processing , *FRUIT quality , *DISCRETE wavelet transforms , *OLIVE oil , *IMAGE segmentation , *COMPUTER algorithms , *FEATURE extraction , *TEXTURE analysis (Image processing) - Abstract
The quality of olive fruit and its virgin olive oil is a main concern for consumers and fruit industrial companies. The effectiveness and fast detection of olive's skin defects is the most decisive factor in determining its quality. It is necessary to design and implement image processing tools for segmentation and correct classification of the different fresh incoming olive batches. In this paper, we propose a new automatic image segmentation algorithm, based on discrete wavelets transform. The aim of the segmentation algorithm is to discriminate between olives and the background with the challenge of irregular and dispersive lesion borders, low contrast, artifacts in the olive fruit and variety of colors within the interest region. The second part of our work proposes a scheme for olive fruit classification. The classifier first identifies the olive fruit color and then, based upon discrete wavelets transform and Tamura statistical texture features, the healthy olive fruit is distinguished from the damaged one. The new texture feature vector is, then, compared with the robust Local Binary Pattern feature vector. The simplicity of our segmentation and classification algorithms makes them appropriate for designing a productive and profitable computer vision machine. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
7. MRI brain lesion segmentation using generalized opposition-based glowworm swarm optimization.
- Author
-
Si, Tapas, De, Arunava, and Bhattacharjee, Anup Kumar
- Subjects
- *
MAGNETIC resonance imaging of the brain , *PARTICLE swarm optimization , *IMAGE segmentation , *K-means clustering , *MACHINE learning - Abstract
An improved glowworm swarm optimization algorithm with generalized opposition-based learning is proposed in this paper and is used in segmentation for magnetic resonance images. Noises are removed and intensity inhomogeneities are corrected in the MR images. Next, a clustering technique with glowworm swarm optimization algorithm with generalized opposition based learning is used. Finally, lesions are separated from the normal tissues of the brain in the post-processing step. The performance of the proposed methodology based on both numerical and visual results are compared with K-means and particle swarm optimization based methodologies over two sets of MR images. The experimental results demonstrate that the proposed methodology statistically outperforms other methodologies. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
8. Brain MRI segmentation for tumor detection via entropy maximization using Grammatical Swarm.
- Author
-
Si, Tapas, De, Arunava, and Bhattacharjee, Anup Kumar
- Subjects
- *
MAGNETIC resonance imaging of the brain , *IMAGE segmentation , *TUMOR diagnosis , *MAXIMUM entropy method , *DISCRETE wavelet transforms ,BRAIN tumor diagnosis - Abstract
This paper presents a new method for the segmentation of Magnetic Resonance Imaging (MRI) of brain tumor. First, discrete wavelet transform (DWT)-based soft-thresholding technique is used for removing noise in the MRI. Second, intensity inhomogeneity (IIH) independent of noise is removed from the MRI image. Third, again DWT is used to sharpen the de-noised and IIH corrected image. In this method, the image is decomposed into first level using wavelet decomposition and approximate values are assigned to zero and reconstruct the image results in detailed image. The detailed image is added with the pre-processed image to produce sharpened image. Entropy maximization using Grammatical Swarm (GS) algorithm is used to obtain a set of threshold values and a threshold value is selected with the expert knowledge to separate the lesion part from the other non-diseased cells in the image. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.