Proposed Image Segmentation Algorithm Research Articles

Image Segmentation with Multi-feature Fusion in Compressed Domain based on Region-Based Graph

Image segmentation plays a significant role in image processing and scientific research. In this paper, we develop a novel approach, which provides effective and robust performances for image segmentation based on the region-based (block-based) graph instead of pixel-based graph. The modified Discrete Cosine Transform (DCT) is applied to obtain the Square Block Structures (DCT-SBS) of the image in the compressed domain together with the coefficients, due to its low memory requirement and high processing efficiency on extracting the block feature. A novel weight computation approach focusing on multi-feature fusion from the location, texture and RGB-color information is employed to efficiently obtain weights between the DCT-SBS. The energy function is redesigned to meet the region-based requirement and can be easily transformed into the traditional Normalized cuts (Ncuts). The proposed image segmentation algorithm is applied to the salient region detection database and Corel1000 database. The performance results are compared with the state-of-the-art segmentation algorithms. Experimental results clearly show that our method outperforms other algorithms, and demonstrate good segmentation precision and high efficiency.

PHISS: Progressive and hierarchical image segmentation-sharing scheme

As a classical secret sharing technique, (k,n) threshold secret image sharing mainly contains the shadow generation phase and the image reconstruction phase. The sensitive secret image is encrypted into n shadows by the dealer in the shadow generation phase, and the secret image can be reconstructed completely by the participants when the number of shadows meet or exceed the threshold k. Previous progressive or hierarchical secret image sharing schemes have some disadvantages (e.g., inflexibility and inefficiency). To solve this problem, we propose a progressive and hierarchical image segmentation-sharing scheme (referred to as PHISS) in this paper. First, the important content region of the sensitive image (called by the target sub-image) is generated by using the proposed image segmentation algorithm. Here the target sub-image refers to the part with the largest amount of information, for instance, the human face in private photo, the pathological region in medical image and the aircraft in military image. Then, in the shadow generation phase, the target sub-image is encrypted into several shadows with hierarchy, and the hierarchies of the generated shadows in the background sub-image are equal. Last, in the reconstruction phase, the background sub-image can be reconstructed completely. The target sub-image has different quality with different number of shadows and different level of shadows. The image can be reconstructed losslessly if and only if all shadows involved. The experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed method.

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture.

Inner Mongolia is rich in grassland tourism resources, and the development of grassland tourism is of great significance to Inner Mongolia tourism and promotion of grassland protection. To better promote the grassland tourism of the Silk Road culture, the Conditional Global Area Network (CGAN) and Morphology Connected Component Chan-Vase (MCC-CV) algorithm are used to enhance and segment the traditional embroidery patterns in Inner Mongolia. Firstly, the generative adversarial network (GAN) is optimized, and a new GAN is proposed with the feature vector extracted from the convolutional neural network (CNN) as the constraint condition. Secondly, the automatic segmentation algorithm of embroidery based on the MCC-CV model is proposed, and finally, the proposed algorithm is tested. The test results demonstrate that after 8000 iterations of the proposed image-enhancement algorithm, its personalized features are enhanced, and the segmentation accuracy of the proposed image segmentation algorithm is 60%. The proposed algorithm provides some ideas for the application of deep learning (DL) technology in the grassland tourism of the Silk Road culture and also helps operators to accurately grasp the market and make tourists more comfortable and pleasant.

Bone Region Segmentation in Medical Images Based on Improved Watershed Algorithm.

Watershed algorithm is widely used in image segmentation, but it has oversegmentation in image segmentation. Therefore, an image segmentation algorithm based on K-means and improved watershed algorithm is proposed. Firstly, Gaussian filter is used to denoise human skeleton image. K-means clustering algorithm is used to segment the denoised image and the connected component with the largest area is extracted as the initial human skeleton region. The initial bone region was morphologically opened and then morphologically closed to eliminate the noise. Morphologically open operation is used to disconnect other human tissues that adhere to the human bone region and eliminate the background noise with small area, while closed operation smoothes the edge of the human bone region and fills the fracture in the contour line. Secondly, the watershed segmentation algorithm is implemented on the image after morphological operation. The similarity degree of two blocks is defined according to the mean difference of gray level of adjacent blocks and the mean value of standard deviation of gray level of pixels in the edge of the block 4-neighborhood. The adaptive threshold T is generated by Otsu method for histogram of gradient amplitude image. If the similarity degree is greater than T, the image blocks will be merged; otherwise, the image blocks will not be merged. The proposed image segmentation algorithm is used to extract and segment the human bone region from 100 medical images containing human bone. The number of blocks segmented by watershed algorithm is 2775 to 3357, but the number of blocks segmented by the proposed algorithm is 221 to 559. The experimental results show that the proposed algorithm effectively solves the oversegmentation problem of watershed algorithm and effectively segments the image target.

Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection

The baseline approaches on satellite image segmentation problem suffer from issues like sensitivity towards initialization, local optima solutions, a predefined number of output clusters, single-objective optimization, etc. To solve these challenges, this study proposes a unique pixel-level Multi-Spectral (MS) very high resolution (VHR) image segmentation algorithm based on variable-length multi-objective genetic clustering. We propose a new approach to update solutions by retaining variable length property throughout the optimization process. The resulting clustering algorithm contains a set of near-Pareto-optimal solutions. A map that has a scale of less than 1/10000 is called a large-scale map. We propose a large-scale change detection technique as an application of the proposed image segmentation algorithm. Solving Land-use/Land-Cover (LULC) change detection problems in a congested area is a complex task. This study considers the dataset from Pleiades-HR 1B, and Landsat 5 TM sensors in the experimental study. An extensive quantitative and qualitative analysis is performed to validate the superior performance of the proposed method with different state-of-the-art techniques.

Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system

ABSTRACT In the poultry slaughtering, accurate viscera positioning is essential to reduce the damage of internal organs. The introduction of machine vision technique can help to locate the viscera and can provide new direction for the poultry evisceration. After a midline abdominal incision of the poultry, the internal organs are taken out from the poultry that placed on the conveyor line by the parallel robot. Based on machine vision, the recognition accuracy of opened poultry viscera directly affects the level of visceral damage and residue caused by gripping manipulator. However, visceral positioning is often influenced by different noise in the abdominal cavity, such as mucous membranes and blood stains. Thus, the image segmentation of poultry viscera is a complex process, and it is challenging to remove the noise. In general, existing image segmentation methods can hardly segment visceral regions well. To strengthen the anti-noise ability, we proposed an improved region-based active contour method with the level-set formulation. This method combined with several operations of color space conversion and top-low-hat transformation, which could extract the viscera contour and precisely removed the noise. The results showed that recognition accuracy of the heart-liver area and fat area in the viscera are 98.98% and 99.75%, respectively, while the overall viscera for poultry was 98.96%. The results of this experiment suggested that the proposed image segmentation algorithm could achieve the required accuracy for poultry viscera detection. Thus, the proposed visceral contour recognition method could be applied in poultry processing, providing critical information to guide the robot for automated evisceration.

Research on Effect of Equivalent Diameter of Voids on Sound Absorption Performance of Porous Asphalt Concrete Based on Grey Entropy Method

The sound absorption performance of porous asphalt concrete (PAC) is inseparable from the sizes of voids, as different sizes of voids have different absorption effects on noise in different frequency bands. However, the relationship between the two is not clear. In this study, the equivalent diameter of voids was obtained by the proposed image segmentation algorithm based on the square area, then grey entropy method was used to analyze the effect of different equivalent diameter of voids on the sound absorption performance of PAC in the frequency range of traffic noise. The results show that with the increase of air voids, the peak and average sound absorption coefficient of PAC increase, the sound absorption performance of PAC is improved; and the sound absorption performance of PAC is mainly affected by the equivalent diameter of voids of 3-4mm.

Performance comparison of segmentation algorithms for hand gesture recognition

The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.

Dynamic Biomedical Image Segmentation Based on Wavelet Transform

In this paper, the image segmentation algorithm based on wavelet transform is presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Dynamic Image Segmentation for Sport Graphics Based on Wavelet Transform

In this paper, a image segmentation algorithm based on wavelet transform are presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Performance Evaluation and Comparative Analysis of Proposed Image Segmentation Algorithm

In this paper, a novel approach of K-Region based Clustering image segmentation algorithm has been proposed. The proposed algorithm divides an image of size N × N into K number of regions. The K and N are multiples of 2. The value of K must be less than N. Authors divided the image into 4, 16, 64, 256, 1024, 4096 and 16384 regions, based on the value of K. The adjacent pixels having similar intensity value in each region are grouped into same clusters. Further, the clusters of similar values in each adjacent region are grouped together to form the bigger clusters. The different segmented images have been obtained based on the K number of regions. The four parameters, namely, Probabilistic Rand Index (PRI), Variation of Information (VOI), Global Consistency Error (GCE) and Boundary Displacement Error (BDE) have been used to evaluate the performance of the proposed algorithm. The performance of proposed algorithm was evaluated using 100 images taken from Berkeley image database. The time-complexity of the proposed algorithm has also been calculated. The comparative analysis of proposed algorithm was made with existing image segmentation algorithm, namely, K-mean clustering and Region-growing algorithm. Significant results were obtained in case of proposed algorithm when\the PRI, VOI, GCE and BDE values were compared with those of existing algorithms. MATLAB 7.4 has been used to implement the proposed algorithm.

An unconditionally stable numerical method for bimodal image segmentation

In this paper, we propose a new level set-based model and an unconditionally stable numerical method for bimodal image segmentation. Our model is based on the Lee–Seo active contour model. The numerical scheme is semi-implicit and solved by an analytical method. The unconditional stability of the proposed numerical method is proved analytically. We demonstrate performance of the proposed image segmentation algorithm on several synthetic and real images to confirm the efficiency and stability of the proposed method.

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.

Image segmentation based on Markov random fields with adaptive neighborhood systems

We present a new image segmentation algorithm based on Markov random fields (MRF) with adaptive neighborhood (AN) systems. First, a new criterion function is developed to adaptively select the neighborhood system of the MRF. Second, thanks to the appropriate format of the new criterion function, a general iterated conditional mode (GICM) is deduced to incorporate the AN selection into the inference process in the segmentation of images. Third, an end points detection step is introduced to preserve the ends of line structures in the images. The proposed algorithm has the following advantages over previous works: the convergence of the new algorithm is much faster than classical algorithms; the AN configuration of each site is iteratively optimized in the inference process, which leads to more accurate segmentation results; and no extra knowledge is needed in the AN selection. Numerical experiments on a wide range of images demonstrate that the proposed image segmentation algorithm performs better, in terms of speed and detail preserving, than the algorithms based on classical MRFs.

Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames

In this paper, a novel image segmentation and a robust unsupervised video objects tracking algorithm are proposed. The proposed method is able to track complete object regions in a sequence of video frames. In this work, object tracking is achieved by analysing the movement of the contours with frame by frame in the video stream. The proposed algorithm involves with three major components for analysing the shapes and motions of the object in the video frames. First, a modified mathematical morphology edge detection algorithm is utilized to extract the contour features in the video frames. Then, a contour-based image segmentation algorithm is proposed and applied to the contour features for partitioning the predetermined target objects in the video frames. Finally, a trajectory estimation scheme is developed to handle the movements of the objects in the video frames. The proposed image segmentation algorithm is capable of automatically partitioning the predetermined objects. The proposed tracking algorithm is also robust against overlapping and videos acquired by non-stationary cameras. The experimental results show that the proposed algorithm can precisely partition and track the predetermined objects in video frames.