Journal articles: 'Histogram and partition based filter'

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Yang, Si, Lihua Zheng, Wanlin Gao, Bingbing Wang, Xia Hao, Jiaqi Mi, and Minjuan Wang. "An Efficient Processing Approach for Colored Point Cloud-Based High-Throughput Seedling Phenotyping." Remote Sensing 12, no. 10 (May 12, 2020): 1540. http://dx.doi.org/10.3390/rs12101540.

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Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.

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Priya, Sarv, Amit Agarwal, Caitlin Ward, Thomas Locke, Varun Monga, and Girish Bathla. "Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models." Neuroradiology Journal 34, no. 4 (February 3, 2021): 355–62. http://dx.doi.org/10.1177/1971400921990766.

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Objective Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. Methods We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Results The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. Conclusions First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

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Ausiannikau, A. V., and V. M. Kozel. "Filtration of histogram evaluation of probability density based on fuzzy data accessibility to a grouping interval." Doklady BGUIR 19, no. 4 (July 1, 2021): 13–20. http://dx.doi.org/10.35596/1729-7648-2021-19-4-13-20.

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The paper proposes a histogram estimate of the probability density based on fuzzy data belonging to a grouping interval. A methodology for constructing a histogram estimate using a histogram smoothing filter is presented. The technique of constructing such a filter is described. The main filter parameter is established – the coefficient of the statistical relationship between the amount of data falling into the grouping interval for a single inclusion function and when approaching to use the membership function. The use of an iterative procedure for a histogram filter allows for a greater “smoothness” of the histogram. The simulation results show the effectiveness of using a histogram filter for different data volumes. At the same time, the choice of the number of grouping intervals for the “correct” recognition of probability density becomes not critical. The histogram filter is a simple tool that can easily be built into any algorithm for constructing histogram estimates.

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Loquin, Kevin, and Olivier Strauss. "Histogram density estimators based upon a fuzzy partition." Statistics & Probability Letters 78, no. 13 (September 2008): 1863–68. http://dx.doi.org/10.1016/j.spl.2008.01.053.

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GUO, Hong-wei, Jiang YU, Jia-xing ZHU, and Zhi-yong LI. "Weighted mean filter based on local histogram." Journal of Computer Applications 30, no. 11 (December 14, 2010): 3019–21. http://dx.doi.org/10.3724/sp.j.1087.2010.03019.

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Wang, Baoping, Jiulun Fan, Weixin Xie, and Chengmao Wu. "Adaptive histogram-based filter for image restoration." Journal of Electronics (China) 21, no. 4 (July 2004): 306–13. http://dx.doi.org/10.1007/bf02687886.

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Jung-Hua Wang, Wen-Jeng Liu, and Lian-Da Lin. "Histogram-based fuzzy filter for image restoration." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 2 (April 2002): 230–38. http://dx.doi.org/10.1109/3477.990880.

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Dai, Ying Meng, Lin Feng Wei, and Cong Luo. "Image Retrieval Method Based on Vision Feature of Color." Applied Mechanics and Materials 303-306 (February 2013): 1406–11. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1406.

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Color histogram is an important technique for color database retrieving, but it often ignores color’s spatial distribution information. This paper proposes an improved color histogram algorithm based on the HSV space, whose subspaces are non-equally quantized. The algorithm first proceeds annular partition on the original image, and then uses the method presented by Aibing Rao etc. [1] to count each partition. At last, it calculates the weighted sums for the distances between distinct color histograms. Experimental results demonstrate that the algorithm reduces the feature dimensions and keeps a good accuracy as well as the spatial distribution information. Thus, a better retrieval result is obtained.

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Pan, Shengdong, Xiangjing An, and Hangen He. "OptimalO(1) Bilateral Filter with Arbitrary Spatial and Range Kernels Using Sparse Approximation." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/289517.

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A number of acceleration schemes for speeding up the time-consuming bilateral filter have been proposed in the literature. Among these techniques, the histogram-based bilateral filter trades the flexibility for achievingO(1) computational complexity using box spatial kernel. A recent study shows that this technique can be leveraged forO(1) bilateral filter with arbitrary spatial and range kernels by linearly combining the results of multiple-box bilateral filters. However, this method requires many box bilateral filters to obtain sufficient accuracy when approximating the bilateral filter with a large spatial kernel. In this paper, we propose approximating arbitrary spatial kernel using a fixed number of boxes. It turns out that the multiple-box spatial kernel can be applied in manyO(1) acceleration schemes in addition to the histogram-based one. Experiments on the application to the histogram-based acceleration are presented in this paper. Results show that the proposed method has better accuracy in approximating the bilateral filter with Gaussian spatial kernel, compared with the previous histogram-based methods. Furthermore, the performance of the proposed histogram-based bilateral filter is robust with respect to the parameters of the filter kernel.

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Schulte, Stefan, Valérie De Witte, Mike Nachtegael, Dietrich Van der Weken, and Etienne E. Kerre. "Histogram-based fuzzy colour filter for image restoration." Image and Vision Computing 25, no. 9 (September 2007): 1377–90. http://dx.doi.org/10.1016/j.imavis.2006.10.002.

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Xiao, Xiao, De Wen Zhuang, and Shou Jue Wang. "Content-Based Image Retrieval through Region Uniformly Partition." Key Engineering Materials 500 (January 2012): 471–74. http://dx.doi.org/10.4028/www.scientific.net/kem.500.471.

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It has been demonstrated that accurate image segmentation is still an open problem. For avoiding this difficulties in content-based image retrieval, an region uniform partition approaching was proposed. Based on fusing regional color features using smooth slide histogram and texture features extracted using Gabor wavelet, we provided the corresponding similarity measure. The image retrieval performance on a subset of the COREL database are better than SIMPLIcity system showed the effectiveness of the proposed method.

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Yuan, Bao Hong, De Xiang Zhang, Kui Fu, and Ling Jun Zhang. "Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter." Applied Mechanics and Materials 380-384 (August 2013): 3672–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3672.

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In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.

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Wen, Zhi Qiang, Yan Hui Zhu, and Zhao Yi Peng. "Study on Particle Filter Object Tracking Based on Weighted Fusion." Advanced Materials Research 403-408 (November 2011): 3049–53. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3049.

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For the lack of self-adaptivity to environments, a measurement model based on weighted information fusion is presented in particle filter. Combined with color information and movement information of object, color histogram and motion Information histogram are built respectively, and then present a weighted linear model as the measurement model. The center-around method is adopted to compute the weight in the linear model. Lastly, a mass of experiments show the presented method be effective.

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Zeng, Fei, Qing Wu, and Jun Du. "Foggy Image Enhancement Based on Filter Variable Multi-Scale Retinex." Applied Mechanics and Materials 505-506 (January 2014): 1041–45. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1041.

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In foggy weather, contrast and color of images captured by traffic visual surveillance system are degraded significantly. To improve contrast and enlarge gray range of fog-degraded image, an improved filter variable multi-scale Retinex algorithm is presented. From comparing simulation resuts by four algorithms (global histogram equalization, local histogram equalization, multiscale retinex respectively variable filter retinex), the subjective and objective evaluation is summarized respectively. Experimental results show that the method has good universality and practicality to enhance local texture details of foggy image and suppress noise effectively.

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PARK, Hanhoon. "Making Joint-Histogram-Based Weighted Median Filter Much Faster." IEICE Transactions on Information and Systems E98.D, no. 3 (2015): 721–25. http://dx.doi.org/10.1587/transinf.2014edl8144.

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Dong, En Zeng, Li Ya Su, and Yan Hong Fu. "An Improved Tracking Algorithm Combing Color and LBP Texture Features Based on Particle Filter." Applied Mechanics and Materials 385-386 (August 2013): 1484–87. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1484.

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In this paper, an tracking algorithm combing color and LBP texture features based on particle filter is proposed to overcome the disadvantages of existing particle filter object tracking methods. A color histogram and a texture histogram were combined to build the objects reference model, effectively improving the accuracy of object tracking. Experimental results demonstrate that, compared with the method based on single feature, the proposed method is highly effective, valid and is practicable.

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Guo, Zhi Hui, Yuan Bao Gu, and Hong Tao Yao. "Auto-Exposure Algorithm Based on Luminance Histogram and Region Segmentation." Applied Mechanics and Materials 543-547 (March 2014): 2278–82. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2278.

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This paper proposed an automatic exposure algorithm based on luminance histogram and region segmentation. The method ensures well exposure to the main object under the backlighting or excessive front-lighting conditions, and uses the characteristics of the luminance histogram to judge the scene condition. According to the luminance histogram, if the image under normal light conditions, the method compares the average luminance value of the image with the corresponding reference target brightness value to adjust exposure compensation. If the image under special conditions, the average brightness value will be calculated using fixed-partition method and exposure compensation will be adjusted. The paper combines auto-exposure and auto-gain control to adjust the exposure compensation. As it turns out, this method in a variety of light conditions can achieve well exposure.

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Liu, Tuanning, Yuanping Zhou, and Rongzhen Miao. "Reduced-Dimension Wiener Filter Based on Perpendicular Partition." IEEE Access 7 (2019): 107917–26. http://dx.doi.org/10.1109/access.2019.2933139.

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Tang, Xuri, Mai Jiang, Yu Ping Wang, and Zhi Gang Pi. "Ceramic Tile Color Difference Classification System Based on Color Histogram." Advanced Materials Research 662 (February 2013): 926–30. http://dx.doi.org/10.4028/www.scientific.net/amr.662.926.

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According to the ceramic tile color difference classification detection problem, this paper presents a method for color difference based on Histogram statistical values. First, the color image in RGB color space is converted to HSI color space, median filter was selected for image preprocessing. Then, the ceramic samples HSI Histogram statistical of each channel was calculated respectively. Take the Histogram statistical as the color difference classification character value. For real timerequirement, using minimum distance classifier as classification basis. Compared with the S, I channel, the results showed that adopted the H channel Histogram statistical value as feature vector has higher accuracy for ceramic tile color difference classification.

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Lin, Tzu-Chao, Chao-Ming Lin, Mu-Kun Liu, and Chien-Ting Yeh. "Partition-based fuzzy median filter based on adaptive resonance theory." Computer Standards & Interfaces 36, no. 3 (March 2014): 631–40. http://dx.doi.org/10.1016/j.csi.2013.09.002.

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Jing, Junfeng, Ru Ren, Pengfei Li, and Minqi Li. "Statistical classification for E-glass fiber fabric defects based on sparse coding." Journal of Engineered Fibers and Fabrics 14 (January 2019): 155892501984598. http://dx.doi.org/10.1177/1558925019845985.

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In this research, a statistical classification algorithm based on sparse coding is presented to classify the defects on E-glass fiber fabrics adaptively. First, all images are preprocessed by being convolved with the MR8 filter banks to obtain the filter responses. For the filter response space of each type of image, we will learn a Class-specific dictionary, and all the Class-specific dictionaries are concatenated to form a complete dictionary. Then, the reconstructed contribution rate of each atom of the complete dictionary to the image filter response is counted to obtain two types of histogram features of each image. Finally, the improved sparse representation classification is used to classify test defect images based on the histogram features. The proposed adaptive classification method has achieved an average classification accuracy of 96.67% on the dataset collected onsite. The results validate the superiority of the proposed method to E-glass fiber fabrics.

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Tian, Ruijie, Tiansheng Chen, Huawei Zhai, Weishi Zhang, and Fei Wang. "A Method for Solving Approximate Partition Boundaries of Spatial Big Data Based on Histogram Bucket Sampling." Symmetry 14, no. 5 (May 20, 2022): 1055. http://dx.doi.org/10.3390/sym14051055.

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In recent years, the volume of spatial data has rapidly grown, so it is crucial to process them in an efficient manner. The level of parallel processing in big data platforms such as Hadoop and Spark is determined by partitioning the dataset. A common approach is to split the data into chunks based on the number of bytes. While this approach works well for text-based batch processing, in many cases, it is preferable to take advantage of the structured information contained in the dataset (e.g., spatial coordinates) to plan data partitioning. In view of the huge amount of data and the impossibility of quickly establishing partitions, this paper designs a method for approximate partition boundary solving, which divides the data space into multiple non-overlapping symmetric bins and samples each bin, making the probability density of the sampling set bounded by the deviation of the probability density of the original data. The sampling set is read into the memory at one time for calculation, and the established partition boundary satisfies the partition threshold-setting. Only a few boundary adjustment operations are required, which greatly shortens the partition time. In this paper, the method proposed in the paper is tested on the synthetic dataset, the bus trajectory dataset, and six common spatial partitioning methods (Grid, Z-curve, H-curve, STR, Kd-tree, and R*-Grove) are selected for comparison. The results show that the symmetric bin sampling method can describe the spatial data distribution well and can be directly used for partition boundary division.

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Dangguo Shao, Paul Liu, and Dong C. Liu. "Histogram-Based Fast Adaptive Bilateral Filter for Ultrasound Speckle Reduction." International Journal of Digital Content Technology and its Applications 6, no. 23 (December 31, 2012): 298–305. http://dx.doi.org/10.4156/jdcta.vol6.issue23.34.

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Fan, Haiju. "SAR Image Despeckling Based on Adaptive PDE Filter and Histogram." Journal of Information and Computational Science 11, no. 7 (May 1, 2014): 2283–90. http://dx.doi.org/10.12733/jics20103353.

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Luan, Zeng, Zhai You, and Xiong Wei. "Improved SURF Descriptor Based on Triangle Partition." Advanced Materials Research 718-720 (July 2013): 2296–301. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2296.

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In order to improve the robustness and real time performance of SURF based image matching algorithms, a new descriptor is proposed. We compute the new descriptor in a rectangle local region (the side set to 20s). Firstly, the local region is divided into 8 equal triangle subregion. Secondly, local region location grid is rotated to align its dominate orientation to a canonical direction. The keypoint dominate orientation and its orthogonalorientation is defined as the x and y directions of the descriptors local coordinate system.Thirdly, compute the Haar wavelet response in x and y directions within the keypoint local region. In order to reduce the boundary effect and outer noise, Haar wavelet response in the same Grid of different triangle is both assigned to each triangle in different weight, and then a gaussian weighting function is used. Compute the histogram of Haar wavelet response and absolute Haar wavelet response, so each triangle subregion constitutes a vector with 4 dimensions. Finally, a descriptor with 32 dimensions is constituted and the descriptor is normalized to achieve illumination invariance. The experimental results show that the performance of the new descriptor is even better than SURF descriptor.

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Gao, Yun, Hao Zhou, and Xue Jie Zhang. "A Novel Object Tracking Based on Foreground Hue Histogram." Applied Mechanics and Materials 278-280 (January 2013): 1205–10. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1205.

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We propose a tracking algorithm for a single non-rigid object based on its foreground hue histogram. A tracked region can be described by the foreground hue histogram only calculating foreground object pixels, which can effectively restrain the disturbing of complex background environments. For measuring the object likelihood, we match the foreground hue histogram with that of the tracked object and refer the result of motion detection to encircle the tracked object region as much as possible. During the tracking, we update the hue histogram model for adapting the object appearance variation. The proposed algorithm is realized in the particle filter frame, and the experiments show that it is capable of robustly and accurately tracking a single non-rigid object for the situations of complex background scenes and strong appearance variations.

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Inoue, Kohei, Naoki Ono, and Kenji Hara. "Local Contrast-Based Pixel Ordering for Exact Histogram Specification." Journal of Imaging 8, no. 9 (September 10, 2022): 247. http://dx.doi.org/10.3390/jimaging8090247.

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Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an image can be voted to any target histogram to achieve exact histogram specification. This paper proposes a method for ordering pixels in an image on the basis of the local contrast of each pixel, where a Gaussian filter without approximation is used to avoid the duplication of pixel values that disturbs the strict pixel ordering. The main idea of the proposed method is that the problem of pixel ordering is divided into small subproblems which can be solved separately, and then the results are merged into one sequence of all ordered pixels. Moreover, the proposed method is extended from grayscale images to color ones in a consistent manner. Experimental results show that the state-of-the-art histogram specification method occasionally produces false patterns, which are alleviated by the proposed method. Those results demonstrate the effectiveness of the proposed method for exact histogram specification.

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Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Gabor-HOG Features based Face Recognition Scheme." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 331. http://dx.doi.org/10.11591/tijee.v15i2.1546.

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Extraction of invariant features is the core of Face RecognitionSystems (FRS). This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptor known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG), which the face image is filtered with the multiscale multiresolution Gabor filter bank to generate multiple Gabor magnitude images (GMIs), then the down-sampled GMIs and apply Histogram of Oriented Gradient to form the features. The experimental results on the FERET face database show the effectiveness of our methods.

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Peng, Yishu, Yunhui Yan, and Jiuliang Zhao. "Detail Enhancement for Infrared Images Based on Propagated Image Filter." Mathematical Problems in Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/9410368.

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For displaying high-dynamic-range images acquired by thermal camera systems, 14-bit raw infrared data should map into 8-bit gray values. This paper presents a new method for detail enhancement of infrared images to display the image with a relatively satisfied contrast and brightness, rich detail information, and no artifacts caused by the image processing. We first adopt a propagated image filter to smooth the input image and separate the image into the base layer and the detail layer. Then, we refine the base layer by using modified histogram projection for compressing. Meanwhile, the adaptive weights derived from the layer decomposition processing are used as the strict gain control for the detail layer. The final display result is obtained by recombining the two modified layers. Experimental results on both cooled and uncooled infrared data verify that the proposed method outperforms the method based on log-power histogram modification and bilateral filter-based detail enhancement in both detail enhancement and visual effect.

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Guo, Lie, Guang Xi Zhang, Ping Shu Ge, and Lin Hui Li. "Pedestrian Tracking with HOG and Color Histogram Features." Applied Mechanics and Materials 241-244 (December 2012): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.498.

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To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.

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Sun, Ming, and Jia Wei Li. "Object Tracking Algorithm Based on Block LAB Feature Histogram and Particle Filter." Advanced Materials Research 485 (February 2012): 193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.485.193.

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In order to improve real-time object tracking effect when tracking objects are partly covered or mixed by different backgrounds, and even under the conditions of changed illuminations, in this paper, we proposed an object tracking algorithm based on block LAB feature histogram and particle filter. To demonstrate new algorithm’s excellent performance, we carried several compared experiments when objects moved under different conditions such as changed illuminations, mixed backgrounds and so forth. Experiment results show that tracking objects are often lost by using tracking algorithm based on traditional features such as color histogram, but moving objects under various and complex environments could be correctly tracked by using real-time tracking algorithm proposed in this paper.

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Wu, ChangCheng. "Impulsive noise filter using median- and partition-based operation." Optical Engineering 47, no. 11 (November 1, 2008): 117004. http://dx.doi.org/10.1117/1.3028332.

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Ait Abdelali, Hamd, Fedwa Essannouni, Leila Essannouni, and Driss Aboutajdine. "An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel." Modelling and Simulation in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2592368.

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We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel (KFPPK) to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.

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He, Jiai, and Yong Na Li. "Particle Filter Object Tracking Algorithm Based on Color and Gradient." Advanced Materials Research 926-930 (May 2014): 3141–44. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3141.

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With the robustness of a single color which is not high in standard particle filter tracking, a fusion of color and gradient particle filter algorithm is proposed. By the advantages of color described the target ’global and gradients described the shape of structure, they are weighted fusion to form a new integrated histogram and applied to the particle filter framework. The experimental results show that compared with the traditional particle filter algorithm, the text of the algorithm can achieve relatively reliable target tracking under complicated background and illumination changes, with better robustness and reliability.

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Wibowo, Suryo Adhi, Hansoo Lee, Eun Kyeong Kim, and Sungshin Kim. "Visual Tracking Based on Complementary Learners with Distractor Handling." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/5295601.

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The representation of the object is an important factor in building a robust visual object tracking algorithm. To resolve this problem, complementary learners that use color histogram- and correlation filter-based representation to represent the target object can be used since they each have advantages that can be exploited to compensate the other’s drawback in visual tracking. Further, a tracking algorithm can fail because of the distractor, even when complementary learners have been implemented for the target object representation. In this study, we show that, in order to handle the distractor, first the distractor must be detected by learning the responses from the color-histogram- and correlation-filter-based representation. Then, to determine the target location, we can decide whether the responses from each representation should be merged or only the response from the correlation filter should be used. This decision depends on the result obtained from the distractor detection process. Experiments were performed on the widely used VOT2014 and VOT2015 benchmark datasets. It was verified that our proposed method performs favorably as compared with several state-of-the-art visual tracking algorithms.

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Sun, Qiao, Shengxiu Zhang, Lijia Cao, Xiaofeng Li, and Naixin Qi. "Visual tracking via crossing-bin histogram Bhattacharyya similarity." Sensor Review 37, no. 4 (September 18, 2017): 478–84. http://dx.doi.org/10.1108/sr-03-2017-0033.

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Purpose The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization. Design/methodology/approach In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter. Findings A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking. Originality/value In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.

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KumarSoni, Shivank, and Y. K. Jain. "Histogram Equalization based PCCE Algorithm and Wiener Filter for Power Reduction." International Journal of Computer Applications 111, no. 2 (February 18, 2015): 44–48. http://dx.doi.org/10.5120/19513-1132.

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Pang, Ke, Zaifeng Shi, Jiangtao Xu, and Suying Yao. "Adaptive Partition-Cluster-Based Median Filter for Random-Valued Impulse Noise Removal." Journal of Circuits, Systems and Computers 27, no. 07 (March 26, 2018): 1850110. http://dx.doi.org/10.1142/s0218126618501104.

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As the most popular nonlinear denoise technique, the median filter has attracted significant attention in recent years. In this paper, a novel adaptive median filter is presented to remove random-valued impulse noise in images, named Adaptive Partition-Cluster-Based Median (APCM) Filter. Based on the partition cluster idea, the noise detector classifies pixels into different groups and identifies the noisy pixels in different regions adaptively without iterations. According to the results of noise detection, an improved adaptive decision-based filter is presented to restore the pixels which are corrupted by random-valued impulse noise. The proposed filter technique is open to any impulse noise. Extensive simulation results demonstrate that the proposed method substantially outperforms other state-of-the-arts impulse noise filter techniques both visually and in terms of objective quality measures. Furthermore, the proposed method is much friendly to the hardware parallel implementation of image processing because of its low computation complexity and simple realizable structure.

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Du, Sijie, Hongxin Xu, and Tianping Li. "Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion." Journal of Sensors 2020 (November 25, 2020): 1–13. http://dx.doi.org/10.1155/2020/8846977.

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In recent years, the Mean shift algorithm has extensive applications in the field of video tracking. It has some advantages of low cost, small memory, and good tracking effect. However, there are some shortcomings in the existing algorithm; for example, it cannot produce adaptive changes as the target size changes. And when there are similar objects, it is prone to target positioning errors and tracking failures caused by occlusion. In this paper, an improved method of continuous adaptive change Mean shift (Camshift) for high-precision positioning and tracking is proposed. The traditional Camshift method only uses hue components in HSV to extract features. This paper uses the combination of H and S components in HSV space to build a two-dimensional color feature histogram and with the image’s LBP feature histogram to increase tracking accuracy. Meanwhile, for the sake of target occlusion and nonlinear changes in the tracking process, this paper introduces a Gaussian-Hermit particle filter that is updated by the Kalman filter. Experimental result demonstrates that the real-time performance of the proposal in this paper is better than Mean shift, Camshift, simple particle filter, and Kalman filter.

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Zhao, San Lung, Shen Zheng Wang, Hsi Jian Lee, and Hung I. Pai. "Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking." Advanced Materials Research 121-122 (June 2010): 585–90. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.585.

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The study presents a human tracking system. To tracking a person, we adopt a particle filter as tracking kernel, since the method has proven successful for tracking in non-linear and non-Gaussian estimation. In a particle filter, a set of weighted particles represents the possible target sates. In this study, we measure the weight according to both the appearances of the target object and background scene to improve the discriminability between them. In our tracker, the appearances are modeled as color histogram, since it is scale and rotation invariant. However, the color histogram extraction for a large number of overlap regions is repeated redundantly and inefficiently. To speed up it, we reduce the cost for calculating overlapped regions by creating a cumulative histogram map for the processing image. The experimental results show that the tracker has the best precision improvement, and the tracking speed is 49.7 fps for 384 × 288 resolution, when we use 600 particles. The results show that the proposed method can be applied to a real-time human tracking system with high precision.

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Cheng, Xuan, Liping Song, and Hongbing Ji. "Extended Target GMPHD Filter Based on Mean Shift and Graph Structure." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 3 (June 2018): 420–25. http://dx.doi.org/10.1051/jnwpu/20183630420.

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In view of excessive measurements partition number, a large computation load of extended target tracking and leakage estimation when the extended targets cross, an extended target tracking algorithm based on GMPHD with mean shift and graph structure is proposed. Firstly, the kernel density estimation is used to eliminate the clutter measurements. Secondly, mean shift algorithm is adopted to divide the extended target measurements set, and sub-division is considered to carry or not based on the information fed back from the updated graph structure. Then, the extended target GMPHD algorithm is used to filter. Finally, the graph structure is updated by the one-step predicted value of the filtering result, and the updated graph structure information is used to guide the measurement partition at the next moment. Matlab simulation shows that the algorithm proposed decreases largely the number of measurements partition, reduces the computational complexity, and solves the leakage estimation problem when the targets cross.

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Liang, Zhuoqian, Bingwen Feng, Xuba Xu, Xiaotian Wu, and Tao Yang. "Geometrically Invariant Image Watermarking Using Histogram Adjustment." International Journal of Digital Crime and Forensics 10, no. 1 (January 2018): 54–66. http://dx.doi.org/10.4018/ijdcf.2018010105.

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In this article, a blind image watermarking scheme, which is a robust against common image processing and geometric attacks is proposed by adopting the concept of histogram-based embedding. The average filter is employed to low-pass pre-filter the host image. The watermark bits are embedded into the histogram of the low-frequency component and the template bits are embedded in the high-frequency residual. The embedding is performed by adjusting the value of two consecutive histogram bins. Furthermore, a post-quantization is employed after the embedding round to improve robustness. All pixel modifications incurred are based on the human visual system (HVS) characteristics. As a result, a good tradeoff between robustness and imperceptibility is achieved. Experimental results reported the satisfactory performance of the proposed scheme with respect to both common image processing and geometric attacks.

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Farina, Marcello, and Ruggero Carli. "Partition-Based Distributed Kalman Filter With Plug and Play Features." IEEE Transactions on Control of Network Systems 5, no. 1 (March 2018): 560–70. http://dx.doi.org/10.1109/tcns.2016.2633786.

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Yang, Qi, and Jia Fu Jiang. "PFCHA: A New Moving Object Tracking Algorithms Based on Particle Filter and Histogram." Applied Mechanics and Materials 110-116 (October 2011): 3343–50. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.3343.

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The complexity of the video background of moving target tracking algorithm led to the robustness of the important reasons is not high for the limitations of existing algorithms, a framework based on the movement of particle filter tracking algorithm. In order to reduce the impact of occlusion for the algorithm, the algorithm of moving objects make full use of color and motion characteristics of moving target detection, and to avoid the interference of the complex background, within the framework of particle filter in the object color histogram analysis. Finally, given an effective comparison of the calculation. Experimental results show that particle filter based target tracking algorithm can effectively remove the interference of the complex background, the context for any trace detection of high robustness.

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Zou, Xing, Yu Liu, Ze Fu Tan, Cheng Sheng Tu, and Hong Zhang. "A Fog-Removing Treatment Based on Combining High-Frequency Emphasis Filtering and Histogram Equalization." Key Engineering Materials 474-476 (April 2011): 2198–202. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.2198.

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Fog may cause the impairment of image as well as the decrease in the distinguish ability. The present paper is to get rid of the weather’s influence from the impaired image. According to Retinex theory and exponential relationship between the degradation of the image and the depths of the scene points, it puts forward a fog-removing treatment based on combining high-frequency emphasis filtering and histogram equalization .Firstly, obtain the padding parameters and fill it. Secondly, filter the impaired image using Butterworth highpass filter of order 2. Through the padding parameters, Highpass filtering is not overly sensitive to the value of cutoff frequencies, as long as the radius of the filter is not so small that frequencies near the origin of the transform are passed. In which the gray-level tonality due to the low-frequency components was retained. Lastly, histogram balanced the image gotten last step. The simulation result based on Matlab shows his algorithm can effectively improve the visual effect scene under the condition of mist.

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Jiang, Chundi, Wei Yang, Yu Guo, Fei Wu, and Yinggan Tang. "Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy." Entropy 20, no. 11 (October 28, 2018): 827. http://dx.doi.org/10.3390/e20110827.

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Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

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Li, Bicao, Guanyu Yang, Zhoufeng Liu, Jean Louis Coatrieux, and Huazhong Shu. "Multimodal Medical Image Registration Based on an Information-Theory Measure with Histogram Estimation of Continuous Image Representation." Mathematical Problems in Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/2135453.

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This work presents a novel method for multimodal medical registration based on histogram estimation of continuous image representation. The proposed method, regarded as “fast continuous histogram estimation,” employs continuous image representation to estimate the joint histogram of two images to be registered. The Jensen–Arimoto (JA) divergence is a similarity measure to measure the statistical dependence between medical images from different modalities. The estimated joint histogram is exploited to calculate the JA divergence in multimodal medical image registration. In addition, to reduce the grid effect caused by the grid-aligning transformations between two images and improve the implementation speed of the registration method, random samples instead of all pixels are extracted from the images to be registered. The goal of the registration is to optimize the JA divergence, which would be maximal when two misregistered images are perfectly aligned using the downhill simplex method, and thus to get the optimal geometric transformation. Experiments are conducted on an affine registration of 2D and 3D medical images. Results demonstrate the superior performance of the proposed method compared to standard histogram, Parzen window estimations, particle filter, and histogram estimation based on continuous image representation without random sampling.

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Roy, Amarjit, Joyeeta Singha, and Rabul Hussain Laskar. "Removal of Impulse Noise from Gray Images Using Fuzzy SVM Based Histogram Fuzzy Filter." Journal of Circuits, Systems and Computers 27, no. 09 (April 26, 2018): 1850139. http://dx.doi.org/10.1142/s0218126618501396.

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Impulse noise is an image noise that degrades the quality of the image drastically. In this paper, k-means clustering has been incorporated with fuzzy-support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from gray images. Here, local binary pattern (LBP) has been incorporated with previously used feature vector prediction error of the processing pixel, absolute difference between median value and processing pixel, median pixel, pixel under operation and mean value around the processing kernel. In this work, [Formula: see text]-means clustering has been used for reducing the feature vector set, where features have been extracted from the images corrupted with 10%, 50%, and 90% impulse noise. If the pixel is depicted as noisy in testing phase, histogram adaptive fuzzy filter is processed over the noisy pixel under operation. It is seen that the proposed filter offers improved performance over some of the state-of-the-art filter in terms of different image quality measures likely PSNR, SSIM, MSE, FSIM, etc. It is observed that performance is increased by [Formula: see text][Formula: see text]2–5[Formula: see text]dB than baseline filters likely SVM fuzzy filter, and artificial neural network based adaptive sized mean filter (ANNASMF) especially at high density noise.

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Wang, Tao, Wen Wang, Hui Liu, and Tianping Li. "Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion." Sensors 19, no. 5 (March 12, 2019): 1245. http://dx.doi.org/10.3390/s19051245.

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With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.

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Medeiros, Henry, Germán Holguín, Paul J. Shin, and Johnny Park. "A parallel histogram-based particle filter for object tracking on SIMD-based smart cameras." Computer Vision and Image Understanding 114, no. 11 (November 2010): 1264–72. http://dx.doi.org/10.1016/j.cviu.2010.03.020.

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Journal articles on the topic 'Histogram and partition based filter'

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