Similarity Detection Algorithm Research Articles

A novel quality test method of electricity meter LCD Screen based on image processing

In order to solve the problem of incorrect test on the LCD screen quality of smart electricity meter and to make its visual inspection item perfect, a novel quality test method based on image processing is put forward in this paper. First, a novel algorithm of self-learning fast template matching based on the sequential similarity detection algorithm is proposed. Then, how to synthesize characteristics of many templates into a standard template and how to establish a real-time and self-learning template library are described in detail. Finally, the sequential similarity detection algorithm is improved with adaptive threshold. The experiment results show that the proposed test method has higher efficiency and accuracy for smart electricity meter LCD screen quality testing. Therefore, it has great practical applications.

EPAS: A Sampling Based Similarity Identification Algorithm for the Cloud

The explosive growth of data brings new challenges to the data storage and management in cloud environment. These data usually have to be processed in a timely fashion in the cloud. Thus, any increased latency may cause a massive loss to the enterprises. Similarity detection plays a very important role in data management. Many typical algorithms such as Shingle, Simhash, Traits and Traditional Sampling Algorithm (TSA) are extensively used. The Shingle, Simhash and Traits algorithms read entire source file to calculate the corresponding similarity characteristic value, thus requiring lots of CPU cycles and memory space and incurring tremendous disk accesses. In addition, the overhead increases with the growth of data set volume and results in a long delay. Instead of reading entire file, TSA samples some data blocks to calculate the fingerprints as similarity characteristics value. The overhead of TSA is fixed and negligible. However, a slight modification of source files will trigger the bit positions of file content shifting. Therefore, a failure of similarity identification is inevitable due to the slight modifications. This paper proposes an Enhanced Position-Aware Sampling algorithm (EPAS) to identify file similarity for the cloud by modulo file length. EPAS concurrently samples data blocks from the head and the tail of the modulated file to avoid the position shift incurred by the modifications. Meanwhile, an improved metric is proposed to measure the similarity between different files and make the possible detection probability close to the actual probability. Furthermore, this paper describes a query algorithm to reduce the time overhead of similarity detection. Our experimental results demonstrate that the EPAS significantly outperforms the existing well known algorithms in terms of time overhead, CPU and memory occupation. Moreover, EPAS makes a more preferable tradeoff between precision and recall than that of other similarity detection algorithms. Therefore, it is an effective approach of similarity identification for the cloud.

Application of image processing technology in the classroom energy-saving system

In this paper, image processing technology is applied to the field of classroom energy-saving. After the preprocessing of classroom images, we utilize SOPC based on the Nios II embedded soft-core processor, in order to acquire the number and position of students through illumination detection, image segmentation and template matching achieved by the sequential similarity detection algorithm (SSDA). Then the signal to control the switches of electrical appliances intelligently will be output through SOPC of FPGA. With the effective combination of hardware and software, the system achieves the intelligent control of electrical appliances such as lights in the classroom, which covers the shortage of the energy management of the classroom.

Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform

Considering the disadvantages of massive calculation and slow speed of traditional Scale Invariant Feature Transform (SIFT) algorithm, we propose an improved image mosaic method which combines Wavelet Transform (WT) and Compressed Sensing (CS) algorithm. The method works as follows. Firstly, images are transformed with wavelet and compressed using compressed sensing technology. Then, image feature points are extracted in combination with SIFT algorithm. Finally, Sequential Similarity Detection Algorithm (SSDA) with adaptive threshold is used to fast search of image matching to find out an optimal stitching line, and a panoramic image is obtained. Experimental results demonstrate that the method realizes fast image matching, efficiently overcomes the shortcomings of heavy computation and low efficiency in the process of extracting image features, and guarantees matching accuracy and stitching efficiency, which meets the real-time requestments in machine vision system. This algorithm can be applied to image matching and stitching in the field of digital image security.

Road edge detection based on dual-threshold SSDA template matching

Extracting road edge information from remote sensing image can simplify the conventional urban road mapping work. Based on the general road image features,this paper proposes a dual-threshold SSDA( sequential similarity detection algorithm) template matching method in an image processing model. And on the basis of the general SSDA,another algorithm is presented to reduce the excessive number of samples responsible for the error growth. Compared with other algorithms,this algorithm can more effectively access the road edge information extraction. As for some parts of the road which cannot be completely extracted through pretreatment process,the detection results can be corrected to reduce treatment,and hence the processing efficiency will be improved.

Sequential similarity detection algorithm based on image edge feature

This paper proposes a new sequential similarity detection algorithm (SSDA), which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.

Rapid Location of LED Die Array Based on Cross Correlation in Frequency Domain

A rapid template matching algorithm is used to locate l-emitting d (LED) die array on wafer planes in vision-based LED sorting or die-bonding system, the image of LED die array is first converted to binary image and is pre-processed through morphological operations such as dilation and erosion, and the cross correlation operation between LED die templatae and wafer image in frequency domain is implemented by using fast Fourier transform (FFT), and the pixel coordinates of all LED dies on the wafer plane can be determined at one time using the cross correlation in frequency domain (CCFD) instead of in spacial domain. Experimental results of LED die array measurement to a large-diameter LED wafer show that detection efficiency is far higher than conventional normalized cross-correlation (NCC) algorithm, and is only half of consumed time to compare with the sequential similarity detection algorithm (SSDA), the proposed CCFD algorithm can be used for rapid LED die array location in vision-baesd LED sorting or die-bonding machines.

Research of Picture Adaptive Processing for Packaging Printing Online Detection

In the process of printing machines increasing and reduction of speed, the image collected from online detection system for different speed of printing needs picture adaptive processing. In this paper, there are two methods put forward to realize above goal. One method is sequential similarity detection algorithms and the other is ratio value. Apply with VC++ programming to realize image matching on the premise of the unique CCD detection equipment. The experiment results show that: sequential similarity algorithm on the premise of printing machine running in low speed, by comparing with threshold to realize variable-speed imaging. The ratio value method can be used for a variety of printing speed and it has high speed, high detection efficiency and wide range of application.

Robot View Navigation Based on Level-Divided Strategy

In allusion to the colored image matching characteristic in the system of robot view navigation, SSDA (the sequential similarity detection algorithm) is improved and adaptive genetic algorithm is brought in; meanwhile, level-divided search strategy connective with rough and exact matching. The improved algorithm can enhance the image matching speed with no matching accuracy reduced, so that real-time requirements of robot view navigation can be met and robot view navigation will be of preferable robustness.

FINDSITEX: A Structure-Based, Small Molecule Virtual Screening Approach with Application to All Identified Human GPCRs

We have developed FINDSITE(X), an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSER(VMT), TASSER(VMT)-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITE(X) is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSER(VMT)-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 nonredundant (TC < 0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF(0.01)) is 22.7, whereas EF(0.01) by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, the average EF(0.01) reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html .

Motion detection in moving background using a novel algorithm based on image features guiding self-adaptive Sequential Similarity Detection Algorithm

A novel efficient algorithm for motion detection in dynamic background was proposed. In image registration step, a feature-based and self-adaptive Sequential Similarity Detection Algorithm (SSDA) algorithm was proposed, which searches for matching position under constraints induced by image features with variational threshold. Then perform change detection by calculating and classifying the Mean Absolute Difference (MAD) around detected features in the middle frames of three consecutive images. Moving objects position was determined according to the rule that the feature from moving regions shows a lager MAD. Experiments on data sets of four typical scenes show that the improved registration algorithm is accurate and costs less than 0.4s in computation, much faster compared with other four methods, and the proposed Dual Maximum Mean Absolute Difference Algorithm (DMMADA) can obtain a robust set of moving object features. Our algorithm can be used for fast detection of moving targets in dynamic background as well as change detection.

Study of Traditional Chinese Medical Diagnostic Platform Establishment for Hyperlipemia

Hyperlipemia syndrome diagnosis standardizing research is the hotspot in Chinese medicine domain. A traditional Chinese medical diagnostic platform for hyperlipemia is established, which is consisted of four information collection modules including tongue images acquisition, facial image acquisition, inquiry diagnosis, pulse diagnosis, and auscultation diagnosis information. Through this platform, 316 cases of hyperlipemia clinical information have been gathered. A clustering algorithm is applied to analyze hyperlipemia clinical symptoms data based on these samples to get statistically significant classification results, and find the corresponding relationship between these results and syndromes. Clustering algorithm is programmed to classify syndromes and setting similarity threshold interface is available, which make it is possible to classify a large number of samples more adaptively. Compare disease characteristics of samples in every category with syndromes category and find out the relationships between them, by which the foundation of hyperlipemia syndrome differentiation standardization will be establish. Samples are classified to five categories. Finally, an threshold similarity detection algorithm is used to classify a new sample collected by the system to one of the five categories, which can give a syndrome classification suggestion based on statistics to Chinese medical doctors, which will improve the diagnostic efficiency and diagnostic accuracy.

应用序贯相似检测的基本矩阵快速鲁棒估计

A fast and robust fundamental matrix estimation method based on Sequential Similarity Detection Algorithm(SSDA) is presented to estimate the fundamental matrix rapidly and accurately.The SSDA is introduced into the Maximum a Posteriori Sample Consensus(MAPSAC) to search the optimum model parameters and the accumulation times of computing a cost function are cut down by eliminating the false model as soon as possible,which not only keeps the better robustness of MAPSAC,but also reduces its computation effectively.Then,the initial inliers obtained by the improved MAPSAC are optimized with a M-estimator.Those inliers with larger residual errors are removed and the optimized inliers are used to compute the fundamental matrix to enhance the precision and improve the robustness of the algorithm.Experiment results demonstrate that the proposed algorithm performs better in accuracy and robustness,and its average speed has increased at least 30% as compared with that of the MAPSAC.The proposed algorithm can satisfy the requirements for real-time,precision and robustness in the fields such as three-dimensional reconstruction,image matching,image tracking and camera self-calibration.

Extracting road centrelines from high-resolution satellite images using active window line segment matching and improved SSDA

This paper presents a new semi-automatic approach to extracting main road centrelines from high-resolution satellite images. The approach is based on active window line segment matching and an improved sequential similarity detection algorithm (SSDA). First, a user-specified point on the road centreline is selected as a reference central point to define a template window, and a thresholding operation is performed within the window. Then a best matched target window along the road is searched to determine the next central point in the road. To avoid the influence of noise, only the line segment of ‘interest’ in the window is matched. An improved SSDA searching strategy is proposed to increase the matching speed. When the best matched target window is found, its centre is used as the next central point in the road, and a new template window is generated. By repeating the matching process, a series of central points can be obtained automatically and the corresponding final road centreline from them is extracted. Our approach also allows some kinds of user interventions in case automatic tracking fails. Experimental results confirm that the proposed approach is capable of rapidly and accurately extracting main road centrelines and has good robustness against noise.

Automatic functional TV set failure detection system

Enormous technological innovation in recent years has put a great challenge upon manufacturers in consumer electronics industry: how to efficiently and accurately verify the functionality of a product handed to a customer. To address this problem, we propose a fully automated system for functional failure detection in TV sets. In the proposed framework, the automatic assessment of TV set functionality is performed by processing captured images of the TV set under inspection and the captured images from the reference TV set (which is considered to be with no functional failures). A new algorithm for image similarity is proposed and incorporated in the system for evaluating the difference between the tested image and the reference one, in respect to specific degradations that can occur in TV sets. The similarity detection algorithm is a full-reference intra-frame video quality assessment scheme, which is based on spatially local mean square error and variance between the reference and the tested image. Based on the similarity detection, an acceptance decision for the TV set functionality is made. The proposed embedded system for automatic TV functional failure detection includes (i) central control unit through which the testing methodology is carried out: The TV set under inspection is fed by the test video signals defined by the (ii) specified test-case-scenario, while the picture is simultaneously captured from the tested TV set and compared by the (iii) proposed similarity detection algorithm to the reference one, for the same type of TV set. Finally, the results are stored in the (iv) data base for TV set debugging purposes. The usability and effectiveness of the proposed methodology has been experimentally evaluated and detailed analysis of the results has been reported. The proposed similarity detection algorithm has shown to be superior to the other state-of-the-art image quality assessment algorithms in terms of detecting TV picture degradations (such as picture misalignment, illumination change, aliasing, blurring, etc.) in captured images from TV set, under the methodology which incorporates defined test-case-scenario.

Region similarity based edge detection for motion estimation in H.264/AVC

This paper introduces a block-based motion estimation algorithm based on projection with adaptive window size selection. By using one-dimensional (1-D) projection the original two-dimensional (2-D) block-matching problem is translated to a simpler 1D matching, which eliminates majority of potential pixel participation. In this paper, we propose an efficient fast motion estimation method by using region similarity detection (RSD) algorithm to reduce the encoding time. The proposed RSD algorithm effectively estimates the texture direction of the block to find the direction and then horizontal, vertical or diagonal projection is employed for SAD calculation. Experimental results show that the proposed algorithm achieves more than a 60% time reduction with negligible PSNR loss and bitrate increments.

Fast template matching for spike sorting

AbstractThe present study introduces an approach to detect and classify extracellulary recorded action potentials of neurons, usually termed spike sorting. Our approach is based on template matching, which is an optimal filter under Gaussian noise; however, this approach is usually expensive in computational time.To speed up the filter, it is important to curtail the matching process when the distance between template and waveform exceeds some threshold. We approach this aspect of the problem using the frame of similarity detection algorithms (SSDA) and Davies‐Bouldin validation indices (DBVI). Windowing pair of the filter was selected in DBVI‐based order and a signal which has a rapidly increasing error was discarded to reduce the computational time. DBVI is a function of the ratio of the sum of within‐cluster scatter to between‐cluster separation; thus, using this order we can expect to separate a signal and a noise in fewer window point calculation than full point matching. This matching process performed well, with a shortter computational time and fewer incorrect classifications than other ordering methods such as time‐based or amplitude‐based order. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(11): 57–63, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10066

S0302-2-1 パターンマッチングの精密化によるデジタル画像相関法の高精度化(実験力学における計測・解析法の新展開(2))

Until now, the comparison with a strain gauge or the finite element method has performed accuracy verification of the displacement analysis of the subpixel by the pattern-matching method using a digital image correlation method. The result proved that the digital image correlation method has sufficient high accuracy at the time of infinitesimal deformation. However, mismatching may arise depending on the texture of an image. Although accuracy improved more to enlarge subset size, the demerit of which the computation time increases existed. Therefore, when subset size was set small, improvement in the accuracy of the digital image correlation method was investigated by aiming to reduce mismatching. In the digital image correlation method using coarse-to-fine approach, the result checked that an accurate result was obtained, without mismatching occurring, even when subset size was set up small. Moreover, in the digital image correlation method using the SSDA (Sequential Similarity Detection Algorithm) method, it was possible to have analyzed with accuracy almost equivalent to the digital image correlation method by a correlation coefficient.

Automatic matching of high‐resolution SAR images

Based on high‐resolution SAR data, in this paper, a novel automatic matching model is proposed. The model, which employs a coarse to fine strategy as a whole, consists of three steps. In the first step, edge features are extracted on different levels of pyramid images and an efficient Hausdorff distance‐based method is used to yield a coarse global feature match. Due to bi‐tree searching, the bottleneck of Hausdorff distance's matching is well resolved. Secondly, SSDA (Sequence Similarity Detection Algorithm) is employed to acquire tie‐points using a cross‐searching approach which treats features extracted from master and slave images equally. Finally, local‐adaptive splitting algorithm with MMSE (Minimum Mean Square Error) is used to achieve a fine matching; local‐adaptive splitting algorithm is the essential process to achieve sub‐pixel matching accuracy, which enhances the process's flexibility and robustness. Airborne SAR images with high resolution are provided by the Institute of Electronics, CAS and used for experiments—the results of the experiments demonstrate that the model proposed in this paper is robust, with high accuracy (up to a fraction of a pixel), and can be successfully applied to automatic matching of high‐resolution SAR images.