Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based ...Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstrate that the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm.
•An extension of Chain Code-Based Local Descriptor (CCBLD) is proposed.•CCBLD is applied to face recognition task.•Bag-of-Visual-Words paradigm is realized through the dictionary of chain-codes.•Test results show that CCBLD is comparable or outperforms other local descriptors.•The approach is tested using CAS-PEAL, ColorFERET, FG-NET, and other datasets.
A new compact encoding is presented of rasterized bi-level shapes at multiple resolutions. The encoder accepts the Freeman chain code in four directions (F4) at the input, and builds a ...multi-resolution code named MrCC. The encoding process constructs the coarser representation of F4 chain code, and the resulting MrCC codes simultaneously. MrCC encodes the differences between sequences of F4 chain code from successive resolutions. Several transformations are performed during this process. Various rasterized shapes were used to analyse the efficiency of the new code, which was, on average, 22% better than the concatenation of F4 chain codes of various resolutions.
In Character Recognition, the Feature extraction has encompassed a well-known role. Here, Feature Extraction centered on Chain code (CC) is implemented. CC encodes every stroke with a string of ...numbers, in which every number signifies a specific direction wherein the subsequent point on the stroke is present. CC centered feature safeguard information and permits reasonable data to decrease. Disparate CC can signify the same shape since the CC is reliant on starting point. So here, Starting Point and rotation invariant feature extraction technique using Normalized Differential Chain Code (NDCC) is proposed. A two-stage classifier is employed for classification. Here, the NDCC feature is utilized in the pre-classifier and pre-processed (x,y) coordinates are used in the post classifier. In both stages K-NN classifier is used. This feature is verified in HP-Lab data that is present in the UNIPEN format. Investigational outcomes proved that the proposed feature enhances recognition accuracy over the selected dataset.
The biggest disadvantage of using chain code techniques is the generation of low definition contour shapes, in this paper we present the Extended Slope Chain Code (ESCC) which is an improvement on ...the Slope Chain Code (SCC). The ESCC is focused on the representation of high definition contour shapes. Generally speaking, most chain codes hold the length of the straight-line segments which represent the contour shape as a constant. In this case, the contour shapes represented by ESCC are composed of variable segments, which allow us to have a better description of the contour shape. Thus, the length of the segments are a function of the slope changes, i.e. the length of the next segment depends on the value of the slope change at that point. Therefore, the ESCC is continuously adjusting to the curvature requirements of contour shapes, in order to have a better description of contour shapes.
With the proliferation of information and communication technology in every walks of the society, including healthcare services, digitization, and increased sophistication have been gaining pace, ...digital healthcare alternatives such as electronic healthcare record (EHR) have gained prominence with increased patients' data volume. However, traditional EHR-based systems are plagued by data loss risks, security and immutability consensus over health records, gapped communication among constituted hospitals, and inefficient clinical data retrieval systems, among others. Blockchain has been developed as a decentralized technology that holds the promise to address the aforesaid facilities in EHR-based systems. This article presents a patient-centric design of a decentralized healthcare management system with blockchain-based EHR using javascript-based smart contracts. A working prototype based on hyperledger fabric and composer technology has also been implemented which guarantees the security of the proposed model. Experiments with the hyperledger caliper benchmarking tool provide performance such as latency, throughput, resource utilization, and so on under varied scenarios and control parameters. The results affirm the efficacy of the proposed approach.
CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING Ishikawa, T.; Hayashi, A.; Nagamatsu, S. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
05/2018, Letnik:
XLII-2
Journal Article
Recenzirano
Odprti dostop
Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were ...defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.
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•Compression is vital for the efficiency of channel capacity in telemedicine network.•The chain code strategy is adapted for effective compression of binary medical data.•The ...strategies are designed for uncovering redundant stemmed from medical images.•The proposed methods are applied to a wide dataset established by the Authors.•The strategies achieved the highest compressed ratio compared to modern standards.
Obtaining a 3D medical visualization is a tedious process requiring several processing steps (such as segmentation) and assigning various rendering parameters (such as color and opacity). Current systems use video/image exporting or snapshots to save results. Such vendor-dependent tools not only prevent the possibility of further interactions but also creates additional large-size data that is problematic to store in PACS over time and hard to transfer for teleradiology applications. To overcome, alternative strategies propose a representation of the visualizations, which only store segmentation masks that contains the binary form of segmented data. Unfortunately, existing compression methods are limited to effectively compress the volumetric data. In this study, lossless storage of binary segmented data is effectively performed by two newly-proposed chain code approaches. Particularly-two novel contributions are presented: 1. The dictionary of normalized angle difference is improved as a new chain symbol coding procedure, namely normalized angle difference, by adding new symbols to the dictionary aiming to generate a low-entropy symbol sequence for medical volumes. 2. A new volumetric approach that utilizes 26 symbols to encode volumetric data is developed. Each slice is visited, and the contour of the segmented object is codified such that eight different vectors for each slice (pointing to one of the four faces of each voxel, plus four towards one of its edges) are obtained. The developed methods are tested on diverse volumetric segmented data and compared to existing standards. It is shown that the proposed methods outperform well-established techniques.
The registration of the infrared (IR) image and the low-light-level (LLL) image remains a challenging problem due to poor dispersion of feature points, low correlation of structure and texture ...information. In this paper, we propose a method based on neighbourhood difference chain code to address the challenge. First we extracted the feature points of the images with the binary eight or sixteen-neighborhood information. And then construct the descriptor of the feature point by neighborhood difference chain code. At last we use the Euclidean distance to match the feature points. We adopt TNO and INO data sets to verify our method, and by comparing with four objective evaluation parameters obtained by other three methods. The result demonstrated that the proposed algorithm performs competitively, compared to the state-of-arts such as Harris, SIFT and SURF, in terms of accuracy of registration and speed.
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