Directional Chain Code Research Articles

Wheat leaf diseases classification and severity analysis using HT-CNN and Hex-D-VCC-based boundary tracing mechanism.

Wheat is one among the significant crops for humans. Significant fungal illnesses of wheat are brought on by multiple pathogens. Wheat output could be enhanced by the early identification of wheat leaf disease. Thus, a novel hyperparameter tanh-based convolutional neural network (HT-CNN)-based wheat leaf disease prediction is proposed with its severity level. Here, initially, the red, green, and blue (RGB) images are converted into a hue saturation value (HSV) image. Next, the small probability space filtering is applied to the V component. Afterward, the contrast of the V component has been enhanced. The obtained HSV image is converted into the RGB image. Then, by employing weighted Canberra distance-based K-means (WCD-K means), the affected and normal regions are segmented. Next, the image is binarized. Afterward, for tracing a boundary around disease-affected region, the hex directional vertex chain code (Hex-D-VCC) is applied over the binarized image, and then the features are extracted. By employing baker's map-based Harris hawks optimization (BM-HHO), the optimal features are selected. For classifying disease, the selected features are further given into the HT-CNN, and the severity level is calculated to minimize the yield loss. As per the experimental result, the proposed model shows higher accuracy and efficacy when analogized to the other methods.

A Gesture Recognition Algorithm Based on Threedimensional Projection and Direction Chain Code

In this paper, the accelerometer of intelligent terminal is used for gesture recognition. Most of the current motion recognition algorithms in this field have great restrictions on the standardization of motion, and the recognition rate for slightly substandard motion is greatly reduced. Aiming at the existing "$3" algorithm, this paper proposes to project the 3D motion data to the 2D plane for trajectory pattern recognition, and uses the angle and radius to identify the curved area. This method can make up for the people greatly. Physiological habits lead to difficulty in the recognition of gestures, which can greatly improve the recognition accuracy when the movement is not standardized.

Myanmar WarningBoard Recognition System

In any country, warning text is described on the signboards or wallpapers to follow by everybody. This paper present Myanmar character recognition from various warning text signboards using block based pixel count and eight-directions chain code. Character recognition is the process of converting a printed or typewritten or handwritten text image file into editable and searchable text file. In this system, the characters on the warning signboard images are recognized using the hybrid eight direction chain code features and 16-blocks based pixel count features. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation, vertically cropping method and bounding box is used for connected component character segmentation. In the classification step, the performance accuracy is measured by two ways such as KNN (K’s Nearest Neivour) classifier and feature based approach of template matching on 150 warning text signboard images.

Real time detection system for rail surface defects based on machine vision

The detection of rail surface defects is an important part of railway daily inspection, according to the requirements of modern railway automatic detection technology on real-time detection and adaptability. This paper presents a method for real-time detection of rail surface defects based on machine vision. According to the basic principle of machine vision, an image acquisition device equipped with LED auxiliary light source and shading box has been designed and the portable testing model is designed to carry on the field experiment. In view of the real-time requirement, the method of extracting the target area from the original image is carried out without image pre-processing. The surface defects of the rail are optimized based on morphological process and the characteristics of the defects are obtained by tracking the direction chain code. It is demonstrated that the maximum positioning time of this proposed method is 4.65 ms and its maximum positioning failure rate is 5%. The real-time detection speed of this proposed method can reach 2 m/s, which can carry out real-time detection of artificial hand walking. The time of processing each picture is up to 245.61 ms, which ensures the real-time performance of the portable track defect vision inspection system. To a certain extent, the system can replace manual inspection and carry out the digital management of track defects.

The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image

Due to concrete surface roughness, uneven illumination, shadows, complex background and other disruptive factors, the traditional image processing-based concrete crack detection method cannot accurately detect concrete cracks, especially unclear ones and some tiny ones. The crack detection method based on the percolation model, which fully considered the low brightness and slenderness of the cracks, can accurately detect unclear and tiny cracks. But this method is time-consuming, and in some cases, it may cause fractures on the detected cracks. In order to solve these problems, this paper proposed an improved algorithm of image crack inspection based on the percolation model, which can reduce processing time through reducing the number of percolated pixels. To reconnect the fractured cracks, this method extracts the skeleton of cracks first by using an algorithm of skeleton extraction based on direction chain code. Then this paper proposed a region extension-based algorithm to reconnect part of the fractured cracks. Experimental results showed that this algorithm can significantly accelerate crack detection and maintain high detection precision.

Chain code compression using string transformation techniques

This paper considers the suitability of string transformation techniques for lossless chain codes' compression. The more popular chain codes are compressed including the Freeman chain code in four and eight directions, the vertex chain code, the three orthogonal chain code, and the normalised directional chain code. A testing environment consisting of the constant 0-symbol Run-Length Encoding (RLE0L), Move-To-Front Transformation (MTFT), and Burrows–Wheeler Transform (BWT) is proposed in order to develop a more suitable configuration of these techniques for each type of the considered chain code. Finally, a simple yet efficient entropy coding is proposed consisting of MTFT, followed by the chain code symbols' binarisation and the run-length encoding. PAQ8L compressor is also an option that can be considered in the final compression stage. Comparisons were done between the state-of-the-art including the Universal Chain Code Compression algorithm, Move-To-Front based algorithm, and an algorithm, based on the Markov model. Interesting conclusions were obtained from the experiments: the sequential uses of MTFT, RLE0L, and BWT are reasonable only in the cases of shorter chain codes' alphabets as with the vertex chain code and the three orthogonal chain code. For the remaining chain codes, BWT alone provided the best results. The experiments confirm that the proposed approach is comparable against other lossless chain code compression methods, while in total achieving higher compression rates.

Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction

Handwritten Devanagari off-line characters recognition is a challenging task due to peculiarities involved in the writing styles of different person. We are collected features from 64 dimensional feature extraction techniques and shadow features for the Devanagari character recognition in this proposed scheme. These calculated features are used for further classification. Features which are used here for recognition means histograms of direction chain code of the contour points of the characters [4]. The features are classified using neural network and selected the best result by weighted majority voting technique.

A combined static and dynamic feature extraction technique to recognize handwritten digits

The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (\(<\)1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

SVM AND NN BASED OFFLINE SIGNATURE VERIFICATION

Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution toward the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification concerning non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English based signature verification. To fill this gap, a threshold-based scheme for the verification of off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/end point and directional chain code are employed for feature extraction. The thresholds are computed based on the similarity measures obtained employing the nearest neighbor classifier. The SVM classifier has also been considered for mainly comparative experimental result generation. Furthermore, a Bangla signature database, which consists of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries, has been created and is employed for experimentation. An average error rate (AER) of 12.33% was obtained as the best verification result using directional chain code features in this research work. As a comparative study, a different dataset (GPDS-160) has also been considered.

Shot Cut Test Based on Mathematics Parameter and Chain Code

In this paper, An effective shot cut detection algorithm directly in DCT domain was proposed. This algorithm(including abrupt change and ) test the n -1 frame and n frame through the extracting the feature vector of each frame. When extract the features of frame, two-dimensional mathematics parameter m1-s from DCT coefficients without its inverse transform and 8 directional chain code was computed, Divide m1-s space into 30 unequal partitions (subspaces) and compute the numbers pr within the 30 subspaces (entries) as the feature vector to judge the different of the two frames. locating shot cuts is operated by comparison tests.

Recognition of Hand Written Kannada Numerals Using K-Medoids

Data Clustering is considered as an interesting approach for finding similarities in data and putting similar data into groups. Clustering partitions a data set into several groups such that the similarity within a group is larger than that among groups. This paper explores the cluster-based classification scheme in the context of recognition of handwritten Kannada numerals. In this paper, K-Medoids clustering algorithm is being used for the classification. The features used for the classification are obtained from the directional chain code information of the contour points of the numerals. The proposed algorithm is experimented on nearly 1000 samples of handwritten Kannada numerals and obtained 97% of recognition accuracy.

Compact representation of contours using directional grid chain code

An efficient contour-based method for the coding of binary shape information is described. Conventional chain coding techniques show high coding efficiency for lossless compression, but they exploit the coherence of the contour in only a restricted manner. Higher coding efficiency can be achieved by realising the neighbourhood relation as a Markov chain, and this is exploited in a new coding scheme, the directional grid chain coding (DGCC). The method is computationally efficient and the coding process adapts to the inherent changes in the contour. Two schemes are proposed, a lossless and a quasi-lossless method. The lossless scheme achieves 32% saving in bit rate compared with the conventional differential chain code (DCC). The second, quasi-lossless technique achieves 44% bit reduction compared with the DCC and the distortions present in the reconstructed contour are hardly noticeable to the human eye.

수리 형태학과 인간의 시각적 개념을 이용한 최적의 코너 점 추출을 위한 연구

코너 점(Corner point)은 영상 신호 처리의 패턴 인식에 있어 아주 중요한 정보이다 그래서 지금도 여러 가지 코너 점을 추출하는 연구가 진행되고 있다. 코너 점은 에지 영상에서 8 방향 체인 코드(Chain code)를 적용하여 찾는다. 그런데 에지 선분 기울기가 45도의 정수 배가 되지 않을 때, 8 방향 알고리듬을 그대로 적용하면 문제가 발생된다. 따라서 본 논문에서는 이러한 단점을 보완하면서 최적의 코너 점을 찾기 위해서 수리 형태학과 시각적 개념을 접목하여 코너 점을 처리하는 새로운 알고리듬을 제안한다. 제안한 방법으로 컴퓨터 시뮬레이션 하여 좋은 결과를 보이고 있다. 그래서 제안된 논문의 알고리듬은 공장 자동화 및 선박 레이더 영상의 해안의 영역 파악에도 적용되리라 생각된다. Comer point is a very important information to a pattern recognition of image processing. And so, many researchers develope various detecting comer point algoritms. But, there are some problems to get comer points by 8 directional chain code when the degree of edge line is not integer multiplication of 45 degree. So, we propose a new algorithm which is combined with morphology and human visual conception for optimal comer points without the above defects. We get a good simulation result by this proposed algorithm Ana so, we think this algorithm is very useful to FA(factory automation} and ship's radar system to know some coastal area from its image.

Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM

A holistic system for the recognition of handwritten Farsi/Arabic words using right–left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map (SOFM), is used for smoothing the observation probability distributions of trained HMMs. Experiments carried out on test samples show promising performance results.

Planar shape recognition by directional flow-change method

A method based on directional flow-change concept is proposed in this paper for recognizing planar shapes. This method locates critical points by evaluating directional flow-changes in terms of chain code directions along the contour. An angle string can be obtained by computing the sizes of angles at the critical points and then converting them into some special angle codes. Possible shape numbers are generated by applying a set of rules to the angle string. Consequently, the task of shape recognition is transformed to the task of comparing shape numbers. Experimental results show that our method is very effective in recognizing shapes and more efficient in computation than the curve bending function (CBF) method.