Journal of Advanced Veterinary and Animal Research
Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify b... more Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics. Materials and Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g., Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium. A total of 200 dig¬ital microscopic cell images comprising 40 of each of the genera mentioned above were used in this study. Results: The developed technique could identify and distinguish microscopic images of Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium with the highest accuracy of 92.20% for Staphylococcus and the lowest of 77.40% for Salmonella. A...
DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, ex... more DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D $$k-mer$$ k - m e r count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for $$k-mer$$ k - m e r . We develop an efficient system for finding the po...
Scene classification is an important and elementary problem in image understanding. It deals with... more Scene classification is an important and elementary problem in image understanding. It deals with large number of scenes in order to discover the common structure shared by all the scenes in a class. It is used in medical science (X-Ray, ECG and Endoscopy etc), criminal detection, gender classification, skin classification, facial image classification, generating weather information from satellite image; identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. In this paper, at first we propose a feature extraction method named LHOG or Localized HOG. We consider that an image contains some important region which helps to find similarity with same class of images. We generate local information from an image via our proposed LHOG method. Then by combing all the local information we generate the global descriptor using Bag of Feature (BoF) method which is finally used to represent and classify an image accurately and effic...
Indonesian Journal of Electrical Engineering and Computer Science, 2016
Fire incidence is one of the major disasters of human society. This paper proposes a still image-... more Fire incidence is one of the major disasters of human society. This paper proposes a still image-based fire detection system. It has many advantages like lower cost, faster response, and large coverage. The existing methods are not able to detect fire region adequately. The proposed method overcome and addresses the issue. A binary contour image of flame that is capable of classifying fire or no fire in image for fire detection is proposed in this study. The color of fire area can range from red yellow to almost white. So, here it is challenges the detected area is actually fire or no fire. Our propose method consists of five parts. Firstly, the digital image is taken from dataset and the digital image is sampled and mapped as a grid of dots or picture elements. We convert image to separate RGB Color range Matrix. We define some rules to select yellow color range of the image later on converted the image to binary range. Finally, binary contour image of flame information that detect...
Agriculture is the backbone of Bangladesh’s economy and it is one of the largest employment secto... more Agriculture is the backbone of Bangladesh’s economy and it is one of the largest employment sectors. In Bangladesh, the population is increasing rapidly and at the same time, the total cultivable land is decreasing significantly. To ensure maximum crop production using the limited land resources, it is essential to identify and select the appropriate type of soil because different crops need different soil types. Currently, there are two types of methods available to determine the soil type, namely chemical and image analysis. Although the first one is accurate, it is expensive and time consuming. On the other hand, image based soil classification is much cheaper and faster but its accuracy level is low. In this study, we present a novel feature based algorithm that combines quartile histogram oriented gradients (Q-HOG), most frequent $$\varphi $$ φ -Pixels and a new feature selection method for classifying soil types. We have used four machine learning algorithms and evaluated the ...
The seed is an inevitable element for agricultural and industrial production. The non-destructive... more The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy vari...
Nowadays, data are the most valuable content in the world. In the age of big data, we are generat... more Nowadays, data are the most valuable content in the world. In the age of big data, we are generating quintillions of data daily in the form of text, image, video, etc. Among them, images are highly used in daily communications. Various types of images, e.g., medical images, military images, etc. are highly confidential. But, due to data vulnerabilities, transmitting such images in a secured way is a great challenge. For this reason, researchers proposed different image cryptography algorithms. Recently, biological deoxyribonucleic acid (DNA)-based concepts are getting popular for ensuring image security as well as encryption as they show good performance. However, these DNA-based methods have some limitations, e.g., these are not dynamic and their performance results are far from ideal values. Further, these encryption methods usually involve two steps, confusion and diffusion. Confusion increases huge time complexity and needs to send one or more additional map tables with a cipher...
Fire incidence is one of the major disasters of human society. This paper proposes a still image-... more Fire incidence is one of the major disasters of human society. This paper proposes a still image-based fire detection system. It has many advantages like lower cost, faster response, and large coverage. The existing methods are not able to detect fire region adequately. The proposed method overcome and addresses the issue. A binary contour image of flame that is capable of classifying fire or no fire in image for fire detection is proposed in this study. The color of fire area can range from red yellow to almost white. So, here it is challenges the detected area is actually fire or no fire. Our propose method consists of five parts. Firstly, the digital image is taken from dataset and the digital image is sampled and mapped as a grid of dots or picture elements. We convert image to separate RGB Color range Matrix. We define some rules to select yellow color range of the image later on converted the image to binary range. Finally, binary contour image of flame information that detect the fire. We have analyzed different types of fire images in different varieties and found accuracy 85-90%.
Scene classification is an important and elementary problem in image understanding. It deals with... more Scene classification is an important and elementary problem in image understanding. It deals with large number of scenes in order to discover the common structure shared by all the scenes in a class. It is used in medical science (X-Ray, ECG and Endoscopy etc), criminal detection, gender classification, skin classification, facial image classification, generating weather information from satellite image; identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. In this paper, at first we propose a feature extraction method named LHOG or Localized HOG. We consider that an image contains some important region which helps to find similarity with same class of images. We generate local information from an image via our proposed LHOG method. Then by combing all the local information we generate the global descriptor using Bag of Feature (BoF) method which is finally used to represent and classify an image accurately and efficiently. In classification purpose, we use Support Vector Machine (SVM) that analyze data and recognize patterns. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output. In our paper, we use six different classes of images.
Person detection and tracking in crowd is a challenging task. We detect the head region and based... more Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd. Individual object detection has been improved significantly in recent times but the crowd detection and tracking contains some challenges. Crowd analysis is a highly focused area for law enforcement, urban engineering and traffic management.There are a lot of incident occurred in crowd area during some fabulous event. In this research low resolution and verities of image orientation is a key factor as well as overlapping person images in crowd misguided the system. An enhanced system of interest point detection based on gradient orientation information as well as improved feature extraction HOG is used for identifying the human head or face from crowd. We have analyzed different types of images in different varieties and found accuracy 88-90%. In a number of applications, such as document analysis and some industrial machine vision tasks, binary images can be used as the input to algorithms that perform useful tasks. These algorithms can handle tasks ranging from very simple counting tasks to much more complex recognition, localization, and inspection tasks. Thus by studying binary image analysis before going on to gray-tone and color images, one can gain insight into the entire image analysis process.
Journal of Advanced Veterinary and Animal Research
Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify b... more Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics. Materials and Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g., Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium. A total of 200 dig¬ital microscopic cell images comprising 40 of each of the genera mentioned above were used in this study. Results: The developed technique could identify and distinguish microscopic images of Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium with the highest accuracy of 92.20% for Staphylococcus and the lowest of 77.40% for Salmonella. A...
DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, ex... more DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D $$k-mer$$ k - m e r count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for $$k-mer$$ k - m e r . We develop an efficient system for finding the po...
Scene classification is an important and elementary problem in image understanding. It deals with... more Scene classification is an important and elementary problem in image understanding. It deals with large number of scenes in order to discover the common structure shared by all the scenes in a class. It is used in medical science (X-Ray, ECG and Endoscopy etc), criminal detection, gender classification, skin classification, facial image classification, generating weather information from satellite image; identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. In this paper, at first we propose a feature extraction method named LHOG or Localized HOG. We consider that an image contains some important region which helps to find similarity with same class of images. We generate local information from an image via our proposed LHOG method. Then by combing all the local information we generate the global descriptor using Bag of Feature (BoF) method which is finally used to represent and classify an image accurately and effic...
Indonesian Journal of Electrical Engineering and Computer Science, 2016
Fire incidence is one of the major disasters of human society. This paper proposes a still image-... more Fire incidence is one of the major disasters of human society. This paper proposes a still image-based fire detection system. It has many advantages like lower cost, faster response, and large coverage. The existing methods are not able to detect fire region adequately. The proposed method overcome and addresses the issue. A binary contour image of flame that is capable of classifying fire or no fire in image for fire detection is proposed in this study. The color of fire area can range from red yellow to almost white. So, here it is challenges the detected area is actually fire or no fire. Our propose method consists of five parts. Firstly, the digital image is taken from dataset and the digital image is sampled and mapped as a grid of dots or picture elements. We convert image to separate RGB Color range Matrix. We define some rules to select yellow color range of the image later on converted the image to binary range. Finally, binary contour image of flame information that detect...
Agriculture is the backbone of Bangladesh’s economy and it is one of the largest employment secto... more Agriculture is the backbone of Bangladesh’s economy and it is one of the largest employment sectors. In Bangladesh, the population is increasing rapidly and at the same time, the total cultivable land is decreasing significantly. To ensure maximum crop production using the limited land resources, it is essential to identify and select the appropriate type of soil because different crops need different soil types. Currently, there are two types of methods available to determine the soil type, namely chemical and image analysis. Although the first one is accurate, it is expensive and time consuming. On the other hand, image based soil classification is much cheaper and faster but its accuracy level is low. In this study, we present a novel feature based algorithm that combines quartile histogram oriented gradients (Q-HOG), most frequent $$\varphi $$ φ -Pixels and a new feature selection method for classifying soil types. We have used four machine learning algorithms and evaluated the ...
The seed is an inevitable element for agricultural and industrial production. The non-destructive... more The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy vari...
Nowadays, data are the most valuable content in the world. In the age of big data, we are generat... more Nowadays, data are the most valuable content in the world. In the age of big data, we are generating quintillions of data daily in the form of text, image, video, etc. Among them, images are highly used in daily communications. Various types of images, e.g., medical images, military images, etc. are highly confidential. But, due to data vulnerabilities, transmitting such images in a secured way is a great challenge. For this reason, researchers proposed different image cryptography algorithms. Recently, biological deoxyribonucleic acid (DNA)-based concepts are getting popular for ensuring image security as well as encryption as they show good performance. However, these DNA-based methods have some limitations, e.g., these are not dynamic and their performance results are far from ideal values. Further, these encryption methods usually involve two steps, confusion and diffusion. Confusion increases huge time complexity and needs to send one or more additional map tables with a cipher...
Fire incidence is one of the major disasters of human society. This paper proposes a still image-... more Fire incidence is one of the major disasters of human society. This paper proposes a still image-based fire detection system. It has many advantages like lower cost, faster response, and large coverage. The existing methods are not able to detect fire region adequately. The proposed method overcome and addresses the issue. A binary contour image of flame that is capable of classifying fire or no fire in image for fire detection is proposed in this study. The color of fire area can range from red yellow to almost white. So, here it is challenges the detected area is actually fire or no fire. Our propose method consists of five parts. Firstly, the digital image is taken from dataset and the digital image is sampled and mapped as a grid of dots or picture elements. We convert image to separate RGB Color range Matrix. We define some rules to select yellow color range of the image later on converted the image to binary range. Finally, binary contour image of flame information that detect the fire. We have analyzed different types of fire images in different varieties and found accuracy 85-90%.
Scene classification is an important and elementary problem in image understanding. It deals with... more Scene classification is an important and elementary problem in image understanding. It deals with large number of scenes in order to discover the common structure shared by all the scenes in a class. It is used in medical science (X-Ray, ECG and Endoscopy etc), criminal detection, gender classification, skin classification, facial image classification, generating weather information from satellite image; identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. In this paper, at first we propose a feature extraction method named LHOG or Localized HOG. We consider that an image contains some important region which helps to find similarity with same class of images. We generate local information from an image via our proposed LHOG method. Then by combing all the local information we generate the global descriptor using Bag of Feature (BoF) method which is finally used to represent and classify an image accurately and efficiently. In classification purpose, we use Support Vector Machine (SVM) that analyze data and recognize patterns. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output. In our paper, we use six different classes of images.
Person detection and tracking in crowd is a challenging task. We detect the head region and based... more Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd. Individual object detection has been improved significantly in recent times but the crowd detection and tracking contains some challenges. Crowd analysis is a highly focused area for law enforcement, urban engineering and traffic management.There are a lot of incident occurred in crowd area during some fabulous event. In this research low resolution and verities of image orientation is a key factor as well as overlapping person images in crowd misguided the system. An enhanced system of interest point detection based on gradient orientation information as well as improved feature extraction HOG is used for identifying the human head or face from crowd. We have analyzed different types of images in different varieties and found accuracy 88-90%. In a number of applications, such as document analysis and some industrial machine vision tasks, binary images can be used as the input to algorithms that perform useful tasks. These algorithms can handle tasks ranging from very simple counting tasks to much more complex recognition, localization, and inspection tasks. Thus by studying binary image analysis before going on to gray-tone and color images, one can gain insight into the entire image analysis process.
Uploads
Papers by Machbah Uddin