International Science Index
A Structural Support Vector Machine Approach for Biometric Recognition
Face is a non-intrusive strong biometrics for
identification of original and dummy facial by different artificial
means. Face recognition is extremely important in the contexts of
computer vision, psychology, surveillance, pattern recognition,
neural network, content based video processing. The availability of a
widespread face database is crucial to test the performance of these
face recognition algorithms. The openly available face databases
include face images with a wide range of poses, illumination, gestures
and face occlusions but there is no dummy face database accessible in
public domain. This paper presents a face detection algorithm based on
the image segmentation in terms of distance from a fixed point and
template matching methods. This proposed work is having the most
appropriate number of nodal points resulting in most appropriate
outcomes in terms of face recognition and detection. The time taken to
identify and extract distinctive facial features is improved in the range
of 90 to 110 sec. with the increment of efficiency by 3%.
A Comparative Study of Medical Image Segmentation Methods for Tumor Detection
Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.
Computer Countenanced Diagnosis of Skin Nodule Detection and Histogram Augmentation: Extracting System for Skin Cancer
Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.
Embedded Semantic Segmentation Network Optimized for Matrix Multiplication Accelerator
Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.
A Character Detection Method for Ancient Yi Books Based on Connected Components and Regressive Character Segmentation
Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.
Analysis of Image Segmentation Techniques for Diagnosis of Dental Caries in X-ray Images
Early diagnosis of dental caries is essential for maintaining dental health. In this paper, method for diagnosis of dental caries is proposed using Laplacian filter, adaptive thresholding, texture analysis and Support Vector Machine (SVM) classifier. Analysis of the proposed method is compared with Otsu thresholding, watershed segmentation and active contouring method. Adaptive thresholding has comparatively better performance with 96.9% accuracy and 96.1% precision. The results are validated using statistical method, two-way ANOVA, at significant level of 5%, that shows the interaction of proposed method on performance parameter measures are significant. Hence the proposed technique could be used for detection of dental caries in automated computer assisted diagnosis system.
A Review on Image Segmentation Techniques and Performance Measures
Image segmentation is a method to extract regions of interest from an image. It remains a fundamental problem in computer vision. The increasing diversity and the complexity of segmentation algorithms have led us firstly, to make a review and classify segmentation techniques, secondly to identify the most used measures of segmentation performance and thirdly, discuss deeply on segmentation philosophy in order to help the choice of adequate segmentation techniques for some applications. To justify the relevance of our analysis, recent algorithms of segmentation are presented through the proposed classification.
Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition
In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.
Graph Cuts Segmentation Approach Using a Patch-Based Similarity Measure Applied for Interactive CT Lung Image Segmentation
Lung CT image segmentation is a prerequisite in lung
CT image analysis. Most of the conventional methods need a
post-processing to deal with the abnormal lung CT scans such as
lung nodules or other lesions. The simplest similarity measure in
the standard Graph Cuts Algorithm consists of directly comparing
the pixel values of the two neighboring regions, which is not
accurate because this kind of metrics is extremely sensitive to minor
transformations such as noise or other artifacts problems. In this work,
we propose an improved version of the standard graph cuts algorithm
based on the Patch-Based similarity metric. The boundary penalty
term in the graph cut algorithm is defined Based on Patch-Based
similarity measurement instead of the simple intensity measurement
in the standard method. The weights between each pixel and its
neighboring pixels are Based on the obtained new term. The graph
is then created using theses weights between its nodes. Finally,
the segmentation is completed with the minimum cut/Max-Flow
algorithm. Experimental results show that the proposed method is
very accurate and efficient, and can directly provide explicit lung
regions without any post-processing operations compared to the
An Improved C-Means Model for MRI Segmentation
Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.
Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering
In recent years, object detection has gained much
attention and very encouraging research area in the field of computer
vision. The robust object boundaries detection in an image is
demanded in numerous applications of human computer interaction
and automated surveillance systems. Many methods and approaches
have been developed for automatic object detection in various fields,
such as automotive, quality control management and environmental
services. Inappropriately, to the best of our knowledge, object
detection under illumination with shadow consideration has not
been well solved yet. Furthermore, this problem is also one of
the major hurdles to keeping an object detection method from the
practical applications. This paper presents an approach to automatic
object detection in images under non-standardized environmental
conditions. A key challenge is how to detect the object, particularly
under uneven illumination conditions. Image capturing conditions
the algorithms need to consider a variety of possible environmental
factors as the colour information, lightening and shadows varies
from image to image. Existing methods mostly failed to produce the
appropriate result due to variation in colour information, lightening
effects, threshold specifications, histogram dependencies and colour
ranges. To overcome these limitations we propose an object detection
algorithm, with pre-processing methods, to reduce the interference
caused by shadow and illumination effects without fixed parameters.
We use the Y CrCb colour model without any specific colour
ranges and predefined threshold values. The segmented object regions
are further classified using morphological operations (Erosion and
Dilation) and contours. Proposed approach applied on a large image
data set acquired under various environmental conditions for wood
stack detection. Experiments show the promising result of the
proposed approach in comparison with existing methods.
An Image Segmentation Algorithm for Gradient Target Based on Mean-Shift and Dictionary Learning
In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.
Image Segmentation and Contour Recognition Based on Mathematical Morphology
In image segmentation contour detection is one of the important pre-processing steps in recent days. Contours characterize boundaries and contour detection is one of the most difficult tasks in image processing. Hence it is a problem of fundamental importance in image processing. Contour detection of an image decreases the volume of data considerably and useless information is removed, but the structural properties of the image remain same. In this research, a robust and effective contour detection technique has been proposed using mathematical morphology. Three different contour detection results are obtained by using morphological dilation and erosion. The comparative analyses of three different results also have been done.
A Neural Approach for Color-Textured Images Segmentation
In this paper, we present a neural approach for
unsupervised natural color-texture image segmentation, which is
based on both Kohonen maps and mathematical morphology, using
a combination of the texture and the image color information of the
image, namely, the fractal features based on fractal dimension are
selected to present the information texture, and the color features
presented in RGB color space. These features are then used to train
the network Kohonen, which will be represented by the underlying
probability density function, the segmentation of this map is made
by morphological watershed transformation. The performance of our
color-texture segmentation approach is compared first, to color-based
methods or texture-based methods only, and then to k-means method.
Manipulation of Image Segmentation Using Cleverness Artificial Bee Colony Approach
Image segmentation is the concept of splitting the images into several images. Image Segmentation algorithm is used to manipulate the process of image segmentation. The advantage of ABC is that it conducts every worldwide exploration and inhabitant exploration for iteration. Particle Swarm Optimization (PSO) and Evolutionary Particle Swarm Optimization (EPSO) encompass a number of search problems. Cleverness Artificial Bee Colony algorithm has been imposed to increase the performance of a neighborhood search. The simulation results clearly show that the presented ABC methods outperform the existing methods. The result shows that the algorithms can be used to implement the manipulator for grasping of colored objects. The efficiency of the presented method is improved a lot by comparing to other methods.
A General Framework for Knowledge Discovery Using High Performance Machine Learning Algorithms
The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Image Segmentation Using 2-D Histogram in RGB Color Space in Digital Libraries
This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast.
Medical Image Edge Detection Based on Neuro-Fuzzy Approach
Edge detection is one of the most important tasks in image processing. Medical image edge detection plays an important role in segmentation and object recognition of the human organs. It refers to the process of identifying and locating sharp discontinuities in medical images. In this paper, a neuro-fuzzy based approach is introduced to detect the edges for noisy medical images. This approach uses desired number of neuro-fuzzy subdetectors with a postprocessor for detecting the edges of medical images. The internal parameters of the approach are optimized by training pattern using artificial images. The performance of the approach is evaluated on different medical images and compared with popular edge detection algorithm. From the experimental results, it is clear that this approach has better performance than those of other competing edge detection algorithms for noisy medical images.
A Comparative Study of Image Segmentation Algorithms
In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain visual
characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Medical Image Segmentation and Detection of MR Images Based on Spatial Multiple-Kernel Fuzzy C-Means Algorithm
In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.
Maximum Entropy Based Image Segmentation of Human Skin Lesion
Image segmentation plays an important role in
medical imaging applications. Therefore, accurate methods are
needed for the successful segmentation of medical images for
diagnosis and detection of various diseases. In this paper, we have
used maximum entropy to achieve image segmentation. Maximum
entropy has been calculated using Shannon, Renyi and Tsallis
entropies. This work has novelty based on the detection of skin lesion
caused by the bite of a parasite called Sand Fly causing the disease is
called Cutaneous Leishmaniasis.
Review of the Software Used for 3D Volumetric Reconstruction of the Liver
In medical imaging, segmentation of different areas of
human body like bones, organs, tissues, etc. is an important issue.
Image segmentation allows isolating the object of interest for further
processing that can lead for example to 3D model reconstruction of
whole organs. Difficulty of this procedure varies from trivial for
bones to quite difficult for organs like liver. The liver is being
considered as one of the most difficult human body organ to segment.
It is mainly for its complexity, shape versatility and proximity of
other organs and tissues. Due to this facts usually substantial user
effort has to be applied to obtain satisfactory results of the image
segmentation. Process of image segmentation then deteriorates from
automatic or semi-automatic to fairly manual one. In this paper,
overview of selected available software applications that can handle
semi-automatic image segmentation with further 3D volume
reconstruction of human liver is presented. The applications are being
evaluated based on the segmentation results of several consecutive
DICOM images covering the abdominal area of the human body.
Retrieving Similar Segmented Objects Using Motion Descriptors
The fuzzy composition of objects depicted in images
acquired through MR imaging or the use of bio-scanners has often
been a point of controversy for field experts attempting to effectively
delineate between the visualized objects. Modern approaches in
medical image segmentation tend to consider fuzziness as a
characteristic and inherent feature of the depicted object, instead of
an undesirable trait. In this paper, a novel technique for efficient
image retrieval in the context of images in which segmented objects
are either crisp or fuzzily bounded is presented. Moreover, the
proposed method is applied in the case of multiple, even conflicting,
segmentations from field experts. Experimental results demonstrate
the efficiency of the suggested method in retrieving similar objects
from the aforementioned categories while taking into account the
fuzzy nature of the depicted data.
Unsupervised Segmentation Technique for Acute Leukemia Cells Using Clustering Algorithms
Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Improved Skin Detection Using Colour Space and Texture
Skin detection is an important task for computer
vision systems. A good method of skin detection means a good and
successful result of the system.
The colour is a good descriptor for image segmentation and
classification; it allows detecting skin colour in the images. The
lighting changes and the objects that have a colour similar than skin
colour make the operation of skin detection difficult.
In this paper, we proposed a method using the YCbCr colour space
for skin detection and lighting effects elimination, then we use the
information of texture to eliminate the false regions detected by the
YCbCr skin model.
Evolutionary Program Based Approach for Manipulator Grasping Color Objects
Image segmentation and color identification is an
important process used in various emerging fields like intelligent
robotics. A method is proposed for the manipulator to grasp and place
the color object into correct location. The existing methods such as
PSO, has problems like accelerating the convergence speed and
converging to a local minimum leading to sub optimal performance.
To improve the performance, we are using watershed algorithm and
for color identification, we are using EPSO. EPSO method is used to
reduce the probability of being stuck in the local minimum. The
proposed method offers the particles a more powerful global
exploration capability. EPSO methods can determine the particles
stuck in the local minimum and can also enhance learning speed as
the particle movement will be faster.
The Use of Classifiers in Image Analysis of Oil Wells Profiling Process and the Automatic Identification of Events
Different strategies and tools are available at the oil
and gas industry for detecting and analyzing tension and possible
fractures in borehole walls. Most of these techniques are based on
manual observation of the captured borehole images. While this
strategy may be possible and convenient with small images and few
data, it may become difficult and suitable to errors when big
databases of images must be treated. While the patterns may differ
among the image area, depending on many characteristics (drilling
strategy, rock components, rock strength, etc.). In this work we
propose the inclusion of data-mining classification strategies in order
to create a knowledge database of the segmented curves. These
classifiers allow that, after some time using and manually pointing
parts of borehole images that correspond to tension regions and
breakout areas, the system will indicate and suggest automatically
new candidate regions, with higher accuracy. We suggest the use of
different classifiers methods, in order to achieve different knowledge
Segmentation of Noisy Digital Images with Stochastic Gradient Kernel
Image segmentation and edge detection is a fundamental section in image processing. In case of noisy images Edge Detection is very less effective if we use conventional Spatial Filters like Sobel, Prewitt, LOG, Laplacian etc. To overcome this problem we have proposed the use of Stochastic Gradient Mask instead of Spatial Filters for generating gradient images. The present study has shown that the resultant images obtained by applying Stochastic Gradient Masks appear to be much clearer and sharper as per Edge detection is considered.
Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey
Segmentation is one of the essential tasks in image
processing. Thresholding is one of the simplest techniques for
performing image segmentation. Multilevel thresholding is a simple
and effective technique. The primary objective of bi-level or
multilevel thresholding for image segmentation is to determine a best
thresholding value. To achieve multilevel thresholding various
techniques has been proposed. A study of some nature inspired
metaheuristic algorithms for multilevel thresholding for image
segmentation is conducted. Here, we study about Particle swarm
optimization (PSO) algorithm, artificial bee colony optimization
(ABC), Ant colony optimization (ACO) algorithm and Cuckoo
search (CS) algorithm.
Color Image Segmentation Using SVM Pixel Classification Image
The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.