New algorithms to Enhanced Fused Images from Auto-Focus Images

هذا البحث يقترح طريقة جديدة لدمج صورة ذات التركيز التلقائي بالاعتماد على خوارزميات جديدة. الخوارزمية الأولى تعتمد على حساب الانحراف المعياري لدمج صورتين. الخوارزمية الثانية تتركز على التباين عند نقاط الحافات وطريقة الترابط كعامل معيار لجودة الصورة الناتجة. هذه الخوارزمية تعتمد على ثلاثة مربعات بأحجام مختلفة عند المناطق المتجانسة وتتحرك 10 نقاط ضمن المنطقة المتجانسة.  الصورة الناتجة من الدمج تحتوي على نتائج جيدة في التباين بسبب إضافة نقاط حافات من الصورتين والتي تعتمد على الخوارزميات المقترحة. تم مقارنة النتائج مع طرق مختلفة.Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contrast value because of the added edge points from the two combined images that depend on the suggested algorithms. This enhancement in edge regions is measured and reaches to double in enhancing the contrast. Different methods are used to be compared with the suggested method

Design and Implementation of Fuzzy Logic Based Image Fusion Technique

The quality of image holds importance for both humans and machines. To fulfill the requirement of good quality images, image enhancement is needed. Application of a single contrast enhancement technique often does not produce desirable result and may lead to over enhanced images. To overcome this problem image fusion is performed so that better results with desired enhancement can be achieved. In the present paper an amalgamation of image enhancement, fusion and sharpening have been carried out in the candidate algorithm. The algorithm makes use of fuzzy logic for weight calculation. The results are compared with DACE/LIF approach and it is observed that the proposed algorithm improves the result in terms of quality parameters like PSNR (Peak Signal to Noise Ratio), AMBE (Absolute Mean Brightness Error) and SSIM (Structural Similarity Index) by 0.5 dB, 3 and 0.1 respectively from the existing technique

Image segmentation methods and edge detection: An application to knee joint articular cartilage edge detection

Image segmentation is the process of partitioning a digital image into multiple segments and regions for further processing. Edge detection methods are widely used in the area of image processing for feature detection and extraction. In this paper we use human’s Knee MRI (Magnetic resonance imaging) images of patients and applied various image segmentation and edge detection methods for knee cartilage visualization. Also this paper focuses on providing an overview of important concepts, methods and algorithms commonly used for image segmentation and edge detection with focus on knee joint articular cartilage image segmentation and visualizatio

Image Processing-Based Lung Cancer Detection Using Adaptive CNN Mixed Sine Cosine Crow Search Algorithm in Medical Applications

Medical image processing relies heavily on the diagnosis of lung cancer images. It aids doctors in determining the correct diagnosis and management. For many patients, lung cancer ranks among the most deadly diseases. Many lives can be saved if cancerous growth is diagnosed early. Computed Tomography (CT) is a critical diagnostic technique for lung cancer. There was also an issue with finding lung cancer due to the time constraints in using the various diagnostic methods. In this study, an Adaptive CNN Mixed Sine Cosine Crow Search (ACNN-SCCS) strategy is proposed to assess the presence of lung cancer in CT images based on the imaging technique. Accordingly, the presented classification scheme is used to assess these traits and determine whether or not the samples include cancerous cells. To obtain the highest level of accuracy for our research the proposed technique is analyzed and compared to many other approaches, and its performance metrics (detection accuracy, precision, f1-score, recall, and root-mean-squared error) are examined

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

Texture based Image Splicing Forgery Recognition using a Passive Approach

With the growing usage of the internet in daily life along with the usage of dominant picture editing software tools in creating forged pictures effortlessly, make us lose trust in the authenticity of the images. For more than a decade, extensive research is going on in the Image forensic area that aims at restoring trustworthiness in images by bringing various tampering detection techniques. In the proposed method, identification of image splicing technique is introduced which depends on the picture texture analysis which characterizes the picture areas by the content of the texture. In this method, an image is characterized by the regions of their texture content. The experimental outcomes describe that the proposed method is effective to identify spliced picture forgery with an accuracy of 79.5%

Real Time Pattern Recognition using Matrox Imaging System

A primary goal of pattern recognition is to be able to classify data into a set of related elements. Many applications today take advantage of pattern recognition; among them are data mining, face recognition, web searching, robotics and a lot more. Pattern recognition concerning Artificial Intelligence has been in research and development for approximately 50 years. The ability to quickly locate one or more instances of a model in a grey scale image is of importance to industry. The recognition/ localization must be fast and accurate. Two algorithms are employed in this work to achieve fast recognition and localization. Both the algorithm relies on a pyramid representation of both the model image and the search image. Specifically these algorithms are suitable for implementation on a personal computer equipped with an image acquisition board and a camera. Finally, results are given for searches on real images with perspective distortion and the addition of Gaussian noise