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As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this...
Analyzing morphological and structural changes of dendritic spines in 2-photon microscopy images in time is important for neuroscience researchers. Correct segmentation of dendritic spines is an important step of developing robust and reliable automatic tools for such analysis. In this paper, we propose an approach for segmentation of 3D dendritic spines using nonparametric shape priors. The proposed...
Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points...
Text recognition has revolutionized the world of image processing and intelligent transportation system (ITS). It opened several possibilities to traditional ITS concept. Advancement in text recognition has made it possible to implement text recognition in ITS. Traffic panel text recognition, a real time application is considered as a key addition to the revolution in modern ITS. This research aims...
Brain Tumor which is also known as Intracranial Neoplasm is a vital brain disease. This is caused when abnormal cells are formed within the brain. The two essential types of tumor are Malignant or Cancerous tumor and Brain tumor. The patient does not recover when the growth of the abnormal cells is more than the 50% mark of the brain. The describes two different algorithms of image processing. The...
Texture is an important feature in RS image classification of land-use, and its precision mainly depends on the scale parameters, which are strongly correlated with the geometry characteristics of the classified objects. However, there is no a recognized reliable method for texture scale extraction. So this paper proposes an new approach to indirectly extract them with the assistance of domain GIS...
Lung cancer is the most common cancer for death among all cancers and CT scan is the best modality for imaging lung cancer. A good amount of research work has been carried out in the past towards CAD system for lung cancer detection using CT images. It is divided into four stages. They are pre-processing or lung segmentation, nodule detection, nodule segmentation and classification. This paper presents...
Arabic script is cursive in both printed and handwritten forms. This intrinsic nature of cursiveness renders the segmentation task challenging. An Arabic word generally consists of multiple parts known as Parts of Arabic Words (PAWs) or simply sub-words. Sub-words share the same vertical space quite frequently which makes vertical projection segmentation technique inefficient. Several Arabic letters...
In this paper, we have implemented and tested a system of detection and recognition of road signs. The approach taken in this work consists of two main modules: a sensor module, which is based on color segmentation and shape detection where we converted the images to the HSV color space, then labeled the detected regions and tested for their shape. A recognition module, Template Matching, whose role...
Paddy is the most important crop in Asian country. Most of the people depend on rice for their food, so rice is considered as staple food in Asian country. Rice plant is affected by many diseases that affect the farmers in yield loss. In this paper, proposed a method for identification of Blast and Brown Spot diseases. Global threshold method has been applied and kNN classifier has been used to classify...
This proposed method will give a suggestion for Tuberculosis (TB) diagnosing using Artificial Neural Networks (ANN). Since diagnostic imaging techniques such as x-rays (Radiographs), Magnetic Resonance Imaging (MRI), Computed Tomography (CT) are available, X-ray techniques is widely preferred for edging the image of TB affected area in the chest region. This method is preferred due to its fastness...
India is one the Country with High dense population. Due this high population Transportation and Parking of the Vehicles is the major issue faced by the Peoples. This Paper is to Provide a Intelligent Parking System through Image Processing. In this Systematic Approach the Image Processing Technique can be used to Identify the free empty Parking area to Park our Vehicles. In the Proposed process the...
As Sensors are sensitive to weather conditions; video cameras could be used to record the traffic information at different weather conditions. We have sophisticated algorithms to analyze the traffic videos in real time and discover information of interest. Although some sensors could be more accurate, they could also be Intrusive and need a higher maintenance cost. We may need to embed weighing sensors...
The proposed work focused on detection of shapes of toy and its count in the particular area to segregate the items manufactured in the toy industry for packing. The identification of shapes using Ramer-Douglas-Peucker algorithm in Phython language is a technique implemented with the help of open CV image processing tool. The prototype of the model is developed by giving a sample input image with...
Face detection is one of the most researched topics in computer vision. During the past decades, several fast and accurate methods have been developed by using different computer vision and statistical tools. In fact, accuracy and applicability are two main factors which researchers try to improve. In this paper a method for face region detection using color segmentation and randomized Hough transform...
Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification...
We present a learning based fully automatic method to detect and segment the prostate in T2 weighted MR scans. It consists of a localization stage which uses a learned global context to detect the prostate location. This is followed by a segmentation stage which uses a learned local context using prostatic segment specific discriminative classifiers, to compute the probability of a point being on...
Endometrium assessment via thickness measurement is commonly performed in routine gynecological ultrasound examination for assessing the reproductive health of patients undergoing fertility related treatments and endometrium cancer screening in women with post-menopausal bleeding. This paper introduces a fully automated technique for endometrium thickness measurement from three-dimensional transvaginal...
Cervical nuclei carry substantial diagnostic information for cervical cancer. Therefore, in automation-assisted reading of cervical cytology, automated and accurate segmentation of nuclei is essential. This paper proposes a novel approach for segmentation of cervical nuclei that combines fully convolutional networks (FCN) and graph-based approach (FCNG). FCN is trained to learn the nucleus high-level...
We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images...
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