The problem of Segmenting Gray scale still images has been addressed in this work and proposed new methods by generating random field for image segmentation and boundary detection for image classification. The present work describes image segmentation at multiple scales. The detected regions are homogeneous and surrounded by closed edge boundaries. Segmentations yield texture and boundary information. Boundary information requires much more effort than texture information. The proposed techniques rely on boundary, textured and non-textured information for image segmentation at multiple scales. The definition of a general purpose segmentation technique has been revealed as being a rather complicated task. This complication is owing to the huge amount of different kind of data that a segmentation technique may have to handle. Previous approaches to multistage segmentation represented an image at different scales using a scale space. However, structure is only represented implicitly in this representation, structures at coarser scales are inherently smoothed, and the problem of structure extraction on is unaddressed. This work argues that the issue of scale selection and structure detection can not be treated separately. A new concept of scale will be presented that represents images structures at different scales, and the image itself. This scale is integrated into a non-linear transform, which makes structure explicit in the transformed domain. Structures that are stable to changes in scale are identified as being perceptually relevant, the transform can be viewed as collecting spatially distributed evidence for edges and regions, and making it available at contour locations there by facilitating integrated detection of edge and regions without restrictive models of geometry or homogeneity. Markov random field theory has been widely applied to the challenging problem of image segmentation. Image segmentation is a task that classifies pixels of an image using different labels so that the image is partitioned into non-overlapping labeled regions. Extraction of regions or objects of interest is usually the first important step in almost every task of image processing and high level image analysis for better understanding. Although it is fundamental, image segmentation a is field in which researchers are facing challenges because most of the real objects have complex shapes, boundaries and true images are often corrupted by noise that cannot be ignored. To tackle the difficult problem of image segmentation, researchers have proposed a variety of methods. In this thesis three textured models have been studied and proposed new methods under these models.
. Also typical for LiDAR is that every surface point is registered only once, in contrast to optical image sequences where multiple information is gathered of the same object. Image sequences do not only deliver multiple acquisition but also a denser sampling of the surface. Generally, the focus is on optical image sequences to which also IR cameras belong [Stilla & Michaelsen, 2002; Hinz & Stilla, 2006; Kirchhof & Stilla, 2006]. They provide a high frequent image acquisition and additionally supplemental information concerning the activity state of the vehicles. Warm parts (engine, body, etc.) appear as bright areas in the image which makes it possible to distinguish between stationary and parked cars [Yao et al., 2009]. Unfortunately, IR cameras only have a small pixel matrix and thus a low resolution. Similarly, hyperspectral sensors also provide a low resolution but they are often used for vehicle extraction [Manolakis et al., 2003; Casasent & Chen, 2003; Li et al., 2009]. Hyperspectral information can be used to exclude areas of vegetation or to determine shadow areas before the extraction process [Shimoni et al., 2011].
The development of contrast agents (CA) has made it possible to use MRI for molecular imaging. CAs or imaging probes are exogenous substances that produce a bright signal, or a signal void in the MR image. They are usually injected into the body of the living organism and can be localized to particular tissues or areas within the body (James and Gambhir, 2012). The most commonly used MRI CAs are gadolinium (Gd) based and highlight vessels or regions of vascular permeability (Caravan et al., 1999). To provide meaningful biochemical information, contrast agents must be targeted toward specific biochemical events. For example, Gd-based CA can be fused to certain peptide sequences, antibodies, or targeting moieties (Park et al., 2008; Bort et al., 2014). The major advantage associated with MRI is the extremely high spatial and temporal resolution. This makes it possible to track biomarkers inside the body and identify their exact position. The major limitation associated with this imaging technique is its low sensitivity, meaning a high concentration of CA must accumulate in the tissue before detection is possible (James and Gambhir, 2012).
DOI: 10.4236/jcc.2017.510005 37 Journal of Computer and Communications in computer vision, it’s widely used in specific applications including page seg- mentation, address block location, license plate location, etc. Because there are so many possible sources of variation when extracting text from a shaded or textured background, from low-contrast or complex images, or from images having variations in font size, style, color, orientation and alignment. These var- iations make the problem of automatic text information extraction extremely difficult. Numerous of existing methods have been proposed to detect and rec- ognize text in scene imagery, which can be categorized into edge-based detec- tion, connected component based detection and texture based detection. The connected component based methods assume that the text pixels belonging to the same connected region share some common features such as color or gray intensity  ; texture based methods may be unsuitable for small fonts and poor contrast text ; edge based methods return a lot of false alarms and are not robust to complex background images. In Ref. , Wei Fan presented a nov- el text segmentation method which is independent of variations in text font style, size, intensity, and polarity, and of string orientation with separating the pixels of a document image into four categories: “dark text/lines”, “bright text/ lines”, “dark figure/graphics” and “white background”. But this method is only valid for text embedded in a simple background. Antani et al.  assumed that text and background in localized region had consistent gray levels that all cha- racters were either lighter than or darker than the background, so the detection rate may get reduced. Lowell L. Winger  used a fast thresholding scheme which could deal with the texture background images better. It was unable to deal with the low-contrast text images with both English and Chinese character, especially when a background of the text images is various, including building, objects, clouds, houses and so on. These methods only do well for the text images with high reso- lution, huge text or simple background, which not for the lowcontrast text im- ages, even text in small size or in different color, etc., for appearing a series of problems such as location bias, imperfect or fault, etc., and finally causing poor text recognition results.
Where c is a contrast factor determines the degree of the needed contrast. The value of c depends on the objective of the enhancement process,the user can select the value of c according to desired contrast that he needs.The pixel values are within a limited range (0-255) for an 8-bit image.The results usually needs to be clipped to the maximum and minimum allowable pixel values so that all highest components turn out to be 255 and lowest values to 0.After map window reaches the right side, it returns to the left side and moves down a step. The process is repeated until the sliding window reaches the right-bottom corner of the image.
 Tang, Jinshan, Xiaoming Liu, and Qingling Sun. "A direct image contrast enhancement algorithm in the wavelet domain for the screening in mammograms."Selected Topics in Signal Processing, IEEE Journal of 3, no. 1 (2009): 74-80.
Medical equipment perform processing of acquired images through filtering or removing background noise or artifacts, correction of in homogeneity, distortion of images, numerical evaluation of features, visualization of areas of interest etc. Image processing software and hardware applications can provide temporal and spatial analysis and help to automatically identify and analyze images in order to detect patterns or characteristics indicative of tumors. The image processing and analysis can be used in medical applications to determine, for example, “the diameter, volume and vasculature of a tumor or organ, flow parameters of blood or other fluids and microscopic changes” .
The fact that the subwavelength image appears in the detected intensity profile, despite its absence in the field localization, is consistent with the idea that perfect imaging is not sensitive to the detection efficiency but rather to the resolution of the detectors (and the structure size of the material). If the detection is not perfect we can imagine the field as a mixture of an undetected field and the field that has undergone detection. The undetected field does not localize with subwavelength resolution and so does not form sharp spikes, but only the detected field does. In our case we could not observe this feature with the fairly crude implementation of a modified fish-eye mirror by a circuit-board metamaterial. However, in the detected intensity the undetected field is missing—by definition—and so the image becomes sharper. This aspect appears to have made it possible for us to resolve the two sources that were 0 . 2 λ apart, despite the limitations of our device. As the detection of an image is the very point of imaging, we can claim to have observed subwavelength resolution.
binocular disparity over time (CD), and low-resolution interocular velocity differences (IOVD). Computational differences between these two mechanisms suggest that they may be implemented in visual pathways with different spatial and temporal resolutions. Here, we used fMRI to examine how achromatic and S-cone signals contribute to human MID perception. Both CD and IOVD stimuli evoked responses in a widespread network that included early visual areas, parts of the dorsal and ventral streams, and motion- selective area hMT + . Crucially, however, we measured an interac- tion between MID type and chromaticity. fMRI CD responses were largely driven by achromatic stimuli, but IOVD responses were better driven by isoluminant S-cone inputs. In our psychophysical experiments, when S-cone and achromatic stimuli were matched for perceived contrast, participants were equally sensitive to the MID in achromatic and S-cone IOVD stimuli. In comparison, they were relatively insensitive to S-cone CD. These findings provide evidence that MID mechanisms asymmetrically draw on informa- tion in precortical pathways. An early opponent motion signal optimally conveyed by the S-cone pathway may provide a sub- stantial contribution to the IOVD mechanism.
reemergent a few years before blood collection, with about 1% of the cap- tured snails eliminating S. mansoni cercariae and 88.8% of the examined people (21 years old on average) excreting eggs in the feces (5). (iv) San Sebastia ´n de los Reyes (Aragua State), a rural site which was endemic for schistosomiasis in the past, with no vector snails and without transmission for the last 30 years; all the people examined (23 years old on average) had no eggs in the feces and showed negative serology. Diagnosis for schistosomiasis was performed with four different tests per individual: Kato-Katz (KK) anal- ysis (12), the circumoval precipitating test (COPT) (16), enzyme-linked im- munosorbent assay (ELISA) (26) using standard soluble egg antigen (SEA), and APIA (17); all these techniques were previously used in field work reported by Alarco ´n de Noya et al. (4, 5) and Cesari et al. (10). For the aim of the present study, we selected 40 positive sera (by the above-mentioned tests) from the four different sites (see Table 1, below). We also selected and evaluated 36 sera from patients of the above endemic areas harboring other infectious agents (19 females and 17 males; mean age, 19.4 years; range, 2 to 56 years): Trichuris trichura, Ascaris lumbricoides, Strongyloides stercoraris, Enterobium vermicularis, Necator americanus, Ancylostoma sp., Hymenolepis nana, Entamoeba histolytica, Entamoeba coli, Iodoamoeba butschlii, En- dolimax nana, Giardia lamblia, and/or Blastocystis hominis. Cysticercosis sera were also evaluated.
Several limitations of our study are worth con- sideration. First, because we focused on the IQ, the diagnostic accuracy was not evaluated by the gold standard of invasive coronary angiog- raphy. Second, the sample size of each group in this study was relatively small. Further studies with larger populations are necessary to con- firm our findings, and further multi-centre stud- ies will need to be arranged to invite more par- ticipants in the innovation of CM protocols in CCTA. Third, we did not evaluate the lumen and the plaque specifically, which will be investigat- ed in follow-up studies. Fourth, we only designed four protocols to investigate the feasi- bility of ultra-low CM usage; a larger number of different protocols need to be tested to explore a more accurate lower bound of IDR under cer- tain tube voltage and scan conditions. In addi- tion, because of the limited technology, the inclusion criteria of this study were relatively confined. As the third Generation of DSCT comes into clinical application with improved techniques and fewer patient restrictions [6, 30], larger studies with more general patient cohorts are expected in the near future. This study demonstrates that the ultra-low CM protocols in prospective 70 kV high-pitch CCTA were feasible for all non-obese patients, and the SAFIRE algorithm played a critical role in maintaining the IQ. For normal BMI patients (BMI≤25 kg/m 2 ), the CM protocol with a high IC
Several types of pyramid decompositions are Laplacian pyramid, ratio-of-low-pass pyramid and gradient pyramid. After obtaining the resultant fused image the performance evaluation of the images was carried out with the help of peak signal to noise ratio and mean square error. Final fused image is used to extract tumor region after segmentation using artificial neural network. Skull stripping is performed on the segmented image. Boundary and area of the tumor region is calculated and plotted in fused image. The system utilizes wavelet based image fusion to discover a high excellence fused image with spatial and spectral information. Method also detect brain tumor automatically using Artificial Neural Network (ANN) and also determined the position and the area of the tumor. Thus the results from the image fusion using different wavelets are compared on the basis of the PSNR and MSE in detection of the tumor as compared to the original MR image and CT scan image.
We described a deep neural architecture Di-LSTM Contrast Network for metaphor detection, which we submitted for Metaphor Shared Task 2018 (Leong et al., 2018). We showed that our system achieves appreciable performance solely by using the contrast features, generated by our model us- ing pre-trained word embeddings. Additionally, our model gets a significant performance boost from the use of extra baseline features, and re- weighting of examples.
Sarcasm understanding may require infor- mation beyond the text itself, as in the case of ‘I absolutely love this restaurant!’ which may be sarcastic, depending on the contextual situation. We present the first quantitative evidence to show that histori- cal tweets by an author can provide addi- tional context for sarcasm detection. Our sarcasm detection approach uses two com- ponents: a contrast-based predictor (that identifies if there is a sentiment contrast within a target tweet), and a historical tweet-based predictor (that identifies if the sentiment expressed towards an entity in the target tweet agrees with sentiment ex- pressed by the author towards that entity in the past).
Background: Breast cancer is the most common cancer in women all over India and accounts for 25% to 31% of all cancers in women in Indian cities. No preventive method for breast cancer has yet been defined. Special attention and control can so far only be planned among the known hereditary cancers. Therefore, to improve survival rate early detection is needed. The application of MRI for diagnosis of breast lesions is increasing rapidly. MRI imaging technique that employs time signal intensity curve, obtained by performing MRI scan after injection of contrast agent has emanated as amicable tool for screening of breast cancer, owing to its high sensitivity for detection of abnormalities.
Image detail enhancement algorithms can easily improve the way they look with images. They enhance good facts while stay away from halo items and also gradient letting go items all over edges. The detail enhancement technique is really an extensively used image editing tool. Active detail enhancement algorithms provide edge-reserving decomposition algorithms. Any source image is initially decomposed in a base layer which usually is by means of homogeneous areas together with well-defined ends and a depth level which usually is made up of great information or even structure with the edge-preserving decomposition algorithm, after which a new detail-enhanced image is manufactured by amplifying the actual depth layer.
As a result of globalization & also increasing competition, it has become very important for any industry to develop solutions regarding the quality of products. Effective monitoring and control, better data predictions, quick response to query is necessary for effective Quality Control. For a long time the fabric defects inspection process is still carried out with human visual inspection. However, they cannot detect more than 60% of the overall defects for the fabric if it is moving at a faster rate and thus the process becomes insufficient and costly. Therefore, automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. Studies have been carried out in this area, where in different inspection systems for detection of defects and properties of fibers, yarns and fabrics have been looked upon. The purpose of this paper is to categorize and describe the same. In this paper an attempt has been made to present the survey on these different inspection systems for detection of defects and properties in various areas of textiles and its role in the overall quality control.
The objective of this project is to detect and track pedestrians for driver assistance systems. Tracking and detection at night time is also considered in this paper. Night time tracking and detection is not that easy due to poor lighting conditions. So certain algorithms have been used to detect and track like the adaptive contrastdetection method. Now for the time being we just have started the process in which we can detect objects by the algorithms mentioned in the report. We shall start the process of tracking that too at night time as soon as possible. The latest method of Adaptive Contrast Change Detection gave satisfactory results in sufficiently reducing the noise while detecting multiple objects. But in some cases it gives unwanted noise. Hence, we shall use correlation which basically gives the relation between to frames having significant contrast change. Use of correlation has significantly improved the output and gives better result even with multiple moving objects. The approach seems to have efficient practical applications in poorly-lighted conditions such as night-time visual surveillance systems.