Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel SIMD architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file

Real time motorcycle image detection and analysis

This research investigated image tracking and edge detection for motorcycle in various lighting and weather conditions. The capability in different resolution and threshold level also evaluated. Comparison between hardware and software implementation of edge detection also been made. Develop framework showed great accuracy is segmentation of plate number from motorcycle image in daylight condition compared to rainy daylight and night condition. The aim of this project is to develop framework for motorcycle image detection and recognize for traffic offender. Analysis with histogram level and contrast stretching method showed performance in hardware is improved rather than software

Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

Early detection of subtle fracture is important particularly for the senior citizens’ quality of life. Naked eye examination from X-ray image may cause false negatives due to operator subjectivity thus computer vision based automatic detection software is much needed in practice.  In this paper, we propose an automatic extraction method for suspisious wrist fracture regions. We apply K-means in pixel clustering to form the candidate part of possible fracture from wrist X-ray image automatically. This method can recover previously detected patterned false cases with edge detection method after fuzzy stretching. The proposed method is successful in 16 out of 20 tested cases in experiment

Peningkatan Citra Untuk Memperjelas Foto Brain Ct Scan

The CT scan brain photo represent medical image result from Computer Tomographi (CT) Scanner process, normally obtained on photographic negative transparencies. The brain images were acquired from a CT scan photo presenting colour gradation in to gray level, that is : white colour, gray and black colour. In general have histogram which tend to reside in around dark in to gray level, so that its image less clear if seen with naked eye. and can be told exploiting of digital image enhancement not yet is optimal. The research was to show that digital image enhancement used to clarify photograph CT scan brain image. Data collecting done with procedure chosen normal diagnostic CT-scan brain photo and which have indication damage of brain effect of ischemic stroke. The CT scan brain photo so that can be processed must be done scanning beforehand for the image digitization is quantization. The image enhancement techniques can be processed is : histogram equalization transform, contrast stretching transform, histogram classify, region of interst to enhancement and edge detection. Histogram equalization (Histeq) transform, contrast stretching transform, histogram classify, region of interst to enhancement (ROI) and edge detection transform can be used for clarify CT scan brain photo, so that can assist radiology doctor in is diagnostic of disparity or damage brain effect of ischemic stroke.

Confocal Raman data analysis enables identifying apoptosis of MCF-7 cells caused by anticancer drug paclitaxel

Confocal Raman microscopy is a noninvasive, label-free imaging technique used to study apoptosis of live MCF-7 cells. The images are based on Raman spectra of cells components, and their apoptosis is monitored through diffusion of cytochrome c in cytoplasm. K-mean clustering is used to identify mitochondria in cells, and correlation analysis provides the cytochrome c distribution inside the cells. Our results demonstrate that incubation of cells for 3 h with 10 mu M of paclitaxel does not induce apoptosis in MCF-7 cells. On the contrary, incubation for 30 min at a higher concentration (100 mu M) of paclitaxel induces gradual release of the cytochrome c into the cytoplasm, indicating cell apoptosis via a caspase independent pathway. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JBO.18.5.056010

An Improved Approach for Contrast Enhancement of Spinal Cord Images based on Multiscale Retinex Algorithm

This paper presents a new approach for contrast enhancement of spinal cord medical images based on multirate scheme incorporated into multiscale retinex algorithm. The proposed work here uses HSV color space, since HSV color space separates color details from intensity. The enhancement of medical image is achieved by down sampling the original image into five versions, namely, tiny, small, medium, fine, and normal scale. This is due to the fact that the each versions of the image when independently enhanced and reconstructed results in enormous improvement in the visual quality. Further, the contrast stretching and MultiScale Retinex (MSR) techniques are exploited in order to enhance each of the scaled version of the image. Finally, the enhanced image is obtained by combining each of these scales in an efficient way to obtain the composite enhanced image. The efficiency of the proposed algorithm is validated by using a wavelet energy metric in the wavelet domain. Reconstructed image using proposed method highlights the details (edges and tissues), reduces image noise (Gaussian and Speckle) and improves the overall contrast. The proposed algorithm also enhances sharp edges of the tissue surrounding the spinal cord regions which is useful for diagnosis of spinal cord lesions. Elaborated experiments are conducted on several medical images and results presented show that the enhanced medical pictures are of good quality and is found to be better compared with other researcher methods.Comment: 13 pages, 6 figures, International Journal of Imaging and Robotics. arXiv admin note: text overlap with arXiv:1406.571