Multithresholding of color and gray-level images through a neural network technique

Papamarkos, Nikos; Strouthopoulos, C.; Andreadis, Ioannis

doi:10.1016/s0262-8856(99)00015-3

Cited by 76 publications

(29 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although, there is this rich theoretical background for the linear local models of color constancy, it is quite common to see research procedures which are based on the old color space paradigm, even in 2006. Color is one of the most important tools for object discrimination by human observers, but it is overlooked in the past [26]. Discarding the intrinsic characteristics of color images (as vector geometries [27]), many researchers have assumed color images as parallel grayscale images (e.g., see [28][29][30][31]).…”

Section: Previous Work On Pca-based Color Processingmentioning

confidence: 99%

An efficient PCA-based color transfer method

Abadpour

Kasaei

2007

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

Color information of natural images can be considered as a highly correlated vector space. Many different color spaces have been proposed in the literature with different motivations toward modeling and analysis of this stochastic field. Recently, color transfer among different images has been under investigation. Color transferring consists of two major categories: colorizing grayscale images and recoloring colored images. The literature contains a few color transfer methods that rely on some standard color spaces. In this paper, taking advantages of the principal component analysis (PCA), we propose a unifying framework for both mentioned problems. The experimental results show the efficiency of the proposed method. The performance comparison of the proposed method is also given.

show abstract

Section: Previous Work On Pca-based Color Processingmentioning

confidence: 99%

An efficient PCA-based color transfer method

Abadpour

Kasaei

2007

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

show abstract

“…When there are just two regions for classification, one of these receives the label 0 and the other 1, and in this case the technique is called binarization. More than one threshold can be established in the same image; this technique is called multi-thresholding [30,31]. This technique subdivides the image in more than two regions, establishing the lower and the higher limits of each region of interest.…”

Section: Thresholding and Multi-thresholdingmentioning

confidence: 99%

3D segmentation and visualization of lung and its structures using CT images of the thorax

Filho¹,

Cortez²,

Albuquerque³

2013

JBiSE

View full text Add to dashboard Cite

Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.

show abstract

“…Thresholding, a form of image segmentation, separates movement and non-movement in this MTI. Matching refers to the process of attaching a physical shape to each moving target and tracking this shape through successive frames [6][7][8]. For example, an automobile appearing in ultra resolution imagery might be -segmented‖ into several areas according to a specific range of digital numbers: roof, trunk, hood, windshield, and accompanying cast shadow.…”

Section: Related Studiesmentioning

confidence: 99%

Estimating Speed and Direction of Small Dynamic Targets through Optical Satellite Imaging

2010

View full text Add to dashboard Cite

Moving Target Indicators (MTI) are systems used to distinguish movement from stationary scenes and sometimes to derive the spatial attributes of these objects. These systems are currently used in many sectors such as traffic studies, border surveillance, and military applications. The proposed MTI reveals vehicles and their velocities using commercial imagery from a passive optical satellite-mounted sensor. With simple process of image differencing, the MTI can automatically recognize conveyances in motion (speed and direction) represented by polygons formed by a group of pixels from successive images. Micro-change detection with an existing commercial satellite requires special considerations of differences in spatial and spectral resolution between images. Complications involving the movement detection system such as vehicle overlap, vehicle clusters, and zones of low confidence are refined by adding error-reducing modules. This process is tested on a variety of vehicles, their concentrations, and environments, confirming the feasibility of utilizing an MTI with commercial optical satellite imagery for movement recognition and velocity estimation.

show abstract

Multithresholding of color and gray-level images through a neural network technique

Cited by 76 publications

References 14 publications

An efficient PCA-based color transfer method

An efficient PCA-based color transfer method

3D segmentation and visualization of lung and its structures using CT images of the thorax

Estimating Speed and Direction of Small Dynamic Targets through Optical Satellite Imaging

Contact Info

Product

Resources

About