The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Giorgio Baccarani - One of the best experts on this subject based on the ideXlab platform.
-
Random access analog memory for Early Vision
IEEE Journal of Solid-State Circuits, 1992Co-Authors: Eleonora Franchi, Roberto Guerrieri, Marco Tartagni, Giorgio BaccaraniAbstract:An analog frame buffer suitable for Early-Vision processing is described. It has been fabricated using digital 1.6- mu m CMOS technology. A novel architecture is presented to compensate for the effect of parameter mismatch. The chip stores an image for a time frame of 1/30 s with an equivalent precision of more than 6 b. The array size is 32*32 and the cell dimension is 30.8 mu m*40 mu m. Power consumption is below 30 mW and the chip requires a bias supply of 5 V. >
-
Random Access Analog Memory for Early Vision
Solid-State Circuits Conference 1991. ESSCIRC '91. Proceedings - Seventeenth European, 1991Co-Authors: Eleonora Franchi, Roberto Guerrieri, Marco Tartagni, Giorgio BaccaraniAbstract:Early-Vision problems require huge amounts of computer power. The utilization of analog circuits to cope with this problem has been hampered by the lack of an efficient way to store images for a discrete amount of time. In this communication, we investigate the use of an analog memory as a full-frame buffer. Two different momories have been built using a standard CMOS-ASIC process. Measurements carried out on these circuits and an analysis of their area occupation show the advantage of using analog storage for full-frame buffers.
Yu-chen Lin - One of the best experts on this subject based on the ideXlab platform.
-
An Early Vision-based snake model for ultrasound image segmentation.
Ultrasound in medicine & biology, 2000Co-Authors: Chung-ming Chen, Yu-chen LinAbstract:Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the Early-Vision model and the discrete-snake model. By simulating human Early Vision, the Early-Vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model.
-
new ultrasound image segmentation algorithm based on an Early Vision model and discrete snake model
Medical Imaging 1998: Image Processing, 1998Co-Authors: Chung-ming Chen, Yu-chen LinAbstract:Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the Early Vision model and the discrete snake model. By simulating human Early Vision, the Early Vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.
-
Medical Imaging: Image Processing - New ultrasound image-segmentation algorithm based on an Early Vision model and discrete snake model
Medical Imaging 1998: Image Processing, 1998Co-Authors: Chung-ming Chen, Yu-chen LinAbstract:Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the Early Vision model and the discrete snake model. By simulating human Early Vision, the Early Vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.
Eleonora Franchi - One of the best experts on this subject based on the ideXlab platform.
-
Random access analog memory for Early Vision
IEEE Journal of Solid-State Circuits, 1992Co-Authors: Eleonora Franchi, Roberto Guerrieri, Marco Tartagni, Giorgio BaccaraniAbstract:An analog frame buffer suitable for Early-Vision processing is described. It has been fabricated using digital 1.6- mu m CMOS technology. A novel architecture is presented to compensate for the effect of parameter mismatch. The chip stores an image for a time frame of 1/30 s with an equivalent precision of more than 6 b. The array size is 32*32 and the cell dimension is 30.8 mu m*40 mu m. Power consumption is below 30 mW and the chip requires a bias supply of 5 V. >
-
Random Access Analog Memory for Early Vision
Solid-State Circuits Conference 1991. ESSCIRC '91. Proceedings - Seventeenth European, 1991Co-Authors: Eleonora Franchi, Roberto Guerrieri, Marco Tartagni, Giorgio BaccaraniAbstract:Early-Vision problems require huge amounts of computer power. The utilization of analog circuits to cope with this problem has been hampered by the lack of an efficient way to store images for a discrete amount of time. In this communication, we investigate the use of an analog memory as a full-frame buffer. Two different momories have been built using a standard CMOS-ASIC process. Measurements carried out on these circuits and an analysis of their area occupation show the advantage of using analog storage for full-frame buffers.
Chung-ming Chen - One of the best experts on this subject based on the ideXlab platform.
-
An Early Vision-based snake model for ultrasound image segmentation.
Ultrasound in medicine & biology, 2000Co-Authors: Chung-ming Chen, Yu-chen LinAbstract:Due to the speckles and the ill-defined edges of the object of interest, the classic image-segmentation techniques are usually ineffective in segmenting ultrasound (US) images. In this paper, we present a new algorithm for segmenting general US images that is composed of two major techniques; namely, the Early-Vision model and the discrete-snake model. By simulating human Early Vision, the Early-Vision model can capture both grey-scale and textural edges while the speckle noise is suppressed. By performing deformation only on the peaks of the distance map, the discrete-snake model promises better noise immunity and more accurate convergence. Moreover, the constraint for most conventional snake models that the initial contour needs to be located very close to the actual boundary has been relaxed substantially. The performance of the proposed snake model has been shown to be comparable to manual delineation and superior to that of the gradient vector flow (GVF) snake model.
-
new ultrasound image segmentation algorithm based on an Early Vision model and discrete snake model
Medical Imaging 1998: Image Processing, 1998Co-Authors: Chung-ming Chen, Yu-chen LinAbstract:Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the Early Vision model and the discrete snake model. By simulating human Early Vision, the Early Vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.
-
Medical Imaging: Image Processing - New ultrasound image-segmentation algorithm based on an Early Vision model and discrete snake model
Medical Imaging 1998: Image Processing, 1998Co-Authors: Chung-ming Chen, Yu-chen LinAbstract:Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the Early Vision model and the discrete snake model. By simulating human Early Vision, the Early Vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.
-
A computational Early Vision model for segmentation of clinical ultrasound images
Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging , 1Co-Authors: Chan-ben Lin, Chung-ming ChenAbstract:Image segmentation is a fundamental step for quantitative ultrasound image analysis. However, due to the intrinsic noisy nature of an ultrasound image, classic segmentation techniques are usually ineffective in performing ultrasound image segmentation. Here, the authors present a computational Early Vision model for segmentation of clinical ultrasound images. Their approach is based on computing perceptual similarity among local blocks of images, which has been shown to be promising by experimental results on real ultrasound images.
James A. Schirillo - One of the best experts on this subject based on the ideXlab platform.
-
Color memory penetrates Early Vision
Behavioral and Brain Sciences, 1999Co-Authors: James A. SchirilloAbstract:Pylyshyn's concentration on form perception to demonstrate that Early Vision is cognitively impenetrable neglects that color perception is also part of Early Vision. Thus, the finding of Duncker (1939), Bruner et al. (1951), and Delk and Fillenbaum (1965) that the expected color of objects affects how they are perceived challenges Pylyshyn's thesis.
