We present a modified binary level set method for two-phase image segmentation, which is based on the binary level set method originally proposed by X.-C. Tai et al. [Int. J. Comput. Vis. 73, 61–76 (2007)]. The modified binary level set method is superior to Tai et al.'s binary level set method in preserving the curve evolution property of gradually shrinking and expanding the partition interface represented by a binary level set function in the segmentation process. Some experiments conducted on real images show the good results of the modified binary level set method.

Modern field programmable gate array (FPGA) chips, with their large memory capacity and reconfigurability potential, are opening new frontiers in rapid prototyping of embedded systems. With the advent of high-density FPGAs, it is now possible to implement a high-performance very long instruction word (VLIW) processor core in an FPGA. This paper describes research results about enabling the DSP TMS320 C6201 model for real-time image processing applications by exploiting FPGA technology. We present a modular DSP C6201 VHDL model with a variable instruction set. We call this new development a minimum mandatory modules (M³) approach. Our goals are to keep the flexibility of DSP in order to shorten the development cycle and to use the totality of the powerful FPGA resources in order to increase real-time performance. Some common algorithms of image processing and a face tracking in video sequences application were created and validated on an FPGA VirtexII-2000 multimedia board using the development cycle proposed. Our results demonstrate that an algorithm can easily be, in an optimal manner, specified and then automatically converted to VHDL language and implemented on an FPGA device with system-level software. This makes our approach suitable for developing co-design environments. Our approach applies some criteria for co-design tools: flexibility, modularity, performance, and reusability. In this paper, the target VLIW processor is the DSP TMS320C6x. Nonetheless, our design cycle can be generalized to other DSP processors.

Mathematical morphology is a very important image analysis area that uses set theory tools to study shapes. The basic operations in mathematical morphology are dilation and erosion, and these can be used to construct more complex operations. Low-level image processing often uses dedicated computing hardware for repetitive processing over large data structures. High-capacity programmable logic devices (HCPLDs) have increasingly been used for the fast development of real-time image processing systems. In this paper we present a pipeline architecture, using high-capacity programmable logic devices, for real-time mathematical morphology operations. The developed architecture can process (512×512) pixel binary images and has flexible stages that can be reprogrammed according to the shape and size of the structuring elements used in the morphological operations. Tests performed over the architecture demonstrated that it performs well when compared to similar architectures and that it is an efficient choice for dedicated morphological image processing operations.

A two-dimensional (2D) periodic array of gallium arsenide (GaAs) quantum dots, embedded on the surface of a GaAs resonant tunneling diode (RTD), is investigated as a system for image processing. The embedded dot structures have been developed as a high-speed parallel processing device. The image processing task by QDs can be done through the realization of QD Boolean logic gates. It is shown that if the image data are introduced as initial conditions to these QD logic gates, the response of the system realizes a morphological image processing task similar to image dilation and erosion.

Image segmentation is a key element in processing image data. Done properly, image segmentation can both enhance the quality of subsequent processing and enable other higher-level processing to generate useful information. Image segmentation that clearly separates objects from the background can improve the accuracy of the recognition of the object. Isolating important objects within an image requires techniques that divide pixels into object or background information.A technique adopted from the neural network field is presented for performing image segmentation based on the winner-take-all (WTA) scheme implemented with an optoelectronic architecture. This combination allows the parallel nature of optics and the computational strengths of electronics to model a fast and efficient image segmentation system. A model of an architecture for a large array of optoelectronic feedback circuits that can be realized using new technology to perform image segmentation using the WTA scheme is proposed. The architecture is modeled optoelectronically as an interferometer with a simple nonlinearity for the control unit. Results from numerical analysis and simulations show the model can generate WTA behavior. Results from simulations are shown with sample images used to test the model's ability to perform segmentation.

Large degree-of-freedom, real-time adaptive optics control requires reconstruction algorithms that are computationally efficient and readily parallelized for hardware implementation. Poyneer et al. [J. Opt. Soc. Am. A 19, 2100–2111 (2002)] have shown that the wavefront reconstruction with the use of the fast Fourier transform (FFT) and spatial filtering is computationally tractable and sufficiently accurate for its use in large Shack–Hartmann-based adaptive optics systems (up to 10,000 actuators). We show here that by the use of graphical processing units (GPUs), a specialized hardware capable of performing FFTs on big sequences almost 5 times faster than a high-end CPU, a problem of up to 50,000 actuators can already be done within a 6-ms limit. We give the method to adapt the FFT in an efficient way for the underlying architecture of GPUs.

This paper describes a method for single-exposure, contrast-enhanced dual-energy imaging of tumors utilizing a scanned multislit system for digital mammography. This photon-counting system employs an array of silicon strip detectors mounted in an edge-on geometry. The line detectors and pre- and post-collimator slits are carefully aligned, and the multislit setup allows differential filtering of the x-ray beam in the pre-collimator slits. A high-energy image is constructed from those lines where the filter material has been chosen to harden the x-ray beam and the low-energy image from the lines with a filter producing softer beams. Both images are obtained in the same scan, eliminating the need to change tube voltages and anode materials and minimizing the risk of motion artifacts. The method is illustrated on a purpose-built phantom, and logarithmic subtraction of the images produces images essentially free of anatomical clutter with the contrast-enhanced targets clearly visible.

Oriented feature detectors are fundamental tools in image understanding, as many images display information of interest in the form of oriented features. Several oriented feature detectors have been developed; some of the important families of oriented feature detectors are steerable filters and Gabor filters. In this work, design criteria and performance analysis are presented for the following oriented feature detectors: the Gaussian second-derivative steerable filter; the quadrature-pair Gaussian second-derivative steerable filter; the real Gabor filter; the complex Gabor filter; and a line operator that has been shown to outperform the Gaussian second-derivative steerable filter in the detection of curvilinear structures in mammograms. The detectors are assessed in terms of their capability to detect the presence of oriented features as well as their accuracy in the estimation of the angle of the oriented features present in test images. It is shown that the real Gabor filter yields the best detection performance and angular accuracy, whereas the line operator and the steerable filter provide an advantage in terms of computational speed.

This paper presents an efficient technique for linking edge points in order to generate a closed-contour representation. It is based on the consecutive use of global and local schemes. In both cases it is assumed that the original intensity image, as well as its corresponding edge map, are given as inputs to the algorithm. The global scheme computes an initial representation by connecting edge points minimizing a global measure based on spatial information (3D space). It relies on the use of graph theory and exploits the edge points' distribution through the given edge map, as well as their corresponding intensity values. At the same time spurious edge points are removed by a morphological filter. The local scheme finally generates closed contours, linking open boundaries, by using a local cost function that takes into account both spatial and topological information. Experimental results with different images, together with comparisons with a previous technique, are presented.

A new technique for approximating range images with adaptive triangular meshes ensuring a user-defined approximation error is presented. This technique is based on an efficient coarse-to-fine refinement algorithm that avoids iterative optimization stages. The algorithm first maps the pixels of the given range image to 3D points defined in a curvature space. Those points are then tetrahedralized with a 3D Delaunay algorithm. Finally, an iterative process starts digging up the convex hull of the obtained tetrahedralization, progressively removing the triangles that do not fulfill the specified approximation error. This error is assessed in the original 3D space. The introduction of the aforementioned curvature space makes it possible for both convex and nonconvex object surfaces to be approximated with adaptive triangular meshes, improving thus the behavior of previous coarse-to-fine sculpturing techniques. The proposed technique is evaluated on real range images and compared to two simplification techniques that also ensure a user-defined approximation error: a fine-to-coarse approximation algorithm based on iterative optimization (Jade) and an optimization-free, fine-to-coarse algorithm (Simplification Envelopes).

A texture-based method for classification of land cover and benthic habitats from hyperspectral and multispectral images is presented. The features considered in this work are a set of statistical and multiresolution texture features, including a 2-level wavelet transform that uses an orthonormal Daubechies filter. These features are computed over spatial extents from each band of the image. A stepwise sequential feature selection process is applied that results in the selection of optimal features from the original feature set. A supervised classification is performed with a distance metric. Results with AVIRIS hyperspectral and IKONOS multispectral images show that texture features perform well under different land cover scenarios and are effective in characterizing the texture information at different wavelengths. Results over coastal regions show that wavelet texture features computed over the reflectance spectrum can accurately detect the benthic classes.

Forward error correction (FEC) improves the quality of compressed video transmitted through a lossy network for real-time applications such as video streaming. The FEC techniques are generally applied on source video packets at the frame level. In this paper, we propose a technique where the FEC is applied on source packets at the group-of-pictures (GoP) level assuming an MPEG-like compression scheme. We derive analytically an estimate of the average playable frame rate for a given packet loss probability. Our analysis over a range of network conditions indicates that in most practical network conditions, the proposed technique provides a larger playable frame rate compared to the frame-level FEC technique. This analysis results are also validated by video streaming simulations conducted on the NS-2 network simulator.

The Gaussian mixture model (GMM) is an important metric for moving objects segmentation and is fit to deal with the gradual changes of illumination and the repetitive motions of scene elements. However, the performance of the GMM may be plagued by the complex motion of the dynamic background such as waving trees and flags fluttering. A spatiotemporal Gaussian mixture model (STGMM) is proposed to handle the complex motion of the background by considering every background pixel to be fluctuating both in intensity and in its neighboring region. A new matching rule is defined to incorporate the spatial information. Experimental results on typical scenes show that STGMM can segment the moving objects correctly in complex scenes. Quantitative evaluations demonstrate that the proposed STGMM performs better than GMM.

This PDF file contains the editorial “Digital Image Processing, 4th Edition” for JEI Vol. 16 Issue 02